Multi-satellite mission scheduling method, device and equipment

Abstract

The present invention provides a multi-satellite task scheduling method that can be applied to the field of task planning technology to improve satellite resource utilization and task execution efficiency. The method includes: generating an initial satellite scheduling task for the target area to be observed based on whether the sub-areas in the target area to be observed are covered by the satellite strips of the satellite set; using meteorological factors to filter out satellite sub-tasks in the initial satellite scheduling task whose degree of influence by meteorological factors is greater than a predetermined degree threshold, thereby obtaining a meteorologically optimized satellite scheduling task; using multi-dimensional indicators to optimize and train the meteorologically optimized satellite scheduling task to obtain an indicator-optimized satellite scheduling task; generating a supplementary satellite scheduling task based on the intersection of the satellite strips of satellites in the satellite set that are not called by the indicator-optimized satellite scheduling task and the blank area; and scheduling the satellite set based on the indicator-optimized satellite scheduling task and the supplementary satellite scheduling task. The present invention also provides a multi-satellite task scheduling device and equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of mission planning, and in particular to a multi-satellite mission scheduling method, device and equipment. Background Art

[0002] Multi-satellite joint mission scheduling for target areas involves utilizing multiple satellites for coordinated observation to achieve coverage, response, and data acquisition over a wide area. This approach has long been a research hotspot in the aerospace field. Current multi-satellite scheduling tasks suffer from low satellite resource utilization and inefficient execution. Summary of the Invention

[0003] In view of the above problems, the present invention provides a multi-satellite task scheduling method, device and equipment for improving satellite resource utilization and task execution efficiency.

[0004] One aspect of the present invention provides a multi-satellite task scheduling method, the method comprising: generating an initial satellite scheduling task for a target area based on coverage of sub-areas in a target area to be observed by satellite strips of a satellite set, wherein the sub-areas are obtained by dividing the target area using polygons of a predetermined size, and the sub-areas in the target area not covered by the satellite strips are treated as blank areas, and the initial satellite scheduling task includes multiple satellite sub-tasks, which are used to complete the satellite observation of the sub-areas; using meteorological factors, filtering out satellite sub-tasks in the initial satellite scheduling task whose degree of influence by meteorological factors is greater than a predetermined degree threshold, to obtain a meteorologically optimized satellite scheduling task; using multi-dimensional indicators to optimize and train the meteorologically optimized satellite scheduling task until a function value constructed based on the multi-dimensional indicator value of the meteorologically optimized satellite scheduling task and the weight value of the multi-dimensional indicators meets a predetermined indicator balance condition, to obtain an indicator-optimized satellite scheduling task; generating a supplementary satellite scheduling task based on the intersection of the satellite strips in the satellite set that are not called by the indicator-optimized satellite scheduling task and the blank area; and scheduling the satellite set based on the indicator-optimized satellite scheduling task and the supplementary satellite scheduling task.

[0005] According to an embodiment of the present invention, the degree value affected by meteorological factors includes cloud coverage; the predetermined degree threshold includes a predetermined cloud coverage threshold; the meteorological factors are used to filter out satellite subtasks in the initial satellite scheduling task whose degree value affected by meteorological factors is greater than the predetermined degree threshold, to obtain a meteorologically optimized satellite scheduling task, including: obtaining cloud data of the target area through an integrated meteorological interface service, and generating cloud coverage of the sub-area in the target area based on the satellite's transit time and the position of the sub-area; in the initial satellite scheduling task, the satellite subtasks whose cloud coverage exceeds the predetermined cloud coverage threshold are filtered out to obtain a meteorologically optimized satellite scheduling task.

[0006] According to an embodiment of the present invention, in the initial satellite scheduling task, satellite subtasks whose cloud coverage exceeds a predetermined cloud coverage threshold are filtered out to obtain a meteorological optimization satellite scheduling task, including: screening out sub-areas whose cloud coverage exceeds a predetermined cloud coverage threshold as sub-areas to be allocated; filtering out satellite subtasks used to observe sub-areas to be allocated in the initial satellite scheduling task, and scheduling a backup observation system for the sub-areas to be allocated; and adjusting the weight value of the target area coverage of the initial satellite scheduling task and the weight value of the cloud cover impact until a predetermined meteorological balance condition is reached between the target area coverage and the cloud cover impact of the initial satellite scheduling task, thereby obtaining a meteorological optimization satellite scheduling task.

[0007] According to an embodiment of the present invention, the meteorological optimization satellite scheduling task is optimized and trained using multi-dimensional indicators until the function value constructed based on the multi-dimensional indicator value of the meteorological optimization satellite scheduling task and the weight value of the multi-dimensional indicator meets the predetermined indicator balance condition, thereby obtaining the indicator optimization satellite scheduling task, including: constructing an objective function for each dimensional indicator; inputting the dimensional indicator information of the meteorological optimization satellite scheduling task into the objective function, and outputting the dimensional indicator value of the meteorological optimization satellite scheduling task; adjusting the weight value of the dimensional indicator until the dimensional indicator values ​​of the meteorological optimization satellite scheduling task meet the predetermined indicator balance condition, thereby obtaining the indicator optimization satellite scheduling task.

[0008] According to an embodiment of the present invention, the dimensional indicators include at least one of the following: the coverage rate of the sub-area covered by the meteorological optimization satellite scheduling task, the overlap rate of the sub-area covered by the meteorological optimization satellite scheduling task, the azimuth of the satellite imaging strip, the number of satellites associated with the meteorological optimization satellite scheduling task, the number of imaging strips of the meteorological optimization satellite scheduling task, the execution time of the meteorological optimization satellite scheduling task, and the cloud coverage of the area observed by the meteorological optimization satellite scheduling task; the objective function includes at least one of the following: a coverage function used to describe the coverage rate of the sub-area covered by the meteorological optimization satellite scheduling task, an overlap rate function used to describe the overlap rate of the sub-area covered by the meteorological optimization satellite scheduling task, an azimuth function used to describe the azimuth of the satellite imaging strip, a satellite number function used to describe the number of satellites associated with the meteorological optimization satellite scheduling task, a strip number function used to describe the number of imaging strips of the meteorological optimization satellite scheduling task, a duration function used to describe the execution time of the meteorological optimization satellite scheduling task, and a cloud coverage function used to describe the cloud coverage of the area observed by the meteorological optimization satellite scheduling task.

[0009] According to an embodiment of the present invention, the dimensional index information of the meteorological optimization satellite scheduling task is input into the objective function, and the dimensional index value of the meteorological optimization satellite scheduling task is output, including: the area of ​​the sub-region covered by the meteorological optimization satellite scheduling task and the total number of sub-regions in the target area are input into the coverage function, and the coverage index value is output; the overlapping area between each two sub-regions and the total number of sub-regions are input into the overlap rate function, and the overlap rate index value is output; the number of satellite orbits and the azimuth angle of the satellite strip in the meteorological optimization satellite scheduling task are input into the azimuth function , output the azimuth index value; input the binary function value of the satellite selected by the meteorological optimization satellite scheduling task into the satellite number function, and output the satellite number index value; input the binary function value of the satellite strip into the strip number function, and output the satellite strip number index value; input the execution time of the satellite orbit and the number of observation tasks in the meteorological optimization satellite scheduling task into the duration function, and output the duration index value; input the cloud coverage of the sub-region, the influence coefficient based on cloud amount, and the total number of sub-regions into the cloud coverage function, and output the cloud coverage index value.

[0010] According to an embodiment of the present invention, a supplementary satellite scheduling task is generated based on the intersection of satellite strips in a satellite set that are not called by the indicator-optimized satellite scheduling task and the blank area, including: traversing the satellite strips that are not called by the indicator-optimized satellite scheduling task, screening out satellite strips that can cover the blank area, and obtaining supplementary satellite strips; performing overlap detection on the intersection area of ​​the supplementary satellite strips and the satellite strips of the indicator-optimized satellite scheduling task, and adjusting the observation range of the supplementary satellite strips so that the area of ​​the intersection area reaches a minimum threshold; and generating a supplementary satellite scheduling task in response to the coverage rate of the blank area covered by the satellite strips reaching a predetermined coverage rate threshold, or the absence of available satellite strips.

[0011] According to an embodiment of the present invention, a satellite set is scheduled according to an indicator-optimized satellite scheduling task and a supplementary satellite scheduling task, including: detecting satellite strips in the indicator-optimized satellite scheduling task and satellite strips in the supplementary satellite scheduling task to obtain a detection result; in response to the presence of completely covered satellite strips in the detection result, removing the completely covered satellite strips to obtain a target satellite scheduling task; and scheduling the satellite set using the target satellite scheduling task.

[0012] Another aspect of the present invention provides a multi-satellite task scheduling device, which includes: a first generation module for generating an initial satellite scheduling task for a target area according to the coverage of a sub-area in the target area to be observed by a satellite strip of a satellite set, wherein the sub-area is obtained by dividing the target area using polygons of a predetermined size, and the sub-area in the target area not covered by the satellite strip is regarded as a blank area. The initial satellite scheduling task includes a plurality of satellite sub-tasks, and the satellite sub-tasks are used to complete the observation of the sub-area by the satellite; a filtering module for using meteorological factors to filter the initial satellite scheduling tasks whose degree of influence by meteorological factors is greater than a predetermined value. The satellite subtasks with a degree threshold are filtered out to obtain the meteorological optimization satellite scheduling task; the indicator optimization module is used to optimize the meteorological optimization satellite scheduling task using multi-dimensional indicators until the function value constructed based on the multi-dimensional indicator value of the meteorological optimization satellite scheduling task and the weight value of the multi-dimensional indicator meets the predetermined indicator balance condition, and the indicator optimization satellite scheduling task is obtained; the second generation module is used to generate a supplementary satellite scheduling task based on the intersection of the satellite strips in the satellite set that are not called by the indicator optimization satellite scheduling task and the blank area; the scheduling module is used to schedule the satellite set according to the indicator optimization satellite scheduling task and the supplementary satellite scheduling task.

