An Agile Satellite Multi-Satellite Cooperative Observation-Data Transmission Joint Planning Method

Abstract

The present invention discloses a method for joint planning of agile satellite multi-satellite collaborative observation and data transmission, which includes discretely sampling a target area, calculating the visibility of each satellite to the sampling points, and obtaining the coverage characteristics of each satellite for the area based on the sampling point visibility; based on the satellite coverage characteristics, using the K-Means clustering algorithm to divide the target area into several sub-areas and assign them to the corresponding satellites; performing boundary adaptive strip division on the assigned sub-areas and calculating the number of strips; constructing a joint planning problem model for observation and data transmission, and solving it based on the elite genetic algorithm to obtain the planning result, and integrating all satellites and outputting the optimal scheduling scheme; solving the problem of difficult solution for the joint planning of regional target observation and data transmission under the multi-satellite collaboration of agile satellites, not only ensuring the coverage of the assigned area by the satellites, reducing the overlap rate of the observation strips of different satellites, but also improving the overall observation benefit under multi-satellite collaboration, and can be effectively applied to the remote sensing satellite control technology.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite data processing and satellite mission planning, and specifically provides a method for collaborative observation and data transmission joint planning of multiple agile satellites. Background Art

[0002] Remote sensing satellites can carry different payloads to image the Earth's surface, and have great application value in scenarios such as environmental census, resource exploration, and disaster relief. The new generation of agile satellites (Agile Earth Observation Satellite, AEOS) can image targets at different times and angles by adjusting the three-axis attitude, enabling multi-strip imaging in a single orbit, greatly reducing the mission cycle and having great development potential. Agile satellites have three degrees of freedom in maneuvering, namely yaw, pitch, and roll. Therefore, the visible window time for each target is greatly extended.

[0003] In the field of remote sensing satellite earth observation, a regional target usually refers to a target composed of multiple vertices with a certain area that cannot be completely covered by a single satellite in one pushbroom imaging. For this type of target, single-satellite multi-orbit repeated access or collaborative observation by multiple satellites is required to achieve full coverage. Agile satellites have an active pushbroom imaging mode, which can perform attitude maneuvers and earth observation simultaneously. This mode greatly improves the observation efficiency and benefits for regional targets.

[0004] At present, the research on agile satellite mission planning for regional targets faces the following problems:

[0005] First, considering the problem of regional repeated observation in the multi-satellite observation process, there is a problem of regional repeated observation under the cooperation of agile satellites in the existing technology, and the coverage characteristics of different satellites for the same region are not fully considered;

[0006] Second, the existing strip segmentation methods are difficult to reflect the ability of agile satellites to perform pushbroom imaging in any direction, and the dynamics of strip segmentation are not considered;

[0007] Third, most studies on on-board fixed storage resources simplify them based on the maximum number of observable tasks in a single orbit, which is quite different from the actual satellite mission execution process.

[0008] Therefore, there is an urgent need for a method for collaborative observation and data transmission joint planning of multiple agile satellites to solve the above problems. Summary of the Invention

[0009] The purpose of the present invention is to provide a method for collaborative observation and data transmission joint planning of multiple agile satellites, which can effectively solve the problems existing in the above-mentioned prior art.

[0010] To solve the above technical problems, the present invention adopts the following technical solutions: An agile satellite multi-satellite collaborative observation-data transmission joint planning method, comprising the following steps:

[0011] S1. Discretely sample the target area, calculate the visibility of each satellite to the sampling points, and obtain the coverage characteristics of each satellite for the area based on the visibility;

[0012] S2. Divide the target area into several sub-areas based on the K-Means clustering algorithm, and assign them to the corresponding satellites based on the coverage characteristics;

[0013] S3. Perform boundary adaptive strip division on the assigned sub-areas and calculate the number of strips;

[0014] S4. Construct an observation-data transmission joint planning problem model:

[0015]

[0016] Where: x ij Is a binary decision variable, meaning whether the satellite observes strip i in the jth time window, and its value range is x ij ∈{0, 1}; Strip i Is the ith strip that the satellite needs to observe; Is the number of time windows of the satellite for target i; N T Is the total number of strips; T(·) represents the strip observation duration;

[0017] S5. Solve the observation-data transmission joint planning problem model based on the elite genetic algorithm to obtain the planning result, and integrate all satellites and output the optimal scheduling plan;

[0018] Preferably, in step S1, sampling points are generated at a preset interval within the target area, and based on the orbital data and maneuverability of each satellite, the visibility data of each satellite to all sampling points in the area is calculated, and the visibility data of the satellite to all sampling points in the same area is used as the coverage characteristic of the satellite for the area.

