Giant constellation time window rapid prediction method based on trajectory space-time segmentation method

Abstract

The invention discloses a giant constellation time window rapid prediction method based on a trajectory space-time segmentation method, and aims to solve the technical problems that the calculation amount of a satellite time window is large and the task requirement of a giant satellite base for rapid response in the future is difficult to meet in the existing giant satellite base management and control process. According to the technical scheme provided by the invention, the giant constellation time window rapid prediction method based on the track space-time segmentation method comprises the following steps: acquiring track data and preset parameters of a target constellation; a step of performing grid segmentation on the map; a step of dividing the track data; according to the segmented orbit data, obtaining a time window, meeting a preset task requirement, of each satellite in a forecasting time period; and summarizing the time windows, meeting the preset task requirements, of the satellites in the specific time period, and obtaining basis data of task planning. The method is suitable for rapid prediction of the giant constellation time window.

Description

Technical Field

[0001] The invention relates to the technical field of orbital data preprocessing in the field of giant constellation task scheduling, and in particular to a fast prediction of giant constellation time windows based on a trajectory time-space segmentation method. Background Art

[0002] One of the difficulties faced by efficient control technology of giant constellations is the control of large-scale orbit prediction data and the calculation of visible time windows for massive tasks. The tense space situation in the future will also place higher demands on the response speed of missions.

[0003] The existing satellite time window calculation in the Giant Star constellation control process is large, which is difficult to meet the future Giant Star constellation's mission requirements for rapid response. Combined with the actual engineering application, considering that the satellite control system in actual applications will inevitably perform orbit prediction, therefore, the orbit prediction data preprocessing technology can be used to improve the mission planning response speed after the mission enters the system.

[0004] Based on the requirements of giant constellations for large-scale tasks and rapid responses, a fast visible time window calculation method is urgently needed to provide strong support for the rapid response of massive time-sensitive tasks and ensure the operating efficiency of giant constellations. Summary of the invention

[0005] In order to solve the technical problem in the prior art that the satellite time window calculation amount in the existing giant star constellation control process is large and it is difficult to meet the future giant star constellation's mission requirements for rapid response, the technical solution provided by the present invention is:

[0006] A method for rapid prediction of giant constellation time windows based on trajectory time-space segmentation method, comprising:

[0007] The step of collecting orbital data and preset parameters of the target constellation;

[0008] The step of gridding the map according to the visibility of all satellites in the target constellation;

[0009] The step of segmenting the orbit prediction data according to the longitude and latitude of the sub-satellite point of the satellite and the divided map grids, and storing the satellite tracks whose sub-satellite points are located in the same grid into the satellite orbit data set corresponding to the grid;

[0010] The steps of designing and selecting a selection strategy for the search regions of the grids located at different locations;

[0011] The step of summarizing the time windows of each satellite that meet the preset mission requirements within the forecast period and obtaining the basis data for mission planning.

[0012] Furthermore, a preferred embodiment is provided, wherein the preset parameters include satellite orbit parameters, payload parameters, and platform maneuverability parameters, wherein the satellite orbit parameters include orbit semi-major axis, eccentricity, inclination, right ascension of ascending node, perigee angular distance, and mean perigee angle, the payload parameters include field of view semi-cone angle, and the platform maneuverability parameters include satellite maximum roll angle, pitch angle, and yaw angle.

[0013] Furthermore, a preferred implementation is provided, in which the orbital data is segmented according to time and space, and a trajectory of a satellite covering a specific area of ​​the earth in a specific time period is generated to obtain the segmented orbital data.

[0014] Furthermore, a preferred embodiment is provided, wherein the preset tasks include a ground imaging task and a ground data transmission task.

[0015] Based on the same inventive concept, the present invention also provides a giant constellation time window rapid prediction device based on trajectory time-space segmentation method, comprising:

[0016] A module for collecting orbital data and preset parameters of the target constellation;

[0017] A module for gridding maps;

[0018] A module for segmenting the track data;

[0019] A module for obtaining a time window for each satellite to meet preset mission requirements within a forecast period according to the segmented orbital data;

[0020] A module that summarizes the time windows of each satellite that meet the preset mission requirements within a specific time period and obtains the basis data for mission planning.