[0013] Another aspect of the present invention provides an electronic device comprising: one or more processors; a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above-mentioned multi-satellite mission scheduling method.

[0014] Another aspect of the present invention further provides a computer-readable storage medium having a computer program or instruction stored thereon, which implements the steps of the multi-satellite task scheduling method when the computer program or instruction is executed by a processor.

[0015] Another aspect of the present invention further provides a computer program product, including a computer program or instructions, which implements the steps of the multi-satellite task scheduling method when executed by a processor.

[0016] According to an embodiment of the present invention, the target area is discretized by dividing it into polygons of predetermined size. An initial satellite scheduling task is generated based on the coverage of the sub-areas by satellite strips. The initial satellite scheduling task is optimized using meteorological factors to obtain a meteorologically optimized satellite scheduling task. The meteorologically optimized satellite scheduling task is optimized using multi-dimensional indicators to obtain an indicator-optimized satellite scheduling task. A supplementary satellite scheduling task is generated based on the intersection of the blank areas of the unused satellite strip domain in the satellite set. The satellite set is scheduled based on the indicator-optimized satellite scheduling task and the supplementary satellite scheduling task. Discretizing the target area provides a more precise basis for satellite scheduling, refines the satellite scheduling task, at least partially avoids duplicate scheduling of satellite resources, and improves satellite resource utilization and overall coverage of the target area. By performing multiple optimizations using meteorological factors and multi-dimensional indicators, the feasibility and reliability of the satellite scheduling task can be improved, thereby increasing the execution efficiency of the satellite scheduling task. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above contents and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:

[0018] Figure 1 A diagram schematically illustrates an application scenario of multi-satellite task scheduling according to an embodiment of the present invention;

[0019] Figure 2 A flowchart of a multi-satellite mission scheduling method according to an embodiment of the present invention is schematically shown;

[0020] Figure 3 A schematic diagram schematically illustrates a method of dividing a target area according to an embodiment of the present invention;

[0021] Figure 4 A flowchart of a multi-satellite mission scheduling method according to another embodiment of the present invention is schematically shown;

[0022] Figure 5 A schematic block diagram of a multi-satellite task scheduling device according to an embodiment of the present invention is shown;

[0023] Figure 6 A block diagram of an electronic device suitable for implementing a multi-satellite task scheduling method according to an embodiment of the present invention is schematically shown. DETAILED DESCRIPTION

[0024] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of embodiments of the present invention. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concept of the present invention.

[0025] The terms used herein are only for describing specific embodiments and are not intended to limit the present invention. The terms "comprise", "include", etc. used herein indicate the presence of the features, steps, operations and / or components, but do not exclude the presence or addition of one or more other features, steps, operations or components.

[0026] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0027] When expressions such as "at least one of A, B, and C, etc." are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0028] The research on multi-satellite joint task scheduling in the target area aims to improve the utilization efficiency of satellite resources and the effectiveness of task execution. For example, a genetic algorithm is used to encode, crossover and mutate satellite subtasks, and a task scheduling solution is obtained after multiple iterations. For another example, by simulating the physical phenomena in the metal annealing process, a random search is performed in the solution space, and inferior solutions are accepted with a certain probability, thereby jumping out of the local optimal solution and finally finding the global approximate optimal solution. This process can also be used to handle large-scale satellite task scheduling. For another example, at each decision-making stage, the optimal satellite task allocation method under the current state is selected, and the entire task scheduling solution is gradually constructed. However, these processes may cause the scheduling results to fall into a local optimal solution, making it difficult to ensure sufficient planning efficiency and observation coverage. At the same time, there will be certain limitations in dealing with complex constraints and global optimization problems. The current multi-satellite task scheduling may have the following problems:

[0029] The problem of low satellite resource utilization may occur due to the large waste of satellite resources in the target area segmentation method and the observation meta-task generation mode. For example, the multi-satellite joint task scheduling in the target area generally adopts different methods to segment the area, generate observation meta-task information, and then select meta-tasks by calling the task scheduling algorithm. When performing regional segmentation, when the division is based on the satellite side swing angle, although the meta-task information obtained can meet the use requirements, there are many repeated meta-task strips, which will cause a large waste of resources. To address this problem, the embodiment of the present invention will use a grid-based division method to decompose the target area, and then combine it with the visibility analysis algorithm to perform visibility calculation on each grid area.

[0030] The difficulty of emergency response and real-time adjustments in multi-satellite joint mission scheduling leads to insufficient real-time responsiveness. To address this, rapid decision-making and efficient response to emergencies are crucial; on the one hand, optimal resource allocation strategies must be provided during the scheduling phase to ensure the feasibility of long-term missions. However, to meet the real-time and responsiveness requirements of meta-task-based task scheduling, greedy algorithms can be used to quickly provide feasible solutions within a short local window, quickly generating executable plans. However, greedy algorithms lack a global perspective and are prone to being trapped in local optimal solutions, making it difficult to achieve a global optimum.

[0031] Low task execution rates are caused by the difficulty in balancing global and local optimization under multiple constraints. For example, when dealing with complex and ever-changing multi-satellite joint task scheduling problems, which often involve multiple constraints, a balance between global and local optimization is necessary. On the one hand, the algorithm needs to effectively explore the global solution space and find the optimal overall task scheduling solution; on the other hand, the algorithm also needs to be able to quickly optimize local issues to ensure rapid response and efficient execution of task scheduling. In short, the algorithm needs to ensure that it can find the optimal solution under complex constraints while being able to make rapid local adjustments to ensure that reasonable choices are always made in a dynamic environment.

[0032] In view of this, an embodiment of the present invention provides a multi-satellite task scheduling method for improving satellite resource utilization, improving task execution efficiency, and improving real-time response efficiency. Specifically, the method includes generating an initial satellite scheduling task for the target area according to the coverage of sub-areas in the target area to be observed by satellite strips of the satellite set, wherein the sub-areas are obtained by dividing the target area using polygons of a predetermined size, and the sub-areas in the target area not covered by the satellite strips are regarded as blank areas. The initial satellite scheduling task includes multiple satellite sub-tasks, and the satellite sub-tasks are used to complete the satellite observation of the sub-areas; using meteorological factors, the satellite sub-tasks in the initial satellite scheduling task whose degree of influence by meteorological factors is greater than a predetermined degree threshold are filtered out to obtain a meteorological optimized satellite scheduling task; using multi-dimensional indicators to optimize the meteorological optimized satellite scheduling task until a function value constructed based on the multi-dimensional indicator value of the meteorological optimized satellite scheduling task and the weight value of the multi-dimensional indicator meets a predetermined indicator balance condition, to obtain an indicator optimized satellite scheduling task; generating a supplementary satellite scheduling task according to the intersection of the satellite strips in the satellite set that are not called by the indicator optimized satellite scheduling task and the blank area; scheduling the satellite set according to the indicator optimized satellite scheduling task and the supplementary satellite scheduling task.

[0033] Figure 1 The following schematically illustrates an application scenario of multi-satellite mission scheduling according to an embodiment of the present invention.

[0034] like Figure 1 As shown, an application scenario 100 according to this embodiment may include a terminal device 101, a network 102, a server 103, a radar 104, a satellite set 105, and a target area 106. The network 102 is used to provide a communication link between the terminal device 101 and the server 103, and between the server 103 and the radar 104. The network 102 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0035] A user can use terminal device 101 to interact with server 103 via network 102 to receive or send messages, for example, sending a request to schedule satellites in satellite constellation 105 to image target area 106, or receiving images of target area 106 captured by satellites in satellite constellation 105. Terminal device 101 may be installed with various communication client applications, such as satellite scheduling applications, shopping applications, web browser applications, search applications, instant messaging tools, email clients, social networking platform software, etc. (These are merely examples). Terminal device 101 may be any electronic device with a display screen and web browsing support, including but not limited to smartphones, tablet computers, laptop computers, and desktop computers.

[0036] The server 103 may be a server that provides various services, such as a background management server (for example only) that supports requests sent by users using the terminal device 101. The background management server may analyze and process received requests and other data, and provide feedback to the terminal device 101 regarding the processing results (e.g., images, web pages, information, or data of the target area 106 obtained or generated in accordance with the request).

[0037] Radar 104 can be used to send signals to satellites in satellite set 105 or receive signals transmitted by satellites. Satellites in satellite set 105 can be used to observe target area 106. Satellites in satellite set 105 can include optical satellites and SAR (Synthetic Aperture Radar) satellites. Target area 106 can be the area to be observed. Server 103 can use radar 104 to schedule satellites in satellite set 105 according to the multi-satellite scheduling task to complete the imaging of target area 106.

[0038] It should be noted that the multi-satellite task scheduling method provided in the embodiments of the present invention can generally be executed by the server 103. Accordingly, the multi-satellite task scheduling apparatus provided in the embodiments of the present invention can generally be set in the server 103. The multi-satellite task scheduling method provided in the embodiments of the present invention can also be executed by a server or server cluster that is different from the server 103 and can communicate with the terminal device 101, the radar 104, and / or the server 103. Accordingly, the multi-satellite task scheduling apparatus provided in the embodiments of the present invention can also be set in a server or server cluster that is different from the server 103 and can communicate with the terminal device 101, the radar 104, and / or the server 103.