[0019] Preferably, the specific calculation process of the visibility data is as follows:

[0020] Preset the sampling interval Δr of the target area, start from the upper left boundary of the target area, and perform dot sampling within the entire area at the sampling interval Δr to obtain the longitude and latitude coordinates of all sampling points;

[0021] Input the trajectories of all satellites during the mission period and the maximum maneuvering angle of the satellites, and calculate the visibility data of the satellites to all area sampling points during the mission period with the maximum maneuvering angle as the half angle of the coverable field of view of the satellites; if visible to the sampling point, the visibility data value is 1, otherwise the value is 0.

[0022] Preferably, in step S2, the coverage characteristics of each satellite for the same area are input, and it is judged whether there is a single satellite that can cover the sampling point according to the visibility data of different satellites for the same sampling point:

[0023] If there is a single satellite that can cover the sampling point, the average longitude and latitude of the single-satellite covered sampling point are used as the initial clustering center c of the category to which the satellite k belongs k Corresponding eigenvalue x ck :

[0024]

[0025] Where: lon and lat respectively represent the longitude and latitude of a certain point; p represents the sampling point in the single-satellite observable area of satellite k; n p Is the number of single-satellite observable sampling points, and the remaining satellites all select the initial clustering center c according to this formula k ;

[0026] Otherwise;

[0027] If there is no single-satellite covered area, start from the sampling points that can be jointly covered by two satellites, calculate the distances between the two satellites and the currently existing initial clustering centers, and select the sampling point with the largest distance as the initial clustering center c k ; Continuously increase the number of satellites, calculate the distances between the sampling points that can be jointly covered by N satellites and the currently existing initial clustering centers and generate the initial clustering center c of this star k Until the initial clustering centers c are generated for all satellites k that have coverage for this area k ;

[0028] Calculate the eigenvalue distances d between all sampling points and all clustering centers c k And use the initial clustering center c corresponding to the minimum eigenvalue distance k The cluster to which it belongs as the cluster to which the current sampling point p belongs;

[0029]

[0030] Among them, ||||2 represents the 2-norm of the vector; select the cluster to which the c corresponding to the minimum eigenvalue distance belongs k As the cluster to which the current point p belongs, and use the average longitude and latitude of all the point sets in the cluster as the new clustering center eigenvalue of this cluster

[0031] Calculate the sum of squared errors of clustering result SSE for the entire data set:

[0032]

[0033] Iterate continuously to generate cluster centers and calculate SSE, repeat the above steps, and when the maximum number of iterations is reached or the cluster center does not change, obtain the final clustering result, obtain the regional sampling points assigned by each satellite to the area according to the category, and calculate the convex hull of all sampling points to generate the sub-area to be observed;

[0034] The above process solves the problem of task allocation for regional targets under multi-satellite collaboration. By solving this problem, the problem of overlapping observation strips between different satellites is avoided. At the same time, the process also takes into account the coverage characteristics of the satellite over the region to ensure the visibility of the satellite to the allocated sub-region. Not only that, the process also reduces the coupling problem of multi-satellite collaboration into multiple single-satellite planning sub-problems, which facilitates the solution of the subsequent planning process.

[0035] Preferably, in step S3, a reference straight line L with a slope k outside the left boundary of the regional target is found, and the number of stripes is calculated based on L as follows:

[0036] The distances between the nearest and farthest points of the target area and L are d min and d max , use the satellite imaging width as the strip segmentation width wid, input the minimum padding width of the area boundary pad, and the minimum strip overlap width wid overlap And the strip division slope k, calculate the strip coverage width D:

[0037] D=d max -d min +2pad;

[0038] Calculate the approximate average width wid of each strip taking into account the overlap ratio men for:

[0039] wid mean =wid-wid overlap / 2;

[0040] The estimated number of stripes is:

[0041]

[0042] in: is the ceiling function.

[0043] Preferably, the stripe division is specifically as follows:

[0044] Initial strip generation: Shift rightward from reference line L Draw the long line segment line1 on the left edge of the strip with slope k, and translate line1 to the right by wid to get line r , calculate the intersection with the area, the length of the farthest intersection is line rLength, draw a rectangular area and record the information of the initial strip boundary points;

[0045] Among them, the distance between the left boundary of the initial strip and the reference line L The calculation formula is as follows:

[0046]

[0047] Generation of the middle strip: Starting from the previous strip line r Translate left by wid overlap And generate a long line segment line1, calculate the left intersection point of the area, translate right by wid to get line r , calculate the right intersection point of the area, compare the coordinates of the left and right intersection points and calculate their respective lengths, take the larger one as the length of this strip, draw the strip and record the boundary point information;

[0048] Repeat the above operations until the long straight line line1 has no intersection with the area, and the strip division is completed.

