GIS-based agile satellite region target observation meta-task generation method

Abstract

The invention relates to a GIS (Geographic Information System)-based agile satellite area target observation meta-task generation method, which comprises the following steps of: inputting sub-satellite point shp data of a satellite, filtering invisible points, and intersecting with shp data of an area to be observed to obtain an available satellite; generating preliminary visible windows according to continuous time grouping, and combining the windows according to satellite maneuvering time constraint; calculating the maximum visual distance of the satellite and generating a maximum visual area; extracting region boundary point data, and calculating a side swing angle range; dividing a to-be-observed area into grid center points, and generating a roll angle data set and candidate strips; and finally, optimizing and selecting an optimal strip and a side-sway angle through a task planning algorithm. According to the method, shp data is used as input, a geographic information system (GIS) technology is adopted to process a complex spatial relationship between a satellite visual range and a to-be-observed area, the spatial calculation efficiency and precision are remarkably improved, the solution space of a combined optimization problem is converted from infinity to finite, problem solving becomes possible, and the method is suitable for large-scale popularization and application. And a foundation is laid for satellite scheduling and planning scheme formulation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of multi-satellite mission planning, in particular to a GIS-based agile satellite regional target observation meta-task generation method. BACKGROUND

[0002] In the field of multi-satellite mission planning, especially in the agile satellite regional target observation meta-task generation method, a meta-task refers to an observation task that can be executed by a satellite during a single transit, which includes a time window attribute and corresponds to a satellite resource one by one. The regional observation task is a common task type in satellite remote sensing and is widely used in disaster emergency response, agricultural monitoring, urban planning and other fields. Due to the irregular shape and wide geographical range of the regional target, a single satellite is limited by its own ability (such as swath, side swing angle and field of view angle), and it is difficult to meet the observation requirements during a single transit, so it is necessary to use a meta-task generation method to divide the target to be observed into multiple meta-tasks and assign them to multiple satellites for collaborative coverage.

[0003] The camera carried by the agile satellite has a side swing capability, and the satellite covers different regions at different side swing angles. However, as a continuous variable, theoretically, a satellite can generate an infinite number of coverage strips, which leads to an infinite solution space of the mission planning problem, making it difficult to solve the combinatorial optimization problem. The existing technology relies on manual geometric calculation or simplified models when dealing with the complex spatial relationship between the satellite's visible range and the target region to be observed, which has the problems of low calculation efficiency and poor accuracy. Geographic Information System (GIS) has strong ability in dealing with spatial relationships, but it is rarely effectively applied in this field and cannot fully exert its spatial computing advantages.

[0004] Therefore, how to provide a method based on GIS technology to efficiently and accurately handle the complex spatial relationship between the satellite's visible range and the target region to be observed, and complete the solution of the mission planning algorithm, has become a technical problem to be solved in the field. SUMMARY

[0005] In view of the above technical problems, the present application provides a GIS-based agile satellite regional target observation meta-task generation method, which uses shp data as input data and uses geographic information system technology to process the complex spatial relationship between the satellite's visible range and the target region to be observed, improves the spatial computing efficiency and accuracy, and facilitates the solution of the mission planning algorithm.

[0006] The technical solution for solving the technical problem of the present application is: a GIS-based agile satellite regional target observation meta-task generation method, comprising the following steps:

[0007] Step S1, input all satellite ground point shp data within a certain time range, remove the satellite invisible ground points, and obtain new ground point shp data;

[0008] Step S2, intersecting the new sub-satellite point shp data and the to-be-observed region shp data to obtain available satellites and corresponding region visible sub-satellite point data;

[0009] Step S3, grouping the sub-satellite points according to continuous time to generate a preliminary visible window;

[0010] Step S4, merging the visible window according to the satellite maneuvering time constraint to form a final visible window;

[0011] Step S5, calculating the maximum visible distance of each satellite through satellite field of view angle data and side swing angle data;

[0012] Step S6, generating the maximum visible region of each visible window based on the final visible window data and the maximum visible distance of each satellite;

[0013] Step S7, generating region boundary point data according to the to-be-observed region shp data;

[0014] Step S8, generating the side swing angle range of each visible window of each satellite according to the maximum value and the minimum value of the distance between the visible region boundary point and the vertical line from the sub-satellite point to the sub-satellite point;

[0015] Step S9, dividing the to-be-observed region shp data into a plurality of grid center points;

[0016] Step S10, generating a side swing angle data set based on the grid center point and the side swing angle range data of each visible window, and generating a corresponding candidate strip;

[0017] Step S11, inputting the candidate strip and the grid center generated by the region division into a task planning algorithm to obtain the optimal candidate strip of each window of each satellite and the corresponding side swing angle.