[0021] Based on the same inventive concept, the present invention also provides a giant constellation task preprocessing method, including preprocessing such as screening, classification, partitioning, and fusion of tasks according to the obtained giant constellation grid segmentation results, obtaining a processed task set, and guiding the scheduling of target constellation tasks.

[0022] Based on the same inventive concept, the present invention also provides a giant constellation task preprocessing device, including a module for preprocessing tasks such as screening, classification, partitioning, and fusion according to the obtained giant constellation grid segmentation results, obtaining a processed task set, and guiding the scheduling of target constellation tasks.

[0023] Based on the same inventive concept, the present invention also provides a computer storage medium for storing a computer program, and when the computer reads the computer program, the computer executes the described method.

[0024] Based on the same inventive concept, the present invention also provides a computer, including a processor and a storage medium. When the processor reads a computer program stored in the storage medium, the computer executes the method described.

[0025] Based on the same inventive concept, the present invention also provides a computer program product, which is a computer program. When the computer program is executed, the method described above is implemented.

[0026] Compared with the prior art, the technical solution provided by the present invention is beneficial in that:

[0027] The invention provides a method for rapid prediction of giant constellation time window based on trajectory space-time segmentation method, which segments the trajectory by space grid and orbital circle, and stores the segmented trajectory in the corresponding grid. This step can be directly used for the storage of orbit prediction data during orbit prediction, which will effectively improve the efficiency of giant constellation mission planning preprocessing.

[0028] The invention provides a method for rapid prediction of giant constellation time window based on trajectory time-space segmentation method, and proposes a virtual three-dimensional coordinate, wherein the coordinate X-axis is the row where the grid is divided according to longitude, the Y-axis is the column where the grid is divided according to latitude, and the Z-axis is the circle where the "time-space fragment" of the trajectory fragment is located. This method can greatly improve the retrieval efficiency of orbit prediction data and reduce the search space during mission planning preprocessing.

[0029] The invention provides a method for rapid prediction of giant constellation time window based on trajectory space-time segmentation method, which segments the trajectory by space grid and orbital circle, and stores the segmented trajectory in the corresponding grid. This step can be directly used for the storage of orbit prediction data during orbit prediction, which will effectively improve the efficiency of giant constellation mission planning preprocessing.

[0030] The invention provides a method for fast prediction of giant constellation time window based on trajectory time-space segmentation, which improves the efficiency of time window calculation by trajectory time-space segmentation. Compared with the traditional method, this method avoids repeated calculation of global data, and the segmented trajectory data makes the calculation more localized and parallelized, thereby greatly shortening the calculation time. This effect mainly comes from the data simplification and parallel processing capabilities in the time-space segmentation process.

[0031] The invention provides a method for rapid prediction of giant constellation time windows based on trajectory time-space segmentation. In the calculation process of the time window, segmented trajectory data is used for accurate window calculation, avoiding the processing of invalid time periods. This refined calculation method significantly improves the accuracy of the time window and effectively reduces errors. This effect mainly comes from the precise analysis of segmented data in the time window calculation stage.

[0032] The invention provides a method for rapid prediction of giant constellation time windows based on trajectory time-space segmentation, which significantly improves the speed and accuracy of calculation when processing giant constellation data, and provides a faster response speed for time window calculation of time-sensitive tasks. Traditional methods are often limited by large data volume and high computational complexity, while this solution simplifies complex problems into multiple small problems through innovative time-space segmentation methods, thereby improving the scalability and applicability of the algorithm as a whole.

[0033] The invention provides a method for rapid prediction of giant constellation time windows based on a trajectory time-space segmentation method, which is suitable for application in the work of rapid prediction of giant constellation time windows. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a schematic diagram of satellite visibility;

[0035] Figure 2 This is a schematic diagram of the network partitioning results;

[0036] Figure 3 It is a schematic diagram of the three-dimensional virtual coordinates of the satellite trajectory space-time debris;

[0037] Figure 4 Schematic diagram of the space-time fragmentation of a satellite orbit;