[0049] According to the above process, each satellite can generate a set of strip tasks to be observed.

[0050] Preferably, in step S4:

[0051] According to the active push - broom imaging rate ω of the satellite s Calculate the required observation duration T(Strip i ) for strips of different lengths; i )

[0052]

[0053] Among them: Φ i is the geocentric angle between the mid - points of the two ends of the strip Strip i ; ω s is the angular velocity of the satellite's orbital motion; k is the push - broom speed coefficient, and here k = 1.0;

[0054] Calculate the observable time window for the target strip according to the orbital data of the satellite within the mission cycle and the maximum attitude maneuvering angle; According to the longitude, latitude of the ground station distribution and the minimum data transmission elevation angle EA 0 , calculate the observation windows of all satellites for all targets and the data transmission windows of all ground stations:

[0055]

[0056] Among them: EA 0 is the minimum elevation angle of the ground station; EA is the elevation angle of the ground station at the current position of the satellite; h is the current orbital height of the satellite; R eis the radius of the Earth; α is the angle between the longitude and latitude of the satellite's sub-satellite point and the longitude and latitude of the ground station, and the calculation formula is as follows:

[0057]

[0058] Where: λ represents the latitude and longitude of a certain point respectively; the subscripts k and g represent the satellite and the ground station.

[0059] According to the constraint satisfaction model, an observation-data transmission joint planning problem model is constructed. This model includes: the performance index is the sum of the observation durations of the satellite for all observation strips; among them, the constraint satisfaction model includes:

[0060] Constraint 1 is the observation uniqueness constraint, that is, a strip can only be observed once:

[0061]

[0062] Where: SET T is the set of observation strip tasks of the satellite;

[0063] Constraint 2 is the observation window constraint. The satellite needs to observe the strip within the observable time window of the strip:

[0064]

[0065] Where: ts ij and te ij represent the start time and end time of the j-th time window of the satellite k for the strip i respectively; t i is the start time of the satellite k's observation of the strip i; d i is the strip observation duration;

[0066] Constraint 3 is the task transfer time constraint. When the satellite k observes two consecutive strips, it is necessary to ensure that the difference between the start time of the latter task and the end time of the former task can meet the satellite attitude maneuvering time:

[0067] t i +d i +tm in ≤t n ;

[0068] Where: i represents the previous executed task; n represents the latter pending task; tm in represents the maneuvering duration required to transfer from task i to task n. This duration is related to the satellite's maneuvering ability, and the specific calculation is described in the subsequent attitude maneuvering modeling section;

[0069] Constraint 4 is the on-board storage constraint. The satellite needs to check whether the storage exceeds the capacity limit during the planning process:

[0070]

[0071] Among them: mem(t) represents the solid-state memory that the satellite has used at the current moment t; represents the on-board solid-state memory required for the observation strip i; mem max is the maximum available solid-state memory.

[0072] Constraint 5 is the data transmission window constraint. The satellite needs to perform data transmission tasks within the countable data transmission window of the ground station:

[0073]

[0074] Among them: respectively represent the actual start and end times of data transmission of the satellite at the ground station g; ts gj , te gj respectively represent the start and end times of the j-th countable data transmission window of the satellite for the ground station g;

[0075] Constraint 6 is the data transmission time constraint. The data of the strip pushbroom observation by the satellite during one power-on must be completely downloaded at one time within a ground station data transmission window. Due to the limited rate of the space-ground link, it is necessary to calculate that the total data transmission duration corresponding to the strip data downloaded each time is less than the data transmission window length:

[0076]

[0077] Among them: v t is the link data transmission rate; dt i is the time consumed for data transmission of task i; Trans g represents the set of strip data transmission tasks arranged at the data transmission station g;

[0078] After constructing the above-mentioned observation-data transmission joint planning model, solution and constraint checking can be carried out in the subsequent steps.

[0079] Preferably, the solution of the elite genetic algorithm in step S5 includes iterating the following operations until the preset number of iterations is reached and stopped, obtaining the maximum sequence of performance indicators, and integrating all satellites to obtain the optimal scheduling scheme. The operations include:

[0080] The visibility data (task number, start and end times) of the current satellite for all strips and ground stations are sorted in ascending order of the start time. Suppose there are N pieces of data in total, then the chromosome length is N, and the task coding method is as follows: the observation task coding set {0, 1, 2}, and the data transmission task coding set {0, 3};

[0081] Starting from the first gene on the chromosome, perform the following operations step by step to obtain all genes: If a certain gene position i is the first observed band in this region or the task gene of the previous band is encoded as 0, then randomly select a gene encoding with the same probability; otherwise, select the encoding by roulette wheel. The roulette wheel probability is calculated as follows:

[0082]

[0083] where: ε is the roulette wheel parameter, and its distribution is ε ∈ [0,1]; code(i - 1) is the observation method of the previous band in the same region;

[0084] If the current task is a data transmission task, perform roulette wheel selection according to the currently used fixed storage:

[0085]

[0086] where: P(tr) represents the data transmission probability; w is the interval mapping parameter used to map the original interval [-1,1] to [-w,w]; mem(t) represents the amount of fixed storage used at the current time t; mem max is the maximum available fixed storage;

[0087] Perform constraint checking on the generated chromosome. Starting from the first gene that is not 0, for the observation task, check constraints 1 - 4 one by one; for the data transmission task, check constraints 5 and 6. If the current gene task satisfies all constraints, calculate the start and end times of executing this task, the amount of fixed storage consumed, and the corresponding observation attitude; if the current gene does not satisfy one of the constraints, set the gene encoding to 0 and the task execution time to -1;

[0088] Chromosome crossover is selected by roulette wheel with the performance index of the parental task planning result as the weight:

[0089]

[0090] where: J i represents the individual performance index value, which can be obtained from the above performance index calculation formula; P i represents the probability that individual i is selected; POP represents all individuals in the entire population; after selecting two individuals, randomly generate a crossover position and perform crossover on all genes after the crossover position;

[0091] Calculate the number of gene mutation points according to the chromosome length Select any position on the entire chromosome for mutation, and the mutation method is the same as the above encoding method;

[0092] Perform constraint checking on the generated offspring, calculate the performance index of the offspring that satisfies the constraints, and retain the larger one.

[0093] Beneficial effects: The present invention provides a method for collaborative observation and data transmission joint planning of multiple agile satellites. First, discrete point sampling is performed on the area and the coverage data of the sampling points is calculated. The area is divided into different satellite task sub-regions by using the K-Means clustering task allocation algorithm based on the multi-satellite coverage characteristics, which solves the problem of difficult collaborative task allocation of multiple agile satellites for the same regional target. Subsequently, the area adaptive strip division method is used to generate the strip to be observed, and the satellite observation strip is quickly generated by mathematical calculation, improving the intersection and union ratio of the strip and the area. Finally, an elite genetic algorithm based on observation-data transmission fusion coding is proposed, and a targeted design is carried out for the process from population generation to population evolution, which solves the problem of difficult solution of the collaborative observation-data transmission joint planning of regional targets under multiple agile satellites. It not only ensures the coverage of the allocated area by the satellite, reduces the overlap rate of the observation strips of different satellites, but also improves the overall observation benefit under multi-satellite collaboration, and can be effectively applied to the remote sensing satellite control technology. Description of the Drawings

[0094] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention, but do not constitute a limitation to the present invention.

[0095] In the drawings:

[0096] Figure 1 is the flowchart of the planning method of the present invention;

[0097] Figure 2 is the schematic diagram of sampling point generation and coverable area calculation;

[0098] Figure 3 is the effect simulation diagram of the K-Means clustering algorithm;

[0099] Figure 4 is the schematic diagram of parental chromosome crossover;

[0100] Figure 5 is the attitude maneuver curve and the solid storage usage curve of satellite 5. Detailed Embodiments

[0101] The embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention. The terms used in the embodiments of the present invention are only used to explain the specific embodiments of the present invention, and are not intended to limit the present invention. The embodiments of the present application will be described below with reference to the drawings.

[0102] Embodiment: Refer to Figure 1 As shown, a method for collaborative observation and data transmission joint planning of multiple agile satellites includes the following steps:

[0103] S1. Generate sampling points at preset intervals in the target area, and calculate the visibility data of all sampling points in the area based on the orbital data and maneuverability of each satellite, and use the visibility data of the satellite to all sampling points in the same area as the coverage feature of the satellite to the area;

[0104] The specific calculation process of visibility data is as follows:

[0105] (11) Given a sampling interval Δr of the target area, starting from the upper left boundary of the target area, dot sampling is performed inside the entire area with a sampling interval Δr to obtain the longitude and latitude coordinates of all sampling points. The result is as follows: Figure 2 Shown on the left.

[0106] (12) Input the orbits of all satellites during the mission cycle and the maximum maneuvering angle of the satellite. Take the maximum maneuvering angle as the half angle of the satellite's field of view. Calculate the satellite's visibility to all regional sampling points during the mission cycle. If the sampling point is visible, the visibility data value is 1, otherwise the value is 0. The sampling point visibility result is as follows: Figure 2 Shown on the right.