[0039] It should be understood that Figure 1 The number of terminal devices, networks, servers, radars, satellite sets, satellites in the satellite set, and target areas is merely illustrative. Any number of terminal devices, networks, servers, radars, satellite sets, satellites in the satellite set, and target areas may be provided as needed.

[0040] The following will be based on Figure 1 The scene described by Figure 2~Figure 3 The multi-satellite task scheduling method according to an embodiment of the present invention is described in detail.

[0041] Figure 2 The flowchart of the multi-satellite mission scheduling method according to an embodiment of the present invention is schematically shown.

[0042] like Figure 2 As shown, the multi-satellite mission scheduling method of this embodiment includes operations S210 to S250.

[0043] In operation S210, an initial satellite scheduling task for the target area is generated based on coverage of sub-areas in the target area to be observed by satellite strips of the satellite set, where the sub-areas are obtained by dividing the target area using polygons of a predetermined size, and sub-areas in the target area not covered by the satellite strips are treated as blank areas. The initial satellite scheduling task includes multiple satellite sub-tasks, and the satellite sub-tasks are used to complete the satellite observation of the sub-areas.

[0044] In operation S220 , the meteorological factors are used to filter out satellite subtasks in the initial satellite scheduling task whose degree of influence by meteorological factors is greater than a predetermined threshold, thereby obtaining a meteorologically optimized satellite scheduling task.

[0045] In operation S230, the meteorological optimization satellite scheduling task is optimized and trained using multi-dimensional indicators until the function value constructed based on the multi-dimensional indicator value of the meteorological optimization satellite scheduling task and the weight value of the multi-dimensional indicator meets the predetermined indicator balance condition, thereby obtaining the indicator optimization satellite scheduling task.

[0046] In operation S240 , a supplementary satellite scheduling task is generated according to the intersection of the satellite strips in the satellite set that are not called by the indicator-optimized satellite scheduling task and the blank area.

[0047] In operation S250 , the satellite scheduling tasks and the supplementary satellite scheduling tasks are optimized according to the indicators, and the satellite set is scheduled.

[0048] Optionally, the satellite set may include multiple satellites that can be used to observe the target area, such as optical satellites, SAR, and hyperspectral satellites.

[0049] Alternatively, a satellite strip may refer to the area covered by continuous images or data acquired by sensors onboard a satellite (such as optical cameras, radar, etc.) while observing the Earth as the satellite orbits. These strips may be long, with their width related to the sensor's field of view and the satellite's altitude, and their length related to the duration of the satellite's continuous imaging or orbital coverage.

[0050] Optionally, the target area may be an area on the ground to be observed. Sub-areas within the target area may be polygonal areas within the target area, formed by dividing the target area using polygons of a predetermined size. The polygons may be at least one of a quadrilateral and a hexagon, and the predetermined size may be adaptively adjusted based on actual needs. In some embodiments, a satellite strip may not fully cover one or more sub-areas, so the satellite strip scheduled in the initial satellite scheduling mission may not fully cover all sub-areas within the target area. Blank areas may be uncovered sub-areas within the target area.

[0051] Optionally, the initial satellite scheduling task may include multiple satellite subtasks, and each satellite subtask may be an observation task of a satellite for a sub-area.

[0052] Optionally, the meteorological factor may include cloud cover, in which case the value of the degree of influence of the meteorological factor may be cloud cover, and the predetermined degree threshold may be a predetermined cloud cover threshold. In one embodiment, satellite subtasks for observing sub-regions where cloud cover exceeds the predetermined cloud cover threshold can be eliminated from the initial satellite scheduling task, and SAR observations can be invoked to observe these sub-regions, thereby ensuring that the multi-satellite scheduling task is not interfered with in complex meteorological environments. By detecting cloud cover and analyzing cloud distribution, the multi-satellite scheduling task can be efficiently executed under various meteorological conditions.

[0053] Optionally, the multidimensional indicators may include at least one of the coverage rate of the sub-area covered by the meteorological optimization satellite scheduling task, the overlap rate of the sub-area covered by the meteorological optimization satellite scheduling task, the azimuth of the satellite imaging strip, the number of satellites associated with the meteorological optimization satellite scheduling task, the number of imaging strips of the meteorological optimization satellite scheduling task, the execution time of the meteorological optimization satellite scheduling task, and the cloud coverage of the area observed by the meteorological optimization satellite scheduling task. Each indicator can be configured with a function, and these functions are used to optimize the meteorological optimization satellite scheduling task until the meteorological optimization satellite scheduling task meets the predetermined indicator balance condition, that is, to achieve a balance between these multidimensional indicators. The predetermined indicator balance condition is, for example, by adjusting the weight value of the multidimensional indicator value so that the rate of change of the function value of the multidimensional indicator value and the weight value of the multidimensional indicator is less than the predetermined rate of change, or for example, the indicator values ​​of each dimension meet the threshold value of each dimension indicator, etc., and can be adaptively adjusted according to actual needs.

[0054] Optionally, the satellite strips scheduled in the initial satellite scheduling task may not completely cover all sub-areas on the target area. There may still be blank areas on the target area. For these blank areas, the intersection of the satellite strips in the satellite set that are not called by the indicator optimization satellite scheduling task and the blank areas can be used to determine supplementary satellite strips. Supplementary satellite scheduling tasks are generated based on these supplementary satellite strips. The satellite set can be scheduled using the indicator optimization satellite scheduling task and the supplementary satellite scheduling task to complete the observation of the target area.

[0055] According to an embodiment of the present invention, the target area is discretized by dividing it into polygons of predetermined size. An initial satellite scheduling task is generated based on the coverage of the sub-areas by satellite strips. The initial satellite scheduling task is optimized using meteorological factors to obtain a meteorologically optimized satellite scheduling task. The meteorologically optimized satellite scheduling task is optimized using multi-dimensional indicators to obtain an indicator-optimized satellite scheduling task. A supplementary satellite scheduling task is generated based on the intersection of the blank areas of the unused satellite strip domain in the satellite set. The satellite set is scheduled based on the indicator-optimized satellite scheduling task and the supplementary satellite scheduling task. Discretizing the target area provides a more precise basis for satellite scheduling, refines the satellite scheduling task, at least partially avoids duplicate scheduling of satellite resources, and improves satellite resource utilization and overall coverage of the target area. By performing multiple optimizations using meteorological factors and multi-dimensional indicators, the feasibility and reliability of the satellite scheduling task can be improved, thereby increasing the execution efficiency of the satellite scheduling task.

[0056] Optionally, operation S210 described above may be a process of discretizing the target area. The target area is discretized into sub-areas, such as grids, and the area covered by the satellite strips is determined by counting the number of sub-areas covered by the satellite strips. The accuracy of this method is related to the size of the sub-areas. The smaller the sub-area area, the higher the accuracy. The sub-area division method is determined based on the adaptability analysis of the sub-areas. In some embodiments, the coverage rate of quadrilateral grids has advantages in regular rectangular areas, and it is easy to control the grid overlap. The regularity of quadrilaterals makes it possible to completely cover the area in a rectangular manner in large-area mission planning without the need for additional edge expansion. However, the hexagonal grid needs to be expanded at the edges to cover the rectangular area, which may result in an increase in redundant grids in the area. Based on the adaptability analysis of the above-mentioned sub-areas, the embodiment of the present invention takes the use of quadrilaterals to divide the target area as an example to improve division efficiency, reduce computing resource consumption, and shorten calculation time.

[0057] Figure 3 The following schematically shows a schematic diagram of dividing the target area according to an embodiment of the present invention. Figure 3 The process of dividing the target area by using polygons of a predetermined size mentioned in the above operation S210 is described by taking .

[0058] In some embodiments, the process of dividing the target area may be as follows:

[0059] (1) Calculate the bounding rectangle of the target region R and evenly divide the target region into quadrilateral subregions, i.e., subregion sizes d. In one embodiment, the area of ​​the bounding rectangle may be the minimum area that can enclose the target region R.

[0060] (2) Traverse all nodes in the sub-region and determine whether the node is inside the target region R according to the ray method. The matrix A is stored. If A(i, j) is 1, it means that the node (i, j) is inside the target region R. If A(i, j) is not 1, it means that the node (i, j) is outside the target region R. The node (i, j) can be the i-th sub-region from left to right and the j-th sub-region from top to bottom in the target region.

[0061] (3) Based on the matrix A, the initial circumscribed matrix B of the target region R is obtained. If B(I, j) is 1, it means that at least one of the four vertices of the subregion at position (I, J) is inside the target region R. If B(I, J) is not 1, it means that the four vertices of the subregion at position (I, j) are not inside the target region R. Node (I, j) can be the i-th subregion from left to right and the j-th subregion from top to bottom of the target region. In one embodiment, the matrix A can be used to determine the subregion inside the target region R, and the matrix B can be used to determine the subregion at the boundary of the target region R.

[0062] (4) Generate a node sorting matrix D, set the index value index to 0; traverse the matrix A, if A(i, j) = 0, then D(i, j) = 1; if A(i, j) = 1, then set index to add 1 so that D(i, j) = index, until the matrix A is traversed.

[0063] (5) Generate the node information matrix N according to the node sorting matrix D, where N(i, j) and N(i, 2) store the horizontal and vertical coordinates of the node with index i, respectively.

[0064] (6) Generate a cell information matrix C based on matrices A, B, and D, where C(i, 1), C(i, 2), C(i, 3), and C(i, 4) store the indices of the upper left vertex, upper right vertex, lower right vertex, and lower left vertex of the i-th sub-region to be covered, respectively.

[0065] Since there are some repeated nodes between sub-regions, using the above storage method, when judging whether a sub-region is covered by a polygon, there is no need to repeatedly judge the nodes, which can reduce the amount of calculation.