[0018] According to one of the technical solutions of the application, in the step S3, the preliminary visible window is generated by grouping the sub-satellite points according to continuous time, which includes:

[0019] Step S31, sorting the time attribute of the sub-satellite point from small to large, and generating an ID after sorting;

[0020] Step S32, calculating the difference value of adjacent time points, and grouping the ID based on the difference value not being 1.

[0021] According to one of the technical solutions of the application, in the step S4, the final visible window is formed by merging the visible window according to the satellite maneuvering time constraint, which includes:

[0022] Step S41, extracting the start observation time of the preliminary visible window and sorting;

[0023] Step S42, calculate the difference of adjacent start time, and merge the window whose difference is greater than the satellite maneuvering time.

[0024] According to one of the technical solutions of the present application, in the step S5, the maximum visible distance of each satellite is calculated by the satellite field of view angle data and the side swing angle data, and the calculation formula is as follows:

[0025] Wherein, a is the field of view angle, b is the maximum side swing angle, R is the earth radius, is the constant pi.

[0026] According to one of the technical solutions of the present application, in the step S6, based on the final visible window data and the maximum visible distance of each satellite, the maximum visible area of each visible window is generated, including:

[0027] Step S61, read the latitude information of the start point of the final visible window and the next second of the start point, and subtract the latitude information of the next second of the start point from the latitude information of the start point, and the specific formula is as follows:

[0028]

[0029] Wherein, is the latitude information of the next second of the start point, is the latitude information of the start point;

[0030] Step S62, according to the value, determine the direction of generating buffer, and the specific formula is as follows:

[0031]

[0032] Step S63, according to the direction of generating buffer, combined with the maximum visible distance, generate the maximum visible area of each visible window.

[0033] According to one of the technical solutions of the present application, in the step S7, according to the to-be-observed area shp data, the area boundary point data is generated, including:

[0034] Step S71, convert the polygon data to line data;

[0035] Step S72, convert the line data to point data.

[0036] According to one of the technical solutions of the present application, in the step S8, according to the maximum and minimum values of the distance between the visible area boundary point and the vertical line from the sub-satellite point, the side swing angle range of each visible window of each satellite is generated, including:

[0037] Step S81, intersect the generated area boundary point data and the maximum visible area of each visible window to obtain the visible boundary point data;​

[0038] Step S82, calculate the vertical distance of each visible window visual boundary point data and the star point line, get the maximum and minimum value of the vertical distance;

[0039] Step S83, according to the maximum and minimum value of the vertical distance of each visible window visual point data distance star point line, calculate the preliminary yaw angle range of each visible window, according to the formula of vertical distance calculation yaw angle as follows:

[0040]

[0041] wherein, , is the vertical distance, R is the radius of the earth, H is the orbit height, is the corresponding earth central angle, is the ratio of circumference to diameter;

[0042] Step S84, the preliminary yaw angle range of each satellite each window and the yaw angle range of each satellite are intersected, and the final yaw angle range of each visible window is obtained.

[0043] According to one of the technical solutions of the application, in the step S9, the shp data of the to-be-observed region is divided into a plurality of grid center points, including:

[0044] Step S91, input the shp data of the to-be-observed region, and generate an H3 grid file;

[0045] Step S92, intersect the grid and the to-be-observed region, and output an attribute data table;

[0046] Step S93, generate the corresponding center point longitude and latitude according to the grid code of the attribute data table, and add them to the attribute data table;

[0047] Step S94, generate the grid center point shp data according to the grid code of the center point longitude and latitude.

[0048] According to one of the technical solutions of the application, in the step S10, based on the grid center point and the yaw angle range data of each visible window, a yaw angle data set is generated, and a corresponding candidate strip is generated, including:

[0049] Step S1001, intersect the grid center point shp data and the maximum visible region of each visible window, and obtain the visible grid center point data of each visible window;

[0050] Step S1002, calculate the vertical distance of each visible window visual grid center point data distance star point line and the corresponding yaw angle;

[0051] Step S1003, merging the side swing angle set generated by the visible grid center point of each visible window and the maximum and minimum side swing angle to generate the final side swing angle data set of each visible window;

[0052] Step S1004, generating the corresponding candidate strip according to the final side swing angle data set of each visible window.