[0107] (13) The visibility data of all sampling points in the same area are used as the coverage feature of the satellite over the area. The convex hull formed by all sampling points with visibility data value of 1 is the sub-area that the satellite can cover in the area.

[0108] S2. Based on the coverage characteristics of different agile satellites in the same area, the target area is segmented using the K-Means clustering algorithm, and the sub-areas are assigned to the corresponding satellites according to their categories;

[0109] (21) Input the coverage characteristics of each satellite for the same area, and determine whether there is a single satellite that can cover the sampling point based on the visibility data of different satellites for the same sampling point:

[0110] (22) If there are sampling points that can be covered by a single satellite, the mean longitude and latitude of the sampling points that can be covered by a single satellite is used as the initial cluster center c of the category to which the satellite k belongs. k Corresponding eigenvalue x ck :

[0111]

[0112] Where: lon and lat represent the longitude and latitude of a point respectively; p represents the sampling point of the single-star observable area of ​​satellite k; n p is the number of observable sampling points of a single satellite, and the remaining satellites select the initial cluster center c according to this formula k ;

[0113] otherwise;

[0114] (23) If there is no area that can be covered by a single star, start from the sampling point that can be covered by the two stars together, calculate the distance between the two stars and the current initial cluster center, and select the sampling point with the largest distance as the initial cluster center c k ; Continuously increase the number of satellites, calculate the distance between the sampling points that N satellites can jointly cover and the current initial clustering center, and generate the initial clustering center c of the satellite k , until all satellites k covering the area generate initial cluster centers c k ;

[0115] (24) Calculate all sampling points and all cluster centers c k The initial cluster center c corresponding to the minimum eigenvalue distance d k The cluster to which the current sampling point p belongs is taken as the cluster to which the current sampling point p belongs;

[0116]

[0117] Among them: || ||2 represents the 2-norm of the vector; select the c corresponding to the minimum eigenvalue distance k The cluster to which the current point p belongs is taken as the cluster to which the current point p belongs, and the mean of the longitude and latitude of all points in the cluster is taken as the new cluster center feature value of the cluster.

[0118] (25) Calculate the sum of squares of the error of the entire data set and the clustering result SSE:

[0119]

[0120] (26) Continuously iterate to generate cluster centers and calculate SSE, repeat the above steps, and when the maximum number of iterations is reached or the cluster center does not change, obtain the final clustering result, obtain the regional sampling points assigned by each satellite to the area according to the category, calculate the convex hull of all sampling points to generate the sub-area to be observed; Figure 2 As shown;

[0121] The above process solves the problem of task allocation for regional targets under multi-satellite collaboration. By solving this problem, the problem of overlapping observation strips between different satellites is avoided. At the same time, the process also takes into account the coverage characteristics of the satellite over the region to ensure the visibility of the satellite to the allocated sub-region. Not only that, the process also reduces the coupling problem of multi-satellite collaboration into multiple single-satellite planning sub-problems, which facilitates the solution of the subsequent planning process.

[0122] S3, dividing the allocated sub-areas into boundary-adaptive strips and calculating the number of strips;

[0123] Find the reference straight line L with a slope of k outside the left boundary of the regional target. Taking L as the reference, the number of strips is calculated as:

[0124] (31) The distances corresponding to the nearest and farthest points between the target area and L are d min and d max respectively. Taking the satellite's ground imaging width as the strip segmentation width wid, the minimum filling width pad of the input area boundary, the minimum overlap width wid overlap of the strip, and the strip division slope k, calculate the strip coverage width D:

[0125] D = d max - d min + 2pad;

[0126] (32) Calculate the approximate average width wid men of each strip considering the overlap rate as:

[0127] wid mean = wid - wid overlap / 2;

[0128] (33) The estimated result of the number of strips is:

[0129]

[0130] where: is the ceiling function.

[0131] The specific strip division is as follows:

[0132] (34) Generation of the initial strip: Translate the reference line L to the right and draw the long line segment line1 of the left boundary of the strip with slope k. Translate line1 to the right by wid to get line r , calculate the intersection points with the region, and the length of the farthest intersection point is the length of line r . Draw the rectangular region and record the information of the initial strip boundary points;

[0133] where: The distance between the left boundary of the initial strip and the reference line L is calculated as follows:

[0134]

[0135] (35) Generation of the intermediate strip: Starting from the previous strip line r , translate it to the left by wid overlap and generate the long line segment line1. Calculate the intersection point on the left side of the region, translate it to the right by wid to get line r , calculate the intersection point on the right side of the region, compare the coordinates of the intersection points on the left and right sides and calculate their respective lengths, and take the larger one as the length of this strip. Draw the strip and record the boundary point information;

[0136] (36) Repeat the above operations until there are no intersections between the long straight line line1 and the area, and the strip division is completed.