[0038] Figure 5 Select Schematic for Search Area. DETAILED DESCRIPTION

[0039] In order to make the advantages and benefits of the technical solution provided by the present invention more clearly reflected, the technical solution provided by the present invention is now further described in detail with reference to the accompanying drawings, specifically:

[0040] Embodiment 1: This embodiment provides a method for quickly predicting a giant constellation time window based on a trajectory time-space segmentation method, including:

[0041] The step of collecting orbital data and preset parameters of the target constellation;

[0042] The step of gridding the map according to the visibility of all satellites in the target constellation;

[0043] The step of segmenting the orbit prediction data according to the longitude and latitude of the sub-satellite point of the satellite and the divided map grids, and storing the satellite tracks whose sub-satellite points are located in the same grid into the satellite orbit data set corresponding to the grid;

[0044] The steps of designing and selecting a selection strategy for the search regions of the grids located at different locations;

[0045] The step of obtaining a time window for each satellite to meet a preset mission requirement within a forecast period according to the segmented orbital data;

[0046] The step of summarizing the time windows of each satellite that meet the preset mission requirements within a specific time period and obtaining the basis data for mission planning.

[0047] Specifically, they include:

[0048] Data preparation: First, obtain the orbital data and preset parameters of the giant constellation. The orbital prediction data of the giant constellation includes the satellite's position, speed, sub-satellite longitude, and sub-satellite latitude. The preset parameters include satellite orbit parameters, payload parameters, and platform maneuverability parameters. The satellite orbit parameters include the orbital semi-major axis, eccentricity, inclination, right ascension of the ascending node, perigee angular distance, and mean anomaly angle. The payload parameters include the semi-cone angle of the field of view. The platform maneuverability parameters include the satellite's maximum roll angle, pitch angle, and yaw angle.

[0049] Trajectory spatiotemporal segmentation: The orbital data is segmented in time and space to obtain trajectory segments of the satellite at different time and space positions.

[0050] Time window calculation: Based on the results of time and space segmentation, calculate the time window that meets the task requirements.

[0051] Result output: The final output can meet the time window required by the task, which is convenient for subsequent task planning and decision-making.

[0052] The detailed steps are:

[0053] Data preparation

[0054] Input: orbit prediction data of the giant star constellation, including the satellite's position, speed, sub-satellite longitude and sub-satellite latitude; orbit parameters of the giant constellation, including the six basic parameters of the satellite orbit (orbital semi-major axis, eccentricity, inclination, right ascension of the ascending node, perigee angular distance and mean anomaly angle); satellite payload parameters, namely the semi-cone angle of the field of view; platform maneuverability parameters, including the satellite's maximum roll angle, pitch angle and yaw angle.

[0055] Output: Provides basic orbital data for trajectory spatiotemporal segmentation and time window calculation for map grid width.

[0056] Trajectory spatiotemporal segmentation

[0057] Input: Prepared orbital data.

[0058] Process: The orbital data is divided into time and space to generate a set of satellite orbital data stored in different grid coordinates.

[0059] Output: After segmentation, the trajectory data set Set is stored in grid coordinates.i,j ={piece1,piece2,...,piece n}, Set i,j Represents the set of fragments corresponding to the grid in the i-th row and j-th column.

[0060] Time window calculation

[0061] Input: segmented trajectory data.

[0062] Process: Analyze trajectory data and calculate the time window for each satellite to meet mission requirements within a specific time period, such as ground imaging missions, ground data transmission missions, etc.

[0063] Output: A list of time windows that meet the task requirements.

[0064] Result Output

[0065] Input: List of calculated time windows.

[0066] Process: Summarize all time windows and generate the basis for task planning and decision-making.

[0067] Output: The final time window result, which is used to guide task execution.

[0068] This process greatly speeds up the calculation of giant constellation mission time windows through space-time segmentation and efficient calculation, and improves the efficiency of mission planning.

[0069] Implementation method 2: This implementation method is a further limitation of the giant constellation time window rapid prediction method based on the trajectory time-space segmentation method provided in implementation method 1, and the preset parameters include orbit semi-major axis, eccentricity, inclination, ascending node right ascension, perigee angular distance and mean anomaly angle.