[0066] A specific power-on / off moment can be selected, for example, when a satellite payload (such as a camera or radar) is activated (turned on) or deactivated (turned off) within a specific time window. The resulting swath area, derived from the roll angle, is called a coverage pattern. In one embodiment, the power-on / off moment determines the length of the satellite swath (along the orbital direction). The roll angle determines the lateral offset of the swath (perpendicular to the orbital direction). Based on the length and lateral offset, a tilted rectangular observation area can be formed on the ground, known as the coverage pattern.

[0067] For a given coverage mode And a given subregion j, the four vertices of the subregion all cover the pattern If the sub-region j is inside or on the edge, then the sub-region j is called a safe coverage sub-region. ,if The length of is equal to the maximum length of the stripe covered by the coverage opportunity S, and there are at least two The vertex of the fully covered subregion is exactly at The left and top sides of This is the longest basic coverage pattern. Coverage opportunities can be used to determine the feasibility of satellite imaging of the target area.

[0068] Based on the above definition, the process of generating coverage patterns based on the discretization of sub-regions of the target area can be as follows:

[0069] Input: Coverage opportunity S, circumscribed subregion J of target region R;

[0070] Output: Coverage pattern set .

[0071] Step 1, let the set .

[0072] Step 2: Filter out the node set from sub-region j , so that the sub-region The node inside can be used as the left node.

[0073] Step 3, if , then go to step 9; otherwise, select As the left node, execute step 4.

[0074] Step 4, according to the left node , from the sub-region Node collection at the filter , making The nodes inside can be used as upper nodes. (left) represents the left node, (up) indicates the upper node.

[0075] Step 5, if , then execute step 8; otherwise select As the upstream node, proceed to step 6.

[0076] Step 6: Construct the overlay pattern .

[0077] Step 7, let ,make , proceed to step 5.

[0078] Step 8, let , proceed to step 3.

[0079] Step 9: Output the collection .

[0080] In traditional mission planning, regional coverage is often rough, prone to blind spots or redundant coverage, leading to waste of resources. In an embodiment of the present invention, the target area is discretized into fine-grained sub-region units, and the target area is subdivided into multiple small sub-region units. The coverage requirements of each sub-region unit can be accurately allocated based on factors such as satellite orbit and viewing angle. This method can refine the satellite's mission planning, provide a more accurate basis for the generation of satellite coverage patterns, make the coverage area of ​​each satellite more accurate, avoid blind spots, optimize the collaborative work between satellites, and flexibly adjust according to actual mission requirements to ensure the maximum satisfaction of each mission goal, effectively improving the overall coverage rate and efficiency of large-area coverage.

[0081] Optionally, the degree of influence of meteorological factors in operation S220 may include cloud cover, and the predetermined degree threshold may include a predetermined cloud cover threshold. Operation S220 may include the following processes: acquiring cloud cover data for the target area through an integrated meteorological interface service, and generating cloud cover for sub-areas within the target area based on satellite transit times and sub-area locations; and filtering out satellite sub-tasks whose cloud cover exceeds the predetermined cloud cover threshold in the initial satellite scheduling task, thereby obtaining a meteorologically optimized satellite scheduling task.

[0082] Optionally, through comprehensive detection and cloud distribution analysis, it can be ensured that multi-satellite joint scheduling tasks can be efficiently executed under various meteorological conditions to reduce meteorological impacts.

[0083] The impact of meteorological factors can include cloud cover, geographical coverage differences, and satellite scheduling optimization.

[0084] The impact of cloud cover, such as the density of clouds, directly determines whether remote sensing satellites can obtain effective images. The greater the cloud cover, the worse the image quality, significantly reducing the imaging capabilities of optical satellites.

[0085] The impact of geographical coverage differences, for example, the cloud cover distribution in different regions is significantly different, and sub-regions with higher cloud cover need to eliminate optical satellite sub-missions.

[0086] Satellite scheduling optimization impact, such as real-time adjustment of initial satellite scheduling task arrangements based on cloud cover, especially in areas with large cloud cover, giving priority to scheduling unaffected SAR satellites to perform tasks to ensure that tasks are completed on time.

[0087] The meteorological factor optimization process can be divided into real-time meteorological data acquisition and cloud cover calculation, cloud removal and optical satellite scheduling, and SAR satellite priority scheduling.

[0088] Real-time meteorological data acquisition and cloud cover calculation: Cloud cover data for the target area is acquired through integrated meteorological interface services. Cloud cover for the target area is calculated and dynamically evaluated in real time based on satellite transit times and sub-areas (i.e., grid locations). Based on the evaluation structure, satellite subtasks with cloud cover exceeding a predetermined cloud cover threshold are filtered out, resulting in meteorologically optimized satellite scheduling tasks.

[0089] In one embodiment, in the initial satellite scheduling task, satellite subtasks whose cloud coverage exceeds a predetermined cloud coverage threshold are filtered out to obtain a meteorologically optimized satellite scheduling task. The process may include cloud removal, optical satellite scheduling, and SAR satellite priority scheduling. For example: sub-regions whose cloud coverage exceeds a predetermined cloud coverage threshold are screened out as sub-regions to be allocated; satellite subtasks used to observe the sub-regions to be allocated in the initial satellite scheduling task are filtered out, and a backup observation system is scheduled for the sub-regions to be allocated; and the weight value of the target area coverage of the initial satellite scheduling task and the weight value of the cloud cover impact are adjusted until a predetermined meteorological balance condition is reached between the target area coverage and the cloud cover impact of the initial satellite scheduling task, thereby obtaining a meteorologically optimized satellite scheduling task.

[0090] Cloud cover assessments can identify sub-areas where cloud cover exceeds a predetermined cloud cover threshold of 30%. These sub-areas are then prevented from being assigned to optical satellites, and satellite sub-tasks used to observe these sub-areas are eliminated. These sub-areas can then be prioritized for allocation. For these pending sub-areas, backup observation systems, such as SAR satellites, can be dispatched to observe these sub-areas, ensuring mission execution even in adverse weather conditions. SAR satellites are unaffected by weather and, in particular, provide stable imaging capabilities under high cloud cover or extensive cloud cover. Therefore, prioritizing SAR satellites ensures that missions remain uninterrupted in complex weather environments.

[0091] The following describes the optimization process of meteorological factors using an embodiment:

[0092] (1) Cloud cover assessment: The meteorological factor optimization strategy can be to conduct real-time assessment of the cloud cover of each sub-area of ​​the target area. The cloud cover calculation formula is shown in formula (1):

[0093] (1)

[0094] in, represents the cloud cover percentage of sub-region i, that is, the cloud cover, is the cloud coverage area in the sub-region, is the total area of ​​the sub-region. If the cloud cover exceeds the predetermined threshold of 30%, the optical satellite mission in that sub-region will be eliminated.

[0095] (2) Scheduling and selection of initial satellite missions: During the scheduling process, the task scheduler dynamically adjusts the satellite selection based on the cloud cover assessment results. The task scheduling process determines the optimal satellite selection based on the execution time, observation time, orbital parameters and cloud cover distribution of each satellite. For a given target area R, if If the cloud coverage exceeds the predetermined threshold of 30%, the optical satellite cannot be used to observe the sub-area to be allocated, and the system will automatically dispatch the SAR satellite to observe the sub-area to be allocated.

[0096] (3) Multi-objective optimization task planning: To ensure efficient execution of tasks under variable weather conditions, task scheduling adopts a multi-objective optimization method. In the multi-objective optimization framework, the objective function L is used to evaluate the effect of task scheduling. The function L can be expressed as formula (2):

[0097] (2)

[0098] Coverage represents the coverage of the target area, that is, the satellite's observation coverage efficiency of the target area (such as the coverage area percentage or sub-area coverage rate), reflecting the spatial coverage efficiency of the mission. Cloud represents the negative impact of cloud cover on mission execution, namely the negative impact of cloud cover on optical satellite imaging (high cloud cover may render data invalid). Large cloud cover can increase the difficulty of mission execution. Coverage can be calculated by the ratio of the number of sub-areas covered by satellite swaths to the total number of sub-areas in the target area. Cloud can be calculated by summing the product of the cloud cover of all sub-areas in the target area and the weight of each sub-area, and then dividing it by the total number of sub-areas in the target area. The optimization goal for Coverage is to maximize coverage, while the optimization goal for Cloud is to minimize coverage.

[0099] By adjusting the weight parameters , , which can achieve the predetermined meteorological balance condition between the target area coverage and cloud cover impact of the initial satellite scheduling mission. The predetermined meteorological balance condition can be achieved by adjusting the weight parameter , , The rate of change of L is within the predetermined range, that is, L does not change significantly, or L reaches the maximum value. By adjusting the weight parameter , It can achieve a balance between mission coverage efficiency, timeliness and cloud cover impact, thereby optimizing the scheduling of satellite resources.

[0100] Optionally, operation S230 described above may include the following process: constructing an objective function for each dimensional indicator; inputting the dimensional indicator information of the meteorological optimization satellite scheduling task into the objective function, and outputting the dimensional indicator value of the meteorological optimization satellite scheduling task; adjusting the weight value of the dimensional indicator until the dimensional indicator values ​​of the meteorological optimization satellite scheduling task meet the predetermined indicator balance condition, and obtaining the indicator optimization satellite scheduling task.