[0137] According to the above process, each satellite can generate a set of strip observation tasks.

[0138] S4. Each satellite calculates the strip observation duration according to the active push-broom imaging rate, and calculates all observation windows and data transmission windows within the mission period. Then, each agile satellite uses the constraint satisfaction model to model the observation-data transmission joint planning problem:

[0139]

[0140] where x ij is a binary decision variable, which means whether the satellite observes strip i in the j-th time window, and its value range is x ij ∈ {0, 1}; Strip i is the i-th strip that the satellite needs to observe; is the number of time windows of the satellite for target i; N T is the total number of strips; T(·) represents the strip observation duration;

[0141] (41) Calculate the required observation duration T(Strip s ) of different length strips Strip i according to the active push-broom imaging rate ω i ) of the satellite;

[0142]

[0143] where: Φ i is the geocentric angle at the midpoints of both ends of strip Strip i ; ω s is the angular velocity of the satellite's orbital motion; k is the push-broom speed coefficient, and here k = 1.0;

[0144] (42) Calculate the observable time window for the target strip according to the orbital data of the satellite within the mission period and the maximum angle of attitude maneuver; calculate the observation windows of all satellites for all targets and the data transmission windows of all ground stations according to the longitude and latitude of the ground station distribution and the minimum elevation angle EA 0 :

[0145]

[0146] where: EA 0 is the minimum elevation angle of the ground station; EA is the elevation angle of the ground station at the current position of the satellite; h is the current orbital altitude of the satellite; R e is the radius of the earth; α is the angle between the longitude and latitude of the satellite's sub-satellite point and the longitude and latitude of the ground station, and the calculation formula is as follows:

[0147]

[0148] Wherein: λ represents the latitude and longitude of a certain point respectively; the subscripts k and g represent the satellite and the ground station.

[0149] (43) Construct an observation-data transmission joint planning problem model according to the constraint satisfaction model. The model includes: the performance index is the sum of the observation durations of all observation strips by the satellite; wherein, the constraint satisfaction model includes:

[0150] Constraint 1 is the observation uniqueness constraint, that is, a strip can only be observed once:

[0151]

[0152] Wherein: SET T is the set of strip task sets to be observed by the satellite;

[0153] Constraint 2 is the observation window constraint. The satellite needs to observe the strip within the observable time window of the strip:

[0154]

[0155] Wherein: ts ij and te ij represent the start time and end time of the j-th time window of the satellite k for the strip i respectively; t i is the start time of the satellite k for observing the strip i; d i is the observation duration of the strip;

[0156] Constraint 3 is the task transfer time constraint. When the satellite k observes two consecutive strips, it is necessary to ensure that the difference between the start time of the latter task and the end time of the former task can meet the satellite attitude maneuvering time:

[0157] t i +d i +tm in ≤t n ;

[0158] Wherein: i represents the previous executed task; n represents the latter to-be-executed task; tm in represents the maneuvering duration required to transfer from task i to task n. This duration is related to the satellite maneuvering ability, and the specific calculation is described in the subsequent attitude maneuvering modeling part;

[0159] Constraint 4 is the on-board storage constraint. The satellite needs to check whether the storage exceeds the capacity limit during the planning process:

[0160]

[0161] Among them: mem(t) represents the fixed storage already used by the satellite at the current time t; represents the on-board fixed storage required to consume the i-th observation strip; mem max is the maximum available fixed storage amount.

[0162] Constraint 5 is the data transmission window constraint, and the satellite needs to perform data transmission tasks within the countable data transmission window of the ground station:

[0163]

[0164] Among them: respectively represent the true start and end times of data transmission of the satellite at the ground station g; ts gj , te gj respectively represent the start and end times of the j-th countable data transmission window of the satellite for the ground station g;

[0165] Constraint 6 is the data transmission time constraint. The data of the strip push-broom observation performed by the satellite during one power-on must be completely downloaded at one time within a data transmission window of a ground station. Due to the limited rate of the space-ground link, it is necessary to calculate that the total data transmission duration corresponding to the strip data downloaded each time is less than the length of the data transmission window:

[0166]

[0167] Among them: v t is the link data transmission rate; dt i is the time consumed for data transmission of task i; Trans g represents the set of strip data transmission tasks arranged at the data transmission station g;

[0168] After constructing the above-mentioned observation-data transmission joint planning model, solution and constraint checking can be carried out in the subsequent steps.