[0070] Implementation method three: This implementation method is a further limitation of the giant constellation time window rapid prediction method based on trajectory time-space segmentation method provided in implementation method one. The orbital data is segmented according to time and space, and the trajectory of the satellite covering a specific area of ​​the earth in a specific time period is generated to obtain the segmented orbital data.

[0071] Embodiment 4: This embodiment further limits the giant constellation time window rapid prediction method based on trajectory time-space segmentation method provided in embodiment 1, and the preset tasks include observation tasks and communication tasks.

[0072] Embodiment 5: This embodiment provides a giant constellation time window rapid prediction device based on trajectory time-space segmentation method, including:

[0073] A module for collecting orbital data and preset parameters of the target constellation;

[0074] A module for gridding a map; a module for segmenting the track data;

[0075] A module for obtaining a time window for each satellite to meet preset mission requirements within a forecast period according to the segmented orbital data;

[0076] A module that summarizes the time windows of each satellite that meet the preset mission requirements within a specific time period and obtains the basis data for mission planning.

[0077] Implementation method six: This implementation method provides a giant constellation task preprocessing method, including preprocessing tasks such as screening, classification, partitioning, and fusion according to the giant constellation grid segmentation result obtained in implementation method one, obtaining a processed task set, and performing task guidance on the target constellation task scheduling.

[0078] Implementation method seven: This implementation method provides a giant constellation task preprocessing device, including a module for preprocessing tasks such as screening, classification, partitioning, and fusion according to the giant constellation grid segmentation result obtained in implementation method five, obtaining a processed task set, and guiding the scheduling of target constellation tasks.

[0079] Embodiment 8: This embodiment provides a computer storage medium for storing a computer program. When the computer reads the computer program, the computer executes the method provided in embodiment 1.

[0080] Embodiment 9: This embodiment provides a computer, including a processor and a storage medium. When the processor reads a computer program stored in the storage medium, the computer executes the method provided in embodiment 1.

[0081] Embodiment 10: This embodiment provides a computer program product, which is a computer program. When the computer program is executed, the method provided in embodiment 1 is implemented.

[0082] Implementation Method XI: Combination Figure 1-5 This embodiment is described in detail by using specific examples to further describe the technical solution provided above. Specifically:

[0083] In view of the large amount of satellite time window calculation in the process of Giant Star Constellation control, which is difficult to meet the future Giant Star Constellation's task requirements for rapid response, this implementation method proposes a Giant Star Constellation control preprocessing method. The orbital prediction data of the Giant Star Constellation is preprocessed by the time-space trajectory segmentation method, so that rapid time window calculation of massive targets in the Giant Star Constellation can be achieved within the forecast period.

[0084] In order to quickly predict the time window of the target and reduce the amount of calculation of the time window by the traditional traversal method, this implementation method divides the map into grids, and then divides the satellite trajectory according to the divided grids and the sub-satellite points of the satellite, and finally obtains the space-time fragments of the satellite trajectory. Each space-time fragment of the trajectory has a three-dimensional identifier, which supports the search of the time window in the local area, avoids the search of all forecast points, and greatly improves the calculation efficiency of the time window.

[0085] The trajectory preprocessing method designed in this implementation is advanced in that, under the conditions that the semi-major axis of the satellite orbit, the satellite attitude maneuverability, and the satellite field of view remain unchanged, the grid side length determination, map area segmentation, and search area selection only need to be performed once at the beginning of the scene setting, and the subsequent constellation system control only needs to perform trajectory segmentation and visibility calculation on the newly predicted satellite trajectory. At the same time, for one orbit prediction cycle, the satellite trajectory segmentation only needs to be performed once, thereby greatly reducing the amount of calculation in the giant star constellation control process.