[0053] According to one of the technical solutions of the application, in step S11, the meta-task set composed of candidate strips and grid centers generated by region segmentation is input into a task planning algorithm to obtain the optimal candidate strip of each window of each satellite and the corresponding side swing angle, including:

[0054] Step S1101, encoding the candidate strips in the window as a group;

[0055] Step S1102, inputting the grid center point data of the to-be-observed region and the grouped and encoded candidate strip data into a task planning algorithm;

[0056] Step S1103, taking the strip of each window of each satellite corresponding to the maximum yield value obtained by the task planning algorithm as the optimal strip, and obtaining the corresponding side swing angle, and the yield value is the number of grid center points covered by all strips.

[0057] Compared with the prior art, the application has the following beneficial effects:

[0058] The agile satellite regional target observation meta-task generation method based on GIS of the application processes the complex spatial relationship between the satellite foot point and the to-be-observed region by integrating the geographic information system (GIS) technology, and adopts a multi-step automated process including data input and filtering, spatial intersection analysis, time window generation and merging, maximum visible distance calculation, boundary point extraction, grid segmentation, side swing angle range determination, candidate strip generation and task planning optimization, thereby realizing efficient generation of agile satellite regional target observation meta-tasks.

[0059] The application takes shp data as input, adopts the geographic information system technology to process the complex spatial relationship between the satellite visible range and the to-be-observed region, and improves the spatial calculation efficiency and accuracy, thereby facilitating the solution of the task planning algorithm

[0060] The application adopts the regional target observation meta-task generation technology, converts the solution space of the combinatorial optimization problem from infinite to finite, makes the problem solution possible, lays a foundation for subsequent satellite scheduling and planning scheme, makes the combinatorial optimization problem solvable, and significantly improves the calculation efficiency and accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1A flowchart of a GIS-based agile satellite regional target observation element task generation method in an embodiment of the present application is shown in the figure.

[0062] Figure 2 A detailed flowchart of a GIS-based agile satellite regional target observation element task generation method in an embodiment of the present application is shown in the figure.

[0063] Figure 3 A flowchart of step S10 in an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0064] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments. Obviously, the drawings described in the following are only some of the embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0065] The present application will be described in detail below in combination with the drawings and specific embodiments. The embodiments cannot be described one by one here, but the embodiments of the present application are not limited to the following embodiments.

[0066] As shown in the figure, a GIS-based agile satellite regional target observation element task generation method of the present application comprises the following steps: Figures 1 to 3

[0067] Step S1, input all satellite subsatellite point shp data in a certain time range, remove the subsatellite points that are not visible to the satellite, and obtain new subsatellite point shp data.

[0068] The satellite subsatellite point refers to the subsatellite point of the satellite, which is the intersection of the satellite radial and the earth surface. Its trajectory is the projection of the satellite motion on the earth surface. The subsatellite point data is usually derived from the satellite orbit prediction system and contains time stamp, latitude and longitude coordinates, satellite identifier and other attributes.

[0069] In order to remove the subsatellite points that are not visible to the satellite, the subsatellite points are removed by limiting the range of solar elevation angle. Specifically, the subsatellite points with solar elevation angle less than 10° are removed, and new subsatellite point shp data is obtained.

[0070] Step S2, intersect the new subsatellite point shp data and the to-be-observed regional shp data to obtain available satellites and corresponding regional visible subsatellite point data.

[0071] The to-be-observed regional shp data is usually polygon vector data, which represents the geographical region to be observed.

[0072] ​Step S3, generating a preliminary visible window according to the continuous time point group, specifically including:

[0073] Step S31, sorting the time attribute of the subsatellite point from small to large, and generating the sorted ID;

[0074] Step S32, calculating the difference value of adjacent time points, and grouping based on the ID whose difference value is not 1;

[0075] First, sort the time attribute from small to large, add an ID attribute to generate the sorted ID; then add an attribute to store the value of the sorted attribute, which is the difference between the next one and the previous one; find the added attribute, whose value is not 1; group by ID, and output.

[0076] Step S4, merging the visible window according to the satellite maneuvering time constraint to form the final visible window, specifically including:

[0077] Step S41, extracting the start observation time of the preliminary visible window and sorting;

[0078] Step S42, calculating the difference value of adjacent start times, and merging the windows whose difference value is greater than the satellite maneuvering time.