[0169] S5. Solve the observation-data transmission joint planning problem model based on the elite genetic algorithm to obtain the planning result, integrate all satellites and output the optimal scheduling plan;

[0170] The solution of the elite genetic algorithm includes iteratively performing the following operations until the preset number of iterations is reached and stopped, obtaining the maximum sequence of performance indicators, and integrating all satellites to obtain the optimal scheduling plan. The operations include:

[0171] (51). The visibility data (task number, start and end times) of the current satellite for all strips and ground stations are sorted in ascending order of the start time. Suppose there are N pieces of data in total, then the chromosome length is N, and the task coding method is as follows: the observation task coding set {0, 1, 2}, and the data transmission task coding set {0, 3};

[0172] (52) Starting from the first gene on the chromosome, perform the following operations step by step to obtain all genes: If a certain gene position i is the first observed band in this region or the previous band task gene code is 0, then randomly select a gene code with the same probability. Otherwise, use roulette wheel selection to select the code. The roulette wheel probability calculation is as follows:

[0173]

[0174] where: ε is the roulette wheel parameter, and its distribution is ε ∈ [0,1], and code(i - 1) is the previous band observation method in the same region;

[0175] If the current task is a data transmission task, perform roulette wheel selection according to the currently used fixed storage:

[0176]

[0177] where: P(tr) represents the data transmission probability; w is the interval mapping parameter used to map the original interval [-1,1] to [-w,w]; mem(t) represents the amount of fixed storage used at the current time t; mem max is the maximum available fixed storage;

[0178] (53) Perform constraint checking on the generated chromosome. Starting from the first gene that is not 0, for the observation task, check constraints 1 - 4 one by one. For the data transmission task, check constraints 5 and 6. If the current gene task satisfies all constraints, calculate the start and end times of executing this task, the amount of fixed storage consumed, and the corresponding observation attitude; if the current gene does not satisfy one of the constraints, set the gene code to 0 and the task execution time to -1;

[0179] (54) Chromosome crossover uses the performance index of the parental task planning result as the weight for roulette wheel selection:

[0180]

[0181] where: J i represents the individual performance index value, which can be obtained from the above performance index calculation formula; P i represents the probability that individual i is selected; POP represents all individuals in the entire population; after selecting two individuals, randomly generate a crossover position and perform crossover on all genes after the crossover position, as Figure 3 shown;

[0182] (55) Calculate the number of gene mutation points according to the chromosome length Select any position in the entire chromosome for mutation, and the mutation method is the same as the above coding method;

[0183] (56) Constrained inspection is performed on the generated offspring, the performance index of the constrained offspring that meets the requirements is calculated, and the larger one is retained.

[0184] Figure 4 and Figure 5 The results of using the above-mentioned joint observation and data transmission planning algorithm for 50 randomly generated sub-region strips by one of the satellites in a day are respectively given. From these results, it can be seen that the attitude angle of the satellite is less than 45° throughout the mission cycle, and the solid storage usage is less than 100%, which proves the effectiveness of the present invention.

[0185] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. For those of ordinary skill in the art in this technical field, after learning the content recorded in the present invention, without departing from the principle of the present invention, several equivalent transformations and substitutions can still be made, and these equivalent transformations and substitutions should also be regarded as belonging to the protection scope of the present invention.

Claims

1. An agile satellite multi-satellite collaborative observation-data transmission joint planning method, characterized in that The steps include: S1. Discretely sample the target area, calculate the visibility of each satellite to the sampling point, and obtain the coverage characteristics of each satellite to the area based on the visibility; Generate sampling points at preset intervals in the target area, and calculate the visibility data of all sampling points in the area based on the orbital data and maneuverability of each satellite. The visibility data of all sampling points in the same area is used as the coverage feature of the satellite to the area. The specific calculation process of visibility data is as follows: The sampling interval Δr of the target area is preset, starting from the upper left boundary of the target area, sampling is performed inside the entire area at the sampling interval Δr to obtain the longitude and latitude coordinates of all sampling points; Input the trajectory of all satellites in the mission cycle and the maximum maneuvering angle of the satellite, and use the maximum maneuvering angle as the half angle of the satellite's coverable field of view to calculate the visibility data of the satellite to all regional sampling points in the mission cycle; S2, based on the K-Means clustering algorithm, the target area is divided into several sub-areas and assigned to corresponding satellites based on coverage characteristics; S3, dividing the allocated sub-areas into boundary-adaptive strips and calculating the number of strips; S4. Construct observation-data transmission joint planning problem model: Where: x ij is a binary decision variable, which means whether the satellite observes strip i in the j-th time window, and its value range is x ij ∈ {0, 1}; Strip i is the i-th strip that the satellite needs to observe; is the number of time windows for the satellite to target i; N T is the total number of strips; T(·) represents the strip observation duration; The observation-data transmission joint planning problem model is built based on the constraint satisfaction model, including: Constraint 1: A strip can only be observed once; Constraint 2: The satellite observes the strip within the observable time window of the strip; Constraint 3: The time interval between two consecutive strips observed by the satellite is greater than or equal to the satellite maneuvering time; Constraint 4: Satellites need to check whether storage exceeds capacity limits during planning; Constraint 5: The satellite performs data transmission tasks within the data transmission window of the ground station; Constraint 6: The satellite must transmit all the strip push-scan observation data in one time within a ground station data transmission window, and the total data transmission time corresponding to each strip data transmission must be less than the data transmission window length. S5. Solve the observation-data transmission joint planning problem model based on the elite genetic algorithm to obtain the planning results, integrate all satellites and output the optimal scheduling plan.