[0086] The mathematical symbols involved in the algorithm are as follows:

[0087] S={S1,S1,...,S Ns} represents the satellite set, Ns represents the number of satellites;

[0088] T={T1,T1,...,T Nt} represents the target set, Nt represents the number of target points;

[0089] LLA={LLA1,LLA2,...,LLA Ns} represents the orbit prediction sub-satellite point data of all satellites, where

[0090] represents the number of orbit prediction points of satellite i;

[0091] PV={PV1,PV2,...,PV S} represents the orbit prediction satellite position and velocity data of all satellites, where

[0092]

[0093] Δdelta represents the length of the final grid division and the longitude and latitude spanned by each grid. This method will design the grid

[0094] is a square with the same span of longitude and latitude;

[0095] N row Indicates that when dividing the reference grid, the two-dimensional map is divided into N equal parts along the longitude direction. row List,

[0096]

[0097] N column Indicates that when dividing the reference grid, the two-dimensional map is divided into N equal parts along the latitude direction. column OK,

[0098]

[0099] M represents the number of regions obtained by segmenting the map area, that is, the number of regions obtained after dividing the reference grid into neighborhoods.

[0100] Quantity, M = N row ×N column ;

[0101] Represents the set of all fragments;

[0102] Set i,j ={piece1,piece2,...,piece n} represents the set of fragments corresponding to the grid at row i and column j;

[0103] piece i = {N s ,t s ,t e} represents the parameters corresponding to each track fragment, where Ns represents the satellite number, ts represents the time corresponding to the starting point of the track fragment, and t e Indicates the time corresponding to the end point of the trajectory fragment;

[0104] Indicates satellite S i To target T j All visible time windows of |W ij | indicates satellite S i To target T j The number of all visible time windows.

[0105] W ijk = {b ijk ,e ijk} represents satellite S i To target T j The visible time window of the kth track, b ijk 、e ijk Respectively represent the start time and end time of the visible time window;

[0106] The steps of rapid prediction of giant constellation time window based on trajectory space-time segmentation method are as follows:

[0107] Step 1: Determine the grid side length. According to the attitude maneuverability and field of view of all satellites in the constellation, determine the maximum longitude / latitude span of their field of view on the surface, and then select the maximum value as the grid side length for grid division.

[0108] Step 2: Map area segmentation: divide the 2D unfolded plane of the earth into grids with the above side lengths and construct a virtual coordinate system;

[0109] Step 3: Search area selection: design a search area selection strategy to determine the grids where the trajectory fragments to be searched for targets in different grid positions are located when performing visible window calculations;

[0110] Step 4: Satellite track segmentation: traverse all sub-satellite point tracks of all satellites, and according to the grid coordinates of the sub-satellite point latitude and longitude, take the continuous track points of a satellite track in the same grid as a track fragment, and mark them with grid coordinates and the number of circles of the fragments. At the same time, organize all track fragments of all satellites in the same area into the track fragment set of the grid;

[0111] Step 5: Visibility calculation, traverse all satellite tracks in the search area, calculate the geocentric angle between the satellite and the target at the time corresponding to all fragments, find all orbital prediction points that meet visibility, sort and integrate all visible moments, and then all time windows can be obtained. At the same time, all geocentric angles can be recorded. The moment corresponding to the minimum geocentric angle of each time window is the middle moment of the time window. At this time, for the current time window, the distance between the satellite sub-satellite point and the target is the shortest, which is usually the moment with the highest imaging quality in the current time window.

[0112] Next, each of the above steps is described in detail.

[0113] First, calculate the side length of the grid. Assuming that the satellite does not perform orbital maneuvers during its mission, the satellite's field of view is determined by the field of view of the satellite payload and the satellite's attitude maneuvering angle. The satellite field of view diagram is shown in the figure below. Figure 1 This implementation method is mainly aimed at satellites with a cone-shaped field of view. The maximum field of view semi-cone angle θ can be obtained by combining the field of view angle of the satellite payload with the attitude maneuvering angle. v , calculate the geocentric angle corresponding to the satellite field of view at the equator. If the earth is assumed to be a sphere, the longitude ΔLon and latitude ΔLat of the satellite field of view at the equator can be obtained. For the conical field of view, ΔLon and ΔLat are equal, so Δdelta is used to represent them uniformly. Since the projection of the satellite field of view on the ground increases with the increase of the satellite orbit altitude, the orbit altitude at the apogee is selected, that is, the semi-major axis a is used as R S Calculate ΔLon.