[0079] First, extract the start observation time of the preliminary visible window and sort it from small to large; then add an attribute to store the difference value after sorting, which is the difference between the next start time and the previous start time; then find the start time whose difference value is greater than the satellite maneuvering time, and merge the corresponding window to form the final visible window.

[0080] Step S5, calculating the maximum visible distance of each satellite through satellite field of view angle data and side swing angle data, the formula is:

[0081]

[0082] Where a is the field of view angle, b is the maximum side swing angle, R is the radius of the earth, Pi is the ratio of the circumference to the diameter;

[0083] Step S6, based on the final visible window data and the maximum visible distance of each satellite, generating the maximum visible area of each visible window, including:

[0084] Step S61, reading the start point and the next second latitude information of the start point of the final visible window, and subtracting the start point latitude information from the next second latitude information of the start point, the specific formula is as follows:

[0085]

[0086] Where, is the next second latitude information of the start point, is the starting point latitude information;

[0087] Step S62, according to value to determine the direction of the buffer, as follows:

[0088]

[0089] Step S63, according to the direction of the generated buffer, combined with the maximum visible distance, to generate the maximum visible area of each visible window;

[0090] Step S7, according to the to-be-observed region shp data, generate region boundary point data, including:

[0091] Step S71, convert the polygon data to line data;

[0092] Step S71, convert the line data to point data;

[0093] Step S8, according to the maximum and minimum values of the vertical distance from the visible region boundary point to the vertical line of the subsatellite point, generate the yaw angle range of each visible window of each satellite, including:

[0094] Step S81, intersect the generated region boundary point data and the maximum visible area of each visible window to obtain the visible boundary point data;

[0095] Step S82, calculate the vertical distance from the visible boundary point data of each visible window to the subsatellite point, to obtain the maximum and minimum values of the vertical distance;

[0096] Step S83, according to the maximum and minimum values of the vertical distance from the visible point data of each visible window to the subsatellite point, calculate the preliminary yaw angle range of each visible window, according to the formula for calculating the yaw angle of the vertical distance as follows:

[0097]

[0098] wherein, , is the vertical distance, R is the radius of the earth, H is the orbit height, is the corresponding earth central angle, is the ratio of the circumference to the diameter;

[0099] Step S84, intersect the preliminary yaw angle range of each window of each satellite and the yaw angle range of each satellite to obtain the final yaw angle range of each visible window;

[0100] Step S9, divide the to-be-observed region shp data into a plurality of grid center points, including:

[0101] Step S91, input the to-be-observed region shp data, and generate an H3 grid file;

[0102] Step S92, intersect the grid and the to-be-observed region, and output an attribute data table;

[0103] Step S93, generate the corresponding central point longitude and latitude according to the grid code of the attribute data table, and add them to the attribute data table;

[0104] Step S94, generate the grid central point shp data according to the central point longitude and latitude of the grid code;

[0105] Step S10, generate a side swing angle data set based on the grid central point and the side swing angle range data of each visible window, and generate the corresponding candidate strip, as shown in Figure 3 , specifically including:

[0106] Step S1001, intersect the grid central point shp data and the maximum visible region of each visible window to obtain the visible grid central point data of each visible window;

[0107] Step S1002, calculate the vertical distance of the visible grid central point data of each visible window from the line connecting the nadir point and the corresponding side swing angle;

[0108] Step S1003, combine the side swing angle set generated by the visible grid central point of each visible window and the maximum and minimum side swing angle to generate the final side swing angle data set of each visible window;

[0109] Step S1004, generate the corresponding candidate strip according to the final side swing angle data set of each visible window;

[0110] Step S11, input the candidate strip and the grid central point generated by the region segmentation into the task planning algorithm to obtain the optimal candidate strip of each window of each satellite and the corresponding side swing angle, specifically including:

[0111] Step S1101, encode the candidate strip in each window as a group;

[0112] Step S1102, input the grid central point data of the to-be-observed region and the grouped and encoded candidate strip data into the task planning algorithm;

[0113] Step S1103, take the strip of each window of each satellite corresponding to the maximum benefit value obtained by the task planning algorithm as the optimal strip, and obtain the corresponding side swing angle, and the benefit value is the number of grid central points covered by all strips.

[0114] The task planning algorithm includes a genetic algorithm, a greedy algorithm or a dynamic programming algorithm, and is preferably a genetic algorithm.

[0115] According to an aspect of the present application, there is provided an electronic device comprising one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected with the memory, the one or more computer programs are stored in the memory, and when the electronic device is running, the processor executes the one or more computer programs stored in the memory, so that the electronic device executes a GIS-based agile satellite regional target observation element task generation method according to any one of the above technical solutions.