[0101] Optionally, the dimensional indicators may include at least one of the following: the coverage rate of the sub-area covered by the meteorological optimization satellite scheduling task, the overlap rate of the sub-area covered by the meteorological optimization satellite scheduling task, the azimuth of the satellite imaging strip, the number of satellites associated with the meteorological optimization satellite scheduling task, the number of imaging strips of the meteorological optimization satellite scheduling task, the execution time of the meteorological optimization satellite scheduling task, and the cloud coverage of the area observed by the meteorological optimization satellite scheduling task; the objective function constructed according to the dimensional indicators may include at least one of the following: a coverage function for describing the coverage rate of the sub-area covered by the meteorological optimization satellite scheduling task, an overlap rate function for describing the overlap rate of the sub-area covered by the meteorological optimization satellite scheduling task, an azimuth function for describing the azimuth of the satellite imaging strip, a satellite number function for describing the number of satellites associated with the meteorological optimization satellite scheduling task, a strip number function for describing the number of imaging strips of the meteorological optimization satellite scheduling task, a duration function for describing the execution time of the meteorological optimization satellite scheduling task, and a cloud coverage function for describing the cloud coverage of the area observed by the meteorological optimization satellite scheduling task.

[0102] Optionally, the coverage rate of the sub-area covered by the meteorological optimization satellite scheduling task measures the coverage of the target area and is one of the optimization objectives. The goal is to increase the coverage rate of the target area as much as possible so that each sub-area within the target area is covered by as many satellite tracks as possible. A high coverage rate means that more satellites pass through the area, thereby improving the execution efficiency of the task. Coverage rate function It can be shown as formula (3).

[0103] (3)

[0104] in, It is a sub-region The coverage area, is the number of sub-regions in the target region.

[0105] Optionally, the overlap rate of the sub-areas covered by the meteorological optimization satellite scheduling mission is an indicator to measure the duplication of satellite coverage areas. A lower overlap rate helps avoid wasting satellite resources and ensures that more areas are efficiently covered. Reducing the overlap rate requires reasonable arrangement of satellite orbits and field of view angles to avoid excessive duplication of coverage of the same area. Overlap rate function It can be shown as formula (4).

[0106] (4)

[0107] in, For sub-region and subregions The overlapping area between them.

[0108] Optionally, the satellite imaging strip azimuth describes the direction of the imaging strip in the satellite strip, affecting the field of view and overlap area during satellite imaging. Reasonable strip azimuth selection can optimize the satellite's coverage path to the target area, reduce unnecessary overlap and improve mission efficiency. Azimuth function It can be shown as formula (5).

[0109] (5)

[0110] in, is the azimuth of the satellite imaging swath, is the number of satellite orbits.

[0111] Optionally, the number of satellites associated with the meteorological optimization satellite scheduling task refers to the number of satellites participating in the task. Usually, the goal of the task is to minimize the number of satellites used. By properly arranging the satellite orbits and task scheduling, the minimum number of satellites can be used to meet the coverage requirements of a large area. Satellite number function It can be shown as formula (6).

[0112] (6)

[0113] in, is a binary function, if If a satellite is selected, , otherwise 0, is the total number of satellites.

[0114] Optionally, the number of imaging strips for a meteorological optimization satellite scheduling mission refers to the total number of imaging strips divided by the satellite orbit. The number of strips directly affects the timeliness and resource utilization efficiency of the mission. Mission planning requires controlling the number of strips to meet the mission coverage requirements while avoiding excessive strip cutting. Strip number function It can be shown as formula (7).

[0115] (7)

[0116] in, is a binary function, if is activated, then ,like If not activated, , is the total number of stripes.

[0117] Optionally, the timeliness of task execution can be the execution time of the meteorological optimization satellite scheduling task, that is, the total execution time of the task. The optimization goal is to shorten the execution time of the task as much as possible to ensure that the target area is covered in a shorter time. Duration function It can be shown as formula (8).

[0118] (8)

[0119] in, It's a satellite The execution time of is the number of tasks.

[0120] Optionally, the cloud cover of the area observed by the meteorological optimization satellite scheduling mission represents the cloud coverage of the target area, which has a significant impact on the imaging of optical satellites. High cloud cover areas may lead to image quality degradation or even failure to image, so the impact of cloud cover on meteorological optimization satellite scheduling mission planning needs to be considered. During optimization, areas with low cloud cover are selected for optical imaging as much as possible, and SAR satellites (synthetic aperture radar) are preferred for imaging. Cloud cover function It can be shown as formula (9).

[0121] (9)

[0122] in, can be the cloud coverage of sub-region i, It can be a penalty factor based on cloud cover. High cloud cover areas will result in larger penalties, thus limiting the mission performance of optical satellites.

[0123] In some embodiments, by inputting the dimensional index information of the meteorological optimization satellite scheduling task into the objective function, the dimensional index value of the meteorological optimization satellite scheduling task is output. The dimensional index value includes at least one of the following: coverage index value, overlap index value, azimuth index value, satellite number index value, satellite strip number index value, duration index value, cloud coverage index value. For example, the area of ​​the sub-region covered by the meteorological optimization satellite scheduling task and the total number of sub-regions in the target area are input into the coverage function shown in formula (3), and the coverage index value is output; the overlapping area between each two sub-regions and the total number of sub-regions are input into the overlap function shown in formula (4), and the overlap index value is output; the number of satellite orbits and the azimuth of the satellite strip in the meteorological optimization satellite scheduling task are input into the azimuth function shown in formula (5), and the azimuth index value is output; the satellites selected by the meteorological optimization satellite scheduling task are input into the azimuth function shown in formula (5), and the azimuth index value is output; the satellites in the satellite set are input into the azimuth function shown in formula (6), and the azimuth index value is output. The binary function value is input into the satellite number function shown in formula (6) to output the satellite number index value; the binary function value of the satellite strip is input into the strip number function shown in formula (7) to output the satellite strip number index value; the execution time of the satellite orbit and the number of observation tasks in the meteorological optimization satellite scheduling task are input into the duration function shown in formula (8) to output the duration index value; the cloud coverage of the sub-region, the influence coefficient based on cloud cover and the total number of sub-regions are input into the cloud cover function shown in formula (9) to output the cloud cover index value.

[0124] Multiple objective functions need to be weighted appropriately based on mission requirements. By setting the weights for each objective function, the impact of different objectives on the final solution can be adjusted. For example, coverage and timeliness might be prioritized in one multi-satellite scheduling task, while cloud cover and the number of swaths might be prioritized in another multi-satellite scheduling task.

[0125] The function value constructed based on the multi-dimensional index value and the weight value of the multi-dimensional index of the meteorological optimization satellite scheduling task can be the objective function shown in the above formula (3) to formula (9): The weighted sum of is shown in formula (10).

[0126] (10)

[0127] in, is the weight of each objective function, satisfying In this way, metrics such as coverage, overlap, cloud cover, and timeliness of the target area can be optimized across multiple dimensions, achieving the optimal trade-off and balance between different objectives. For example, by adjusting the weights, the optimization can be terminated when the weighted sum of the objective functions no longer significantly improves (e.g., the rate of change is <1%).

[0128] By comprehensively considering multi-dimensional goals such as coverage, overlap, meteorological factors (such as cloud cover), strip azimuth, and number of satellites, the diversification and optimization of task scheduling are achieved, enabling multiple satellites to quickly respond to task requirements under complex task constraints and provide efficient and feasible solutions. This overcomes the problems of long calculation time and multiple constraints faced by traditional planning methods, while taking into account the weather and cloud cover restrictions during task execution. While improving task execution efficiency, it ensures the feasibility and reliability of actual task execution and ensures that tasks can be efficiently executed under various constraints.

[0129] Optionally, in the large-area multi-satellite joint task scheduling, the satellite strips in the initially generated satellite scheduling task may be difficult to completely cover the target area or there may be overlapping problems, resulting in the failure to optimize the use of satellite resources. In order to address this problem, the stripe supplementation and optimization process can be used to supplement the blank areas and reduce overlaps. In one embodiment, the operation S240 described above may include the following process: traversing the satellite strips that are not called by the index-optimized satellite scheduling task, screening out the satellite strips that can cover the blank areas, and obtaining supplementary satellite strips; performing overlap detection on the intersection area of ​​the supplementary satellite strips and the satellite strips of the index-optimized satellite scheduling task, and adjusting the observation range of the supplementary satellite strips so that the area of ​​the intersection area reaches the minimum threshold; in response to the coverage rate of the blank area covered by the satellite strips reaching a predetermined coverage rate threshold, or there are no available satellite strips, generating a supplementary satellite scheduling task.

[0130] In some embodiments, the satellite strip scheduling in the initial satellite scheduling task can be obtained by a genetic algorithm, for example, as follows:

[0131] Initialize the population and generate initial stripes based on satellite orbit and field of view information. Crossover and mutation operations are performed to generate satellite strip planning solutions. Each solution is evaluated for fitness, using a fitness function that incorporates multiple optimization criteria, including coverage, overlap, strip azimuth, number of satellites, number of strips, mission execution time, and cloud cover. The optimal solution is retained through selection until convergence or the predetermined number of iterations is reached.

[0132] The satellite strips selected by the genetic algorithm can be used to obtain the initial satellite scheduling task. The blank areas not covered by the initial satellite scheduling task can be obtained by geometric extraction, which can include the following process:

[0133] The satellite strips selected by the genetic algorithm are combined with spatial data set calculation and topological analysis library to obtain polygonal representation of the satellite strip coverage area.

[0134] Using the difference operation, the uncovered areas of the satellite strips in the initial satellite scheduling mission on the target area are calculated. These uncovered areas are considered blank areas. The difference operation can be shown as formula (11).

[0135] (11)

[0136] in, Indicates a blank area. For the target area, The sub-area that has been covered.