2. The agile satellite multi-satellite collaborative observation-data transmission joint planning method according to claim 1, characterized in that: In step S2, the coverage characteristics of each satellite for the same area are input, and the visibility data of different satellites for the same sampling point are used to determine whether a single satellite can cover the sampling point: If there is a single satellite that can cover the sampling points, the average of the longitude and latitude of the sampling points covered by the single satellite is used as the initial clustering center c of the category to which the satellite k belongs k ; otherwise Increment the number of satellites starting from the point where the double satellites can jointly cover the sampling points, and gradually calculate the distances between each satellite and the existing initial clustering centers. Select the sampling point with the maximum distance as the initial clustering center c k ; Calculate the distances between the sampling points that can be jointly covered by the N satellites and the existing initial clustering centers, and generate the initial clustering center c for this satellite k , until the initial clustering centers c are generated for all satellites k that have coverage of this area k ; Calculate the eigenvalue distances between all sampling points and all cluster centers c k and use the initial cluster center c corresponding to the minimum eigenvalue distance k whose cluster the current sampling point p belongs to; Calculate the error sum of the entire data set and iterate until the preset condition is reached, generate the cluster center and obtain the clustering result; The regional sampling points assigned by each satellite to the area are obtained according to the category, and the convex hull of all sampling points is calculated to generate the sub-area to be observed.

3. An agile satellite multi-satellite collaborative observation-data transmission joint planning method according to claim 1, characterized in that: In step S3, a reference straight line L with a slope of k outside the left boundary of the regional target is found. Taking L as a reference, the number of stripes is calculated as: Wherein: is the ceiling function; D = d max -d min + 2pad; Where: D is the strip coverage width; d max and d min are the distances corresponding to the nearest and farthest points of the target area and the reference straight line L respectively; pad is the minimum padding width of the input area boundary; wid mean = wid - wid overlap / 2; wid is the strip splitting width, wid overlap is the minimum overlap width of the strip.

4. An agile satellite multi-satellite collaborative observation-data transmission joint planning method according to claim 3, characterized in that: The strip division is as follows: Translate to the right from the reference line L Draw a long line segment line for the left boundary of the strip with slope k l , and translate line l to the right by wid to get line r , calculate the intersection points with the region, and the length of the farthest intersection point is the length of line r ; draw a rectangular region and record the information of the initial strip boundary points Starting from the previous strip line r Perform the following operations until the long straight line line1 has no intersection with the area: Translate left by wid overlap And generate a long line segment line1, calculate the left intersection point of the area, and translate right by wid to get line r , calculate the right intersection point of the area, compare the coordinates of the left and right intersection points and calculate their respective lengths, take the larger one as the length of this strip, draw the strip and record the boundary point information.

5. The agile satellite multi-satellite collaborative observation-data transmission joint planning method according to claim 4, wherein: Distance is as follows:

6. The agile satellite multi-satellite collaborative observation-data transmission joint planning method according to claim 1, characterized in that: In step S4, the observation time required for strips of different lengths is calculated according to the satellite active push-scan imaging rate; The observable time window for the target strip is calculated based on the satellite's orbital data and the maximum angle of attitude maneuver during the mission cycle; According to the longitude and latitude of the ground stations and the minimum elevation angle of data transmission, the observation windows of all satellites for all targets and the data transmission windows of all ground stations are calculated.

7. An agile satellite multi-satellite collaborative observation-data transmission joint planning method according to claim 6, characterized in that: In step S5, the elite genetic algorithm solution includes iteratively performing the following operations until the preset number of iterations is reached and stopped, obtaining the sequence with the maximum performance index, and integrating all satellites to obtain the optimal scheduling plan. The operations include: Sort the visibility data of the current satellite for all strips and ground stations in ascending order of start time. Suppose there are N pieces of data in total, the chromosome length is N, and the task coding method is as follows: the