[0116] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0117] The memory can be an internal storage unit of the terminal device, for example, a hard disk or a memory of the terminal device. The memory can also be an external storage device of the terminal device, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory can include both the internal storage unit and the external storage device of the terminal device. The memory is used to store the computer programs and other programs and data required by the terminal device. The memory can also be used to temporarily store data that has been output or will be output.

[0118] According to an aspect of the present application, there is provided a computer readable storage medium for storing computer instructions, which, when executed by a processor, implement a GIS-based agile satellite regional target observation element task generation method according to any one of the above technical solutions.

[0119] For example, the computer readable storage media can be read-only memory (ROM), random access memory (RAM), compact disc-read only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device, etc. They can be implemented with program codes executable by the computing device, so that they can be stored in the storage device for execution by the computing device, or they can be made into individual integrated circuit modules, or a plurality of modules or steps in them can be made into a single integrated circuit module. Thus, the present application is not limited to any particular combination of hardware and software.

[0120] In summary, the method for generating a regional target observation element task of an agile satellite based on GIS can improve the calculation efficiency and accuracy by using a geographic information system to process the complex spatial relationship between the satellite visible range and the region to be observed, and can transform the solution space of the combinatorial optimization problem from infinite to finite by using the regional target observation element task generation technology, so that the problem solving becomes possible, and lays a foundation for subsequent satellite scheduling and planning scheme.

[0121] In addition, it should be noted that the embodiments of the present application can be in the form of computer program products implemented on one or more computer usable storage media containing computer usable program code.

[0122] The embodiments of the present application are described with reference to flowcharts and / or block diagrams according to the method, terminal device (system), and computer program product of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device produce an apparatus for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1 The apparatus for implementing the functions specified in one or more flows and / or blocks.

[0123] These computer program instructions can also be stored in a computer readable memory that can guide the computer or other programmable data processing terminal device to work in a specific way, so that the instructions stored in the computer readable memory produce a product including instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1The computer program instructions can also be loaded onto a computer or other programmable data processing terminal device to cause a series of operational steps to be performed on the computer or other programmable terminal device to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal device provide steps for implementing the flow Figure 1 The flow or flows and / or blocks Figure 1 The flow or flows and / or blocks

[0124] It is also to be noted that, as used in the specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a component" includes a plurality of such components and reference to "the element" is a reference to one or more elements and equivalents thereof.

[0125] Finally, it is to be understood that the above description is intended to be illustrative and not restrictive. Many other embodiments will be apparent to those of skill in the art upon reading the above description and the appended claims, and it is therefore intended that the application be understood to include all such embodiments as fall within the scope of the invention. Accordingly, the scope of the application should be determined by the appended claims and their legal equivalents rather than by the description of the preferred embodiment.

Claims

1. A GIS-based agile satellite regional target observation meta-task generation method, characterized in that, Includes the following steps: Step S1: Input the nadir point shape data of all satellites within a certain time range, remove the nadir points that are not visible to the satellites, and obtain new nadir point shape data; Step S2: Intersect the new nadir point shapefile data and the shapefile data of the area to be observed to obtain the available satellites and the corresponding visible nadir point data of the area; Step S3: Generate preliminary visible windows by grouping the nadir points according to continuous time. Step S4: Based on the satellite maneuvering time constraint, merge the visible windows to form the final visible window; Step S5: Calculate the maximum visible distance for each satellite using satellite field-of-view data and side-swing angle data; Step S6: Based on the final visible window data and the maximum visible distance of each satellite, generate the maximum visible area of ​​each visible window; Step S7: Generate region boundary point data based on the shapefile data of the region to be observed; Step S8: Based on the maximum and minimum values ​​of the vertical line connecting the point falling on the boundary of the visible area to the nadir point, generate the lateral tilt angle range for each visible window of each satellite; Step S9: Divide the shapefile data of the area to be observed into multiple grid center points; Step S10: Based on the grid center point and the side swing angle range data of each visible window, generate a side swing angle dataset and generate corresponding candidate strips; Step S11: Input the meta-task set composed of candidate strips and grid centers generated by region segmentation into the task planning algorithm to obtain the optimal candidate strip and corresponding side angle for each window of each satellite.