[0137] A greedy algorithm is used to generate supplementary satellite strips, which are used to fill gaps. The remaining satellite time windows and satellite strips are traversed, and the strip that covers the gaps is selected based on the intersection of the satellite strips and the gaps. Based on the principle of local optimality, the greedy algorithm prioritizes satellite strips that maximize coverage of the gaps. Remaining satellites can be those not scheduled by the indicator optimization satellite scheduling task or those not scheduled by the initial satellite scheduling task.

[0138] The process of generating supplementary satellite strips can be as follows:

[0139] Filling blank areas: Based on the blank areas in the target area, appropriate satellite strips are selected to fill them. Each time a satellite strip is added, the orbit and field of view conditions that match the target area are given priority to ensure coverage efficiency.

[0140] Avoid overlap and resource waste: Overlap detection and adjustments are performed on the intersection of the supplementary satellite strips and existing satellite strips. Overlap is reduced by adjusting the observation range of the supplementary satellite strips, for example, by adjusting the satellite's side swing angle, minimizing the intersection area. That is, the area of ​​the intersection area reaches the minimum threshold, thereby improving satellite resource utilization. The intersection area of ​​the supplementary satellite strips and the satellite strips used for the index optimization satellite scheduling task can be shown in formula (12). The minimum threshold can be adaptively adjusted according to actual needs.

[0141] (12)

[0142] in, Indicates the overlapping area between the supplementary satellite strip and the existing satellite strip. For the newly added satellite strip, For existing satellite strips.

[0143] Consider meteorological factors: For areas with large cloud cover, it is recommended to use SAR satellites for shooting to avoid the situation where optical satellites are unable to form images due to the influence of cloud cover.

[0144] Termination Conditions and Solution Return: The process of determining additional satellite strips can terminate when certain conditions are met. For example, if all uncovered areas are effectively covered (i.e., the percentage of satellite strips covering the blank areas reaches a predetermined coverage threshold, which can be adjusted adaptively based on actual needs), or if there are no more available satellite strips. In this case, the currently available satellite strip solution can be returned.

[0145] The termination condition and the solution return step may include: if the blank area is empty or there are no more satellite strips to be supplemented, the task of generating the supplementary satellite strips is terminated, and the currently selected satellite strip set is returned as the supplementary satellite scheduling task.

[0146] In some embodiments, the supplementary satellite scheduling task can be optimized using the above-mentioned meteorological factor optimization and multi-dimensional indicator optimization process.

[0147] Optionally, the indicator optimization satellite scheduling task and the supplementary satellite scheduling task obtained by the above operation can be used to schedule the satellites in the satellite set. The scheduling process described in operation S250 may include the following operations: detecting the satellite strips in the indicator optimization satellite scheduling task and the satellite strips in the supplementary satellite scheduling task to obtain the detection results; in response to the presence of completely covered satellite strips in the detection results, removing the completely covered satellite strips to obtain the target satellite scheduling task; and using the target satellite scheduling task to schedule the satellite set.

[0148] The obtained indicator-optimized satellite scheduling tasks and supplementary satellite scheduling tasks may be further screened to ensure that the selected satellite strip set is optimal. The screening condition may be to ensure that no satellite strip is completely covered by other satellite strips except the selected satellite strip.

[0149] The steps for satellite strip screening and optimization are as follows:

[0150] Each satellite strip in the satellite strips in the index optimization satellite scheduling task and the satellite strips in the supplementary satellite scheduling task is detected, and it is determined whether there is a satellite strip that is completely covered by other satellite strips except the satellite strip.

[0151] If the current satellite strip is completely covered by other satellite strips, the covered satellite strips are removed, and the satellite strips that are not completely covered by other satellite strips are retained. In one example, if satellite strip A is completely covered by satellite strips B and C, satellite strip A can be removed, and the satellite strips that are not completely covered by satellite strips B and C are retained.

[0152] By detecting each satellite strip and eliminating completely covered satellite strips, a target satellite scheduling task consisting of a streamlined and effective set of satellite strips can be obtained. The target satellite scheduling task can be used to schedule satellites in the satellite set.

[0153] By combining a genetic algorithm with a greedy algorithm, the initial scheduling task is dynamically optimized based on meteorological factors and multi-dimensional indicators. The resulting satellite strips, after screening, are then combined to form the target satellite scheduling task for the scheduling satellite set. This target satellite scheduling task meets regional coverage requirements while minimizing overlap and resource waste.

[0154] By combining a genetic algorithm with a greedy algorithm, the genetic algorithm, as a global search algorithm, finds the global optimal solution or a near-optimal solution. The greedy algorithm, through local step-by-step optimization, rapidly adjusts the task allocation scheme to ensure that constraints are met to the greatest extent possible within each task schedule. The combined genetic and greedy algorithms determine the target satellite scheduling tasks, improving the computational efficiency of task scheduling and optimizing key factors such as task priority and resource utilization. This enhances the system's responsiveness to dynamic task changes and its emergency scheduling capabilities.

[0155] Figure 4 The following schematically shows a flow chart of a multi-satellite mission scheduling method according to another embodiment of the present invention.

[0156] like Figure 4 As shown, the method includes operations S410 to S450.

[0157] In operation S410, satellite orbit and satellite transit analysis is performed. For example, the target access window can be calculated by combining the satellite orbit prediction and the location of the ground target area to calculate the time window of the target area that the satellite can observe.

[0158] In operation S420, the target area is discretized and coverage analysis is performed. The target area mesh division and coverage analysis in operation S420 can refer to the process of dividing the target area using polygons of a predetermined size in operation S210.

[0159] In operation S430, meteorological factors are optimized using . The meteorological data analysis and threshold value verification in operation S430 may refer to operation S220.

[0160] In operation S440, optimization is performed using multi-dimensional indicators. The generation of the initial satellite scheduling task and the supplementary satellite scheduling task in operation S440 can refer to operations S230 to S240.

[0161] In operation S450, satellite strips are screened to obtain target satellite scheduling tasks. The process of removing satellite strips and outputting target satellite scheduling tasks in operation S450 can be referred to operation S250.

[0162] The multi-satellite mission scheduling method of this embodiment includes satellite orbit dynamics calculation, target access window analysis, regional grid discretization, coverage pattern generation algorithm, and meteorological factor optimization. It uses a multi-objective optimization algorithm to achieve optimal satellite strip planning. First, through satellite orbit prediction and access time analysis, the regional grid is divided and the coverage area of ​​each satellite is calculated. Next, a genetic algorithm is used to generate the initial satellite scheduling task, and the solution is optimized by considering factors such as regional coverage rate, overlap rate, and satellite yaw angle. Then, a greedy algorithm is used to supplement satellite strips in the blank areas to obtain the optimal satellite strip combination to ensure comprehensive coverage of the target area. Finally, the impact of meteorological factors on the initial satellite scheduling task is considered to optimize the coverage effect.

[0163] Starting with orbital dynamics calculation and prediction, the satellite orbit is calculated through a precise orbital dynamics model, and the satellite's orbital evolution is predicted based on the principles of celestial mechanics, providing key data for satellite access window analysis, thereby accurately evaluating the satellite's coverage capability of the target area.

[0164] Based on orbital predictions, the target area is spatially discretized into multiple grid cells. Satellite orbit data and field-of-view analysis are combined to quantify the coverage of each satellite. By calculating the coverage and overlap of each grid cell, a preliminary coverage matrix for the entire target area is generated, providing the necessary data support for multi-objective optimization.

[0165] A genetic algorithm is used to globally optimize large-area coverage solutions. It generates multiple candidate solutions through selection, crossover, and mutation. The fitness of these solutions is evaluated based on multi-dimensional objective functions such as coverage, overlap, and satellite roll angle, selecting the optimal satellite strip configuration to optimize the global coordination and configuration of satellite orbit strips. A greedy algorithm is used to fill in gaps. Based on the preliminary satellite strip configuration optimized by the genetic algorithm, the geometric intersection of the satellite strips and the gaps is calculated, and satellite strips are selected for supplementation to improve the coverage ratio of the target area and reduce resource overlap and waste.

[0166] Through geometric optimization strategies, completely overlapping redundant satellite strips are screened and removed, and the satellite strip combination is optimized to ensure the optimal configuration of satellite resources and comprehensive regional coverage, ultimately achieving efficient and accurate mission planning.

[0167] It should be noted that, unless it is clearly stated that there is a sequence of execution between different steps shown in the flowchart in the embodiment of the present invention, or there is a sequence of execution between different steps in technical implementation, otherwise, the execution order between multiple steps may not be prioritized, and multiple steps may also be executed simultaneously.

[0168] Based on the above multi-satellite task scheduling method, the present invention also provides a multi-satellite task scheduling device. Figure 5 The device is described in detail.

[0169] Figure 5 The following schematically shows a structural block diagram of a multi-satellite task scheduling device according to an embodiment of the present invention.

[0170] like Figure 5 As shown, the multi-satellite task scheduling device 500 of this embodiment includes a first generation module 510 , a filtering module 520 , an indicator optimization module 530 , a second generation module 540 and a scheduling module 550 .

[0171] The first generation module 510 is configured to generate an initial satellite scheduling task for the target area to be observed based on the coverage of sub-areas in the target area by satellite strips of the satellite set. The sub-areas are obtained by dividing the target area using polygons of a predetermined size. Sub-areas in the target area not covered by satellite strips are treated as blank areas. The initial satellite scheduling task includes multiple satellite sub-tasks, each of which is used to complete satellite observation of the sub-areas.

[0172] The filtering module 520 is configured to filter out satellite subtasks in the initial satellite scheduling task whose degree of influence by meteorological factors is greater than a predetermined threshold value by using meteorological factors, so as to obtain meteorologically optimized satellite scheduling tasks.