[0114] First calculate the field of view half cone angle θ v The corresponding geocentric angle γ is:

[0115]

[0116] Then the latitude and longitude span Δdelta corresponding to the geocentric angle γ can be expressed as:

[0117] Δdelta=γ

[0118] Assume that there are Ns satellites in the Giant Star constellation. The above method can be used to obtain the Δdelta of all satellites in the Giant Star constellation, that is, ΔDelta = {Δdelta1, Δdelta2, ..., Δdelta Ns}.

[0119] In order to uniformly manage the constellation, this implementation will use a unified grid to divide the forecast tracks of all satellites in the constellation, so the side length used for grid division is:

[0120] Δdelta=max{Δdelta1,Δdelta2,...,Δdelta Ns}

[0121] After calculating the grid side length, the map area is segmented. Expand the earth in two dimensions and divide the map into grids according to Δdelta. The schematic diagram of the divided map grid is as follows: Figure 2 and 3 Construct a virtual three-dimensional coordinate, where the X-axis is the row of the grid divided according to longitude, the Y-axis is the column of the grid divided according to latitude, and the Z-axis is the circle of the "time-space fragment" where the trajectory fragment is located.

[0122] The satellite trajectory segmentation process needs to traverse all the sub-satellite points in the current orbit prediction period of all satellites in the giant star constellation, calculate the grid coordinates of the sub-satellite points according to the longitude and latitude of the sub-satellite points, and set the orbit prediction point of satellite i The x and y coordinates in the virtual coordinate system at that moment can be calculated by the following formula, and the z coordinate can be directly recorded according to the circle number at the current moment.

[0123]

[0124] The continuous trajectory points of a satellite trajectory in the same grid are regarded as a trajectory fragment and marked with virtual coordinates (x, y, z). At the same time, all the trajectory fragments of all satellites in the same area are sorted into the trajectory fragment set of the grid Set i,j, where i represents the number of rows and j represents the number of columns. The corresponding fragments of the grid are collected and the satellite number, start time, and end time parameters of each fragment are recorded in piece i = {N s ,t s ,t e After traversing the orbital prediction parameters of all satellites at all times, the collection of all debris can be obtained. The results are as follows Figure 4 shown.

[0125] Select a search area:

[0126] Calculate satellite to target T j The visible time window needs to determine the visibility between the satellite and the target in a given sub-satellite point set. j If the grid is located in grid r, it is necessary to determine the search area of ​​the time window. The number of grids adjacent to grids at different locations is different. For example, the grid adjacent to the pole is only adjacent to the 5 surrounding grids after two-dimensional expansion, but in the spherical state, the grid is adjacent to all grids adjacent to the pole, so the search area of ​​its visible window is not just the above 5 grids. Therefore, based on the regional segmentation results given in the previous article, it is necessary to give a search area selection strategy so that the final search area can minimize the number of subsatellite points to be searched and ensure that no visible window is missed.

[0127] Figure 5 Several visible window search areas corresponding to typical grid positions are given. r1, r2, r3, and r4 correspond to four typical areas respectively. With reference to these four typical areas, the following search area selection strategy is given:

[0128] (1) r1 represents the grid adjacent to the ±90° latitude line. The search interval of this type of area includes: the reference grid where it is located, the grids adjacent to it, and all grids adjacent to the pole;

[0129] (2) r3 represents the grid adjacent to the ±180° longitude line. The search grids in this area include: the reference grid where it is located, the grid adjacent to it, and the grid adjacent to it across the ±180° longitude line;

[0130] (3) r4 represents the grid adjacent to both the ±180° longitude line and the ±90° latitude line. The search grids in this area include: the reference grid where it is located, the grids adjacent to it, the grids adjacent to it across the ±180° longitude line, and all the grids adjacent to the pole;

[0131] (4) r2 represents the grid position in general cases except for the above special cases. The search grids in this type of area include: the reference grid where the grid itself is located and the grids adjacent to it.

[0132] According to the visible window search range of the target in each area, the search range of each target can be determined. As can be seen from the figure, the search range required for the target in each area is smaller than the entire map area, and the search range is related to the grid division density. The denser the grid division, the smaller the search area.