2. The method according to claim 1, characterized in that, In step S3, generating a preliminary visible window by grouping the nadir points according to continuous time includes: Step S31: Sort the time attributes of the star points from smallest to largest, and generate the sorted IDs; Step S32: Calculate the difference between adjacent time points and group them based on the IDs whose difference is not 1.

3. The method according to claim 1, characterized in that, In step S4, merging the visible windows to form the final visible window according to the satellite maneuvering time constraint includes: Step S41: Extract and sort the start observation times of the preliminary visible windows; Step S42: Calculate the difference between adjacent start times and merge windows with a difference greater than the satellite maneuver time.

4. The method according to claim 1, characterized in that, In step S5, the formula for calculating the maximum visible distance of each satellite using satellite field-of-view data and side-swing angle data is as follows: Where a is the field of view angle, b is the maximum side sway angle, and R is the Earth's radius. Pi is the mathematical constant of a circle.

5. The method according to claim 1, characterized in that, In step S6, generating the maximum visible area of ​​each visible window based on the final visible window data and the maximum visible distance of each satellite includes: Step S61: Read the starting point and latitude information of the final visible window in the next second, and subtract the starting point latitude information from the latitude information of the next second from the starting point. The specific formula is as follows: in, It is the latitude information of the starting point in the next second. It is the latitude information of the starting point; Step S62, according to The value determines the direction of buffer generation, as follows: Step S63: Based on the direction of the generated buffer and the maximum viewing distance, generate the maximum visible area of ​​each visible window.

6. The method according to claim 1, characterized in that, In step S7, generating region boundary point data based on the shapefile data of the region to be observed includes: Step S71: Convert polygon data into line data; Step S72: Convert line data to point data.

7. The method according to claim 1, characterized in that, In step S8, the lateral tilt angle range for each visible window of each satellite is generated based on the maximum and minimum values ​​of the vertical line connecting the boundary point of the visible area to the nadir point, including: Step S81: Intersect the generated region boundary point data with the maximum visible area of ​​each visible window to obtain the visible boundary point data; Step S82: Calculate the vertical distance between the visible boundary point data of each visible window and the line connecting the nadir point, and obtain the maximum and minimum values ​​of the vertical distance; Step S83: Based on the maximum and minimum vertical distances from the visible point data of each visible window to the nadir point, calculate the initial lateral tilt angle range for each visible window. The formula for calculating the lateral tilt angle based on the vertical distance is as follows: in, , R is the vertical distance, R is the Earth's radius, and H is the orbital altitude. It corresponds to the central angle of the Earth. It is pi; Step S84: Intersect the initial lateral tilt angle range of each window for each satellite with the lateral tilt angle range of each satellite to obtain the final lateral tilt angle range of each visible window.

8. The method according to claim 1, characterized in that, In step S9, dividing the shapefile data of the area to be observed into multiple grid center points includes: Step S91: Input shapefile data of the area to be observed and generate H3 grid file; Step S92: Intersect the grid and the area to be observed, and output the attribute data table; Step S93: Generate the corresponding center point latitude and longitude according to the grid code of the attribute data table, and add it to the attribute data table; Step S94: Generate grid center point shape data based on the latitude and longitude of the grid encoding center point.

9. The method according to claim 1, characterized in that, In step S10, based on the grid center point and the side swing angle range data of each visible window, a side swing angle dataset is generated, and corresponding candidate strips are generated, including: Step S1001: Intersect the grid center point shapep data with the maximum visible area of ​​each visible window to obtain the visible grid center point data of each visible window; Step S1002: Calculate the vertical distance from the center point of the visible grid of each visible window to the line connecting the nadir point and the corresponding lateral angle; Step S1003: Merge the set of side swing angles generated by the center point of the visible grid of each visible window with the maximum and minimum side swing angles to generate the final side swing angle dataset for each visible window; Step S1004: Generate corresponding candidate strips based on the final side angle dataset of each visible window.

10. The method according to claim 1, characterized in that, In step S11, the meta-task set composed of candidate strips and grid centers generated by region segmentation is input into the task planning algorithm to obtain the optimal candidate strip and corresponding side angle for each window of each satellite, including: Step S1101: Group the candidate stripes of each window into a group and encode the candidate stripes in the window; Step S1102: Input the grid center point data of the area to be observed and the grouped and coded candidate strip data into the task planning algorithm; Step S1103: Take the stripe of each satellite in each window corresponding to the maximum benefit value obtained by the mission planning algorithm as the optimal stripe, and obtain the corresponding side sway angle. The benefit value is the number of grid center points covered by all stripes.

Images