[0173] The indicator optimization module 530 is used to optimize the training of the meteorological optimization satellite scheduling task using multi-dimensional indicators until the function value constructed based on the multi-dimensional indicator value of the meteorological optimization satellite scheduling task and the weight value of the multi-dimensional indicator meets the predetermined indicator balance condition, thereby obtaining the indicator optimization satellite scheduling task.

[0174] The second generating module 540 is configured to generate a supplementary satellite scheduling task based on the intersection of the satellite strips in the satellite set that are not called by the indicator-optimized satellite scheduling task and the blank area;

[0175] The scheduling module 550 is used to optimize satellite scheduling tasks and supplement satellite scheduling tasks according to indicators, and schedule the satellite set.

[0176] According to an embodiment of the present invention, the target area is discretized by dividing it into polygons of predetermined size. An initial satellite scheduling task is generated based on the coverage of the sub-areas by satellite strips. The initial satellite scheduling task is optimized using meteorological factors to obtain a meteorologically optimized satellite scheduling task. The meteorologically optimized satellite scheduling task is optimized using multi-dimensional indicators to obtain an indicator-optimized satellite scheduling task. A supplementary satellite scheduling task is generated based on the intersection of the blank areas of the unused satellite strip domain in the satellite set. The satellite set is scheduled based on the indicator-optimized satellite scheduling task and the supplementary satellite scheduling task. Discretizing the target area provides a more precise basis for satellite scheduling, refines the satellite scheduling task, at least partially avoids duplicate scheduling of satellite resources, and improves satellite resource utilization and overall coverage of the target area. By performing multiple optimizations using meteorological factors and multi-dimensional indicators, the feasibility and reliability of the satellite scheduling task can be improved, thereby increasing the execution efficiency of the satellite scheduling task.

[0177] Optionally, the filtering module 520 may include a generating submodule and a filtering submodule.

[0178] The generation submodule is used to obtain the cloud cover data of the target area through the integrated meteorological interface service, and generate the cloud cover of the sub-area in the target area based on the satellite's transit time and the position of the sub-area.

[0179] The filtering submodule is used to filter out satellite subtasks whose cloud coverage exceeds a predetermined cloud coverage threshold in the initial satellite scheduling task to obtain a meteorological optimized satellite scheduling task.

[0180] Optionally, the filtering submodule may include a screening unit, a distribution unit and a regulating unit.

[0181] The screening unit is used to screen out sub-areas whose cloud coverage exceeds a predetermined cloud coverage threshold as sub-areas to be allocated.

[0182] The allocation unit is used to filter out the satellite subtasks used to observe the sub-area to be allocated in the initial satellite scheduling task, and schedule a backup observation system for the sub-area to be allocated.

[0183] The adjustment unit is used to adjust the weight value of the target area coverage of the initial satellite scheduling task and the weight value of the cloud cover influence until a predetermined meteorological balance condition is reached between the target area coverage and the cloud cover influence of the initial satellite scheduling task, thereby obtaining a meteorologically optimized satellite scheduling task.

[0184] Optionally, the indicator optimization module 530 may include a construction submodule, an input submodule, and an adjustment submodule.

[0185] Construct a submodule to construct the objective function of each dimension indicator.

[0186] The input submodule is used to input the dimensional index information of the meteorological optimization satellite scheduling task into the objective function and output the dimensional index value of the meteorological optimization satellite scheduling task.

[0187] The adjustment submodule is used to adjust the weight values ​​of the dimensional indicators until the dimensional indicator values ​​of the meteorological optimization satellite scheduling task meet the predetermined indicator balance conditions, thereby obtaining the indicator optimization satellite scheduling task.

[0188] Optionally, the input submodule may include a first input unit, a second input unit, a third input unit, a fourth input unit, a fifth input unit, a sixth input unit and a seventh input unit.

[0189] The first input unit is used to input the area of ​​the sub-region covered by the meteorological optimization satellite scheduling task and the total number of sub-regions in the target area into the coverage function and output the coverage index value.

[0190] The second input unit is used to input the overlapping area between every two sub-regions and the total number of sub-regions into the overlapping rate function, and output the overlapping rate index value.

[0191] The third input unit is used to input the number of satellite orbits and the azimuth of the satellite strip in the meteorological optimization satellite scheduling task into the azimuth function and output the azimuth index value.

[0192] The fourth input unit is used to input the binary function value of the satellite selected by the meteorological optimization satellite scheduling task into the satellite number function and output the satellite number index value.

[0193] The fifth input unit is used to input the binary function value of the satellite strip into the strip number function and output the satellite strip number index value.

[0194] The sixth input unit is used to input the execution time of the satellite orbit and the number of observation tasks in the meteorological optimization satellite scheduling task into the duration function and output the duration index value.

[0195] The seventh input unit is used to input the cloud coverage of the sub-region, the influence coefficient based on cloud amount, and the total number of sub-regions into the cloud coverage function, and output the cloud coverage index value.

[0196] Optionally, the second generation module 540 may include a first generation submodule, a traversal submodule, a first detection submodule and a second generation submodule.

[0197] The first generating submodule is configured to obtain a blank area according to a sub-area on the target area that is not covered by the satellite strip.

[0198] The traversal submodule is used to traverse the satellite strips that are not called by the indicator optimization satellite scheduling task, screen out the satellite strips that can cover the blank areas, and obtain supplementary satellite strips.

[0199] The first detection submodule is used to perform overlap detection on the intersection area of ​​the supplementary satellite strip and the satellite strip of the index optimization satellite scheduling task, and adjust the observation range of the supplementary satellite strip so that the area of ​​the intersection area reaches a minimum threshold.

[0200] The second generating submodule is configured to generate a supplementary satellite scheduling task in response to the coverage rate of the blank area covered by the satellite strips reaching a predetermined coverage rate threshold, or the absence of available satellite strips.

[0201] Optionally, the scheduling module 550 may include a second detection submodule, a removal submodule, and a scheduling submodule.

[0202] The second detection submodule is used to detect the satellite strips in the index optimization satellite scheduling task and the satellite strips in the supplementary satellite scheduling task to obtain detection results.

[0203] The removal submodule is configured to remove the completely covered satellite strip in response to the presence of the completely covered satellite strip in the detection result, and obtain the target satellite scheduling task.

[0204] The scheduling submodule is used to schedule the satellite set using the target satellite scheduling task.

[0205] According to embodiments of the present invention, any multiple modules among the first generation module 510, filtering module 520, metric optimization module 530, second generation module 540, and scheduling module 550 may be combined into a single module, or any one of these modules may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of other modules and implemented in a single module. According to embodiments of the present invention, at least one of the first generation module 510, filtering module 520, metric optimization module 530, second generation module 540, and scheduling module 550 may be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or may be implemented in hardware or firmware through any other reasonable means of circuit integration or packaging, or may be implemented in any one of software, hardware, and firmware, or any appropriate combination of these. Alternatively, at least one of the first generation module 510, the filtering module 520, the indicator optimization module 530, the second generation module 540 and the scheduling module 550 can be at least partially implemented as a computer program module, which can perform corresponding functions when executed.

[0206] Figure 6 A block diagram of an electronic device suitable for implementing a multi-satellite task scheduling method according to an embodiment of the present invention is schematically shown.

[0207] like Figure 6 As shown, an electronic device 600 according to an embodiment of the present invention includes a processor 601, which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM 602) or programs loaded from a storage unit 608 into a random access memory (RAM 603). Processor 601 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or related chipsets, and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)). Processor 601 may also include onboard memory for caching purposes. Processor 601 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0208] Various programs and data required for the operation of the electronic device 600 are stored in the RAM 603. The processor 601, ROM 602, and RAM 603 are connected to each other via a bus 604. The processor 601 executes the programs in the ROM 602 and / or RAM 603 to perform various operations according to the method flow of the embodiment of the present invention. It should be noted that the programs may also be stored in one or more memories other than the ROM 602 and RAM 603. The processor 601 may also execute the programs stored in the one or more memories to perform various operations according to the method flow of the embodiment of the present invention.

[0209] According to an embodiment of the present invention, electronic device 600 may further include an input / output (I / O) interface 605, which is also connected to bus 604. Electronic device 600 may also include one or more of the following components connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including devices such as a cathode ray tube (CRT), liquid crystal display (LCD), and speakers; a storage section 608 including a hard disk; and a communication section 609 including a network interface card such as a LAN card or modem. Communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. Removable media 611, such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed in drive 610 as needed, so that computer programs read from the removable media can be installed into storage section 608 as needed.

[0210] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of the present invention.

[0211] According to an embodiment of the present invention, a computer-readable storage medium may be a non-volatile computer-readable storage medium, and may include, but is not limited to, a portable computer disk, a hard disk, a random access memory (RAM 603), a read-only memory (ROM 602), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present invention, a computer-readable storage medium may include the ROM 602 and / or RAM 603 described above, and / or one or more memories other than ROM 602 and RAM 603.

[0212] The embodiments of the present invention further include a computer program product, which includes a computer program containing program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to cause the computer system to implement the method provided by the embodiments of the present invention.

[0213] The computer program executes the above functions defined in the system / device of the embodiment of the present invention when the computer program is executed by the processor 601. According to the embodiment of the present invention, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0214] In one embodiment, the computer program may be stored on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may be transmitted and distributed in the form of a signal on a network medium, downloaded and installed via the communication portion 609, and / or installed from a removable medium 611. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0215] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 609 and / or installed from a removable medium 611. When the computer program is executed by the processor 601, the above-described functions defined in the system of the embodiment of the present invention are performed. According to the embodiment of the present invention, the systems, devices, means, modules, units, etc. described above can be implemented by computer program modules.

[0216] According to an embodiment of the present invention, the program code for executing the computer program provided by the embodiment of the present invention can be written in any combination of one or more programming languages. Specifically, these computer programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect via the Internet).