[0133] Calculate visibility:

[0134] After determining the target T j After the search area is found, it is necessary to traverse all the fragments Set in the search area i,j , calculate its visibility to the target:

[0135] (1) Traverse each point in the sub-satellite point set and calculate the angle θ between the vector pointing from the satellite to the center of the earth and the vector pointing from the satellite to the target;

[0136] (2) Satisfying 0<θ<θ v The satellite position corresponding to the moment is the visible point, and all visible moments of each track to the target and the corresponding angle θ are recorded;

[0137] (3) Record every visible moment. If it is a continuous moment, in order to reduce the amount of data, only the start and end moments need to be recorded. At the same time, the minimum angle θ and the corresponding moment are updated as the moment with the best imaging quality between the satellite and the target in the current time window, which can be used as a reference for mission planning.

[0138] Finally, we get the time window of each satellite for each target.

[0139] The technical solution provided by the present invention is further described in detail above through several specific implementation modes in order to highlight the advantages and benefits of the technical solution provided by the present invention. However, the several specific implementation modes described above are not intended to be used as limitations on the present invention. Any reasonable modification and improvement of the present invention, combination of implementation modes and equivalent substitution within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for rapid prediction of giant constellation time windows based on trajectory time-space segmentation method, characterized in that: include: The step of collecting orbital data and preset parameters of the target constellation; The step of gridding the map according to the visibility of all satellites in the target constellation; The orbit prediction data is segmented according to the longitude and latitude of the sub-satellite point of the satellite and the divided map grids, and the satellite tracks whose sub-satellite points are located in the same grid are stored in the satellite orbit data set corresponding to the grid; The steps of designing and selecting a selection strategy for the search regions of the grids located at different locations; The step of obtaining a time window for each satellite to meet a preset mission requirement within a forecast period according to the segmented orbital data; The step of summarizing the time windows of each satellite that meet the preset mission requirements within the forecast period and obtaining the basis data for mission planning.

2. The method for rapid prediction of giant constellation time window based on trajectory time-space segmentation method according to claim 1, characterized in that: The orbit prediction data of the target constellation includes the position, speed, sub-satellite longitude and sub-satellite latitude of the satellite, and the preset parameters include satellite orbit parameters, payload parameters and platform maneuverability parameters, wherein the satellite orbit parameters include orbit semi-major axis, eccentricity, inclination, right ascension of ascending node, perigee angular distance and mean anomaly angle, the payload parameters include field of view semi-cone angle, and the platform maneuverability parameters include satellite maximum roll angle, pitch angle and yaw angle.

3. The method for rapid prediction of giant constellation time window based on trajectory time-space segmentation method according to claim 1, characterized in that: The orbital data is segmented according to time and space to generate a trajectory of the satellite covering a specific area of ​​the earth during the forecast period, thereby obtaining the segmented orbital data.

4. The method for rapid prediction of giant constellation time window based on trajectory time-space segmentation method according to claim 1, characterized in that: The preset tasks include ground imaging tasks and ground data transmission tasks.

5. A giant constellation time window rapid prediction device based on trajectory time-space segmentation method, characterized in that: include: A module for collecting orbital data and preset parameters of the target constellation; A module for gridding maps; A module for segmenting the track data; A module for obtaining a time window for each satellite to meet preset mission requirements within a forecast period according to the segmented orbital data; A module that summarizes the time windows in which each satellite meets the preset mission requirements within the forecast period and obtains the basis data for mission planning.

6. A giant constellation mission preprocessing method, characterized in that: The method comprises the steps of screening, classifying, partitioning, fusing and other pre-processing of tasks according to the giant star grid segmentation result obtained in claim 1, obtaining a processed task set, and providing task guidance for the target constellation task scheduling.

7. A giant constellation mission preprocessing device, characterized in that: The invention comprises a module for pre-processing tasks such as screening, classification, partitioning, fusion, etc. according to the giant star grid segmentation result obtained according to claim 5, obtaining a processed task set, and guiding the scheduling of target constellation tasks.

8. A computer storage medium for storing a computing program, characterized in that: When the computer reads the computer program, the computer executes the method of claim 1.

9. A computer, comprising a processor and a storage medium, characterized in that: When the processor reads the computer program stored in the storage medium, the computer executes the method of claim 1 .

10. A computer program product, being a computer program, characterized in that When the computer program is executed, the method of claim 1 is implemented.

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