[0217] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0218] It will be understood by those skilled in the art that the features described in the various embodiments of the present invention may be combined and / or coupled in various ways, even if such combinations or couplings are not explicitly described in the present invention. In particular, the features described in the various embodiments of the present invention may be combined and / or coupled in various ways without departing from the spirit and teachings of the present invention. All such combinations and / or couplings fall within the scope of the present invention.

[0219] The above describes embodiments of the present invention. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present invention. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be advantageously used in combination. Without departing from the scope of the present invention, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

Claims

1. A multi-satellite task scheduling method, characterized in that: The method comprises: generating an initial satellite scheduling task for a target area to be observed based on coverage of a sub-area within the target area to be observed by a satellite strip of a satellite set, wherein the sub-area is obtained by dividing the target area using polygons of a predetermined size, and sub-areas within the target area not covered by the satellite strip are defined as blank areas. The initial satellite scheduling task includes a plurality of satellite sub-tasks, each of which is used to complete satellite observation of the sub-areas; Using meteorological factors, satellite subtasks in the initial satellite scheduling task whose degree of influence by the meteorological factors is greater than a predetermined degree threshold are filtered out to obtain a meteorologically optimized satellite scheduling task; Optimizing and training the meteorological optimization satellite scheduling task using multi-dimensional indicators until a function value constructed based on the multi-dimensional indicator values ​​of the meteorological optimization satellite scheduling task and the weight values ​​of the multi-dimensional indicators satisfies a predetermined indicator balance condition, thereby obtaining an indicator-optimized satellite scheduling task; generating a supplementary satellite scheduling task according to the intersection of the satellite strip in the satellite set that is not called by the indicator-optimized satellite scheduling task and the blank area; Optimizing the satellite scheduling task and the supplementary satellite scheduling task according to the indicator, and scheduling the satellite set; Among them, the meteorological optimization satellite scheduling task is optimized and trained using multi-dimensional indicators until the function value constructed based on the multi-dimensional indicator value of the meteorological optimization satellite scheduling task and the weight value of the multi-dimensional indicator meets the predetermined indicator balance condition, and the indicator optimization satellite scheduling task is obtained: constructing the objective function of each dimensional indicator; inputting the dimensional indicator information of the meteorological optimization satellite scheduling task into the objective function, and outputting the dimensional indicator value of the meteorological optimization satellite scheduling task; adjusting the weight value of the dimensional indicator until the dimensional indicator values ​​of the meteorological optimization satellite scheduling task meet the predetermined indicator balance condition, and obtaining the indicator optimization satellite scheduling task.

2. The method according to claim 1, characterized in that The degree value affected by the meteorological factors includes cloud coverage; the predetermined degree threshold includes a predetermined cloud coverage threshold; The method of filtering out satellite subtasks in the initial satellite scheduling task whose degree of influence by the meteorological factors is greater than a predetermined threshold value by using meteorological factors to obtain a meteorologically optimized satellite scheduling task includes: Obtaining cloud cover data of the target area through an integrated meteorological interface service, and generating the cloud cover of the sub-area in the target area based on the satellite's transit time and the position of the sub-area; In the initial satellite scheduling task, the satellite subtasks whose cloud coverage exceeds the predetermined cloud coverage threshold are filtered out to obtain the meteorological optimization satellite scheduling task.

3. The method according to claim 2, characterized in that The step of filtering out satellite subtasks whose cloud coverage exceeds the predetermined cloud coverage threshold in the initial satellite scheduling task to obtain the meteorological optimization satellite scheduling task includes: Filtering out the sub-areas whose cloud coverage exceeds the predetermined cloud coverage threshold as the sub-areas to be allocated; filtering out satellite subtasks for observing the sub-area to be allocated in the initial satellite scheduling tasks, and scheduling a backup observation system for the sub-area to be allocated; and The weight value of the target area coverage of the initial satellite scheduling task and the weight value of the cloud cover influence are adjusted until a predetermined meteorological balance condition is reached between the target area coverage of the initial satellite scheduling task and the cloud cover influence, thereby obtaining the meteorologically optimized satellite scheduling task.

4. The method according to claim 1, wherein The dimensional indicators include at least one of the following: the coverage rate of the sub-area covered by the meteorological optimization satellite scheduling task, the overlap rate of the sub-area covered in the meteorological optimization satellite scheduling task, the azimuth of the satellite imaging strip, the number of satellites associated with the meteorological optimization satellite scheduling task, the number of imaging strips of the meteorological optimization satellite scheduling task, the execution time of the meteorological optimization satellite scheduling task, and the cloud coverage of the area observed by the meteorological optimization satellite scheduling task; The objective function includes at least one of the following: a coverage function used to describe the coverage rate of the meteorological optimization satellite scheduling task covering the sub-area, an overlap rate function used to describe the overlap rate of the meteorological optimization satellite scheduling task covering the sub-area, an azimuth function used to describe the azimuth of the satellite imaging strip, a satellite number function used to describe the number of satellites associated with the meteorological optimization satellite scheduling task, a strip number function used to describe the number of imaging strips of the meteorological optimization satellite scheduling task, a duration function used to describe the execution duration of the meteorological optimization satellite scheduling task, and a cloud coverage function used to describe the cloud coverage of the area observed by the meteorological optimization satellite scheduling task.

5. The method according to claim 4, characterized in that The step of inputting the dimension index information of the weather optimization satellite scheduling task into the objective function and outputting the dimension index value of the weather optimization satellite scheduling task includes: Inputting the area of ​​the sub-region covered by the weather optimization satellite scheduling task and the total number of the sub-regions in the target area into the coverage function, and outputting a coverage index value; Inputting the overlapping area between every two sub-regions and the total number of sub-regions into the overlapping rate function, and outputting an overlapping rate index value; Inputting the orbit number of the satellites in the weather optimization satellite scheduling task and the azimuth of the satellite strip into the azimuth function, and outputting an azimuth index value; Inputting the binary function value of the satellite selected by the meteorological optimization satellite scheduling task in the satellite set into the satellite number function, and outputting the satellite number index value; Inputting the binary function value of the satellite strip into the strip number function, and outputting the satellite strip number index value; Inputting the execution duration of the satellite's orbit and the number of observation tasks in the weather optimization satellite scheduling task into the duration function, and outputting a duration index value; The cloud coverage of the sub-region, the influence coefficient based on cloud cover, and the total number of the sub-regions are input into the cloud cover function, and a cloud cover index value is output.

6. The method according to claim 1, wherein The generating of the supplementary satellite scheduling task according to the intersection of the satellite strip in the satellite set that is not called by the indicator-optimized satellite scheduling task and the blank area includes: Traversing the satellite strips that are not called by the indicator optimization satellite scheduling task, screening out satellite strips that can cover the blank area, and obtaining supplementary satellite strips; Performing overlap detection on an intersection area between the supplementary satellite strip and the satellite strip of the indicator optimization satellite scheduling task, and adjusting the observation range of the supplementary satellite strip so that the area of ​​the intersection area reaches a minimum threshold; In response to the coverage rate of the blank area by the satellite strips reaching a predetermined coverage rate threshold, or the absence of available satellite strips, the supplementary satellite scheduling task is generated.

7. The method according to claim 1, characterized in that The optimizing the satellite scheduling task and the supplementary satellite scheduling task according to the indicator and scheduling the satellite set includes: Detecting the satellite strips in the indicator optimization satellite scheduling task and the satellite strips in the supplementary satellite scheduling task to obtain a detection result; In response to the presence of a completely covered satellite strip in the detection result, removing the completely covered satellite strip to obtain a target satellite scheduling task; The satellite set is scheduled using the target satellite scheduling task.

8. A multi-satellite task scheduling device, characterized in that: The device comprises: a first generating module, configured to generate an initial satellite scheduling task for a target area to be observed based on coverage of sub-areas in the target area by satellite strips of a satellite set, wherein the sub-areas are obtained by dividing the target area using polygons of a predetermined size, and sub-areas in the target area not covered by the satellite strips are defined as blank areas. The initial satellite scheduling task includes a plurality of satellite sub-tasks, each of which is configured to complete satellite observation of the sub-areas; a filtering module configured to filter out satellite subtasks in the initial satellite scheduling task whose degree of influence by the meteorological factors is greater than a predetermined threshold value by using meteorological factors, thereby obtaining a meteorologically optimized satellite scheduling task; An indicator optimization module is used to optimize and train the meteorological optimization satellite scheduling task using multi-dimensional indicators until a function value constructed based on the multi-dimensional indicator values ​​of the meteorological optimization satellite scheduling task and the weight values ​​of the multi-dimensional indicators satisfies a predetermined indicator balance condition, thereby obtaining an indicator optimization satellite scheduling task; A second generating module is configured to generate a supplementary satellite scheduling task based on an intersection of a satellite strip in the satellite set that is not called by the indicator-optimized satellite scheduling task and the blank area; a scheduling module, configured to optimize the satellite scheduling tasks and the supplementary satellite scheduling tasks according to the indicators, and schedule the satellite set; Among them, the indicator optimization module includes a construction submodule, an input submodule and an adjustment submodule; the construction submodule is used to construct the objective function of each dimensional indicator; the input submodule is used to input the dimensional indicator information of the meteorological optimization satellite scheduling task into the objective function, and output the dimensional indicator value of the meteorological optimization satellite scheduling task; the adjustment submodule is used to adjust the weight value of the dimensional indicator until the dimensional indicator values ​​of the meteorological optimization satellite scheduling task meet the predetermined indicator balance condition, and the indicator optimization satellite scheduling task is obtained.

9. An electronic device comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 7.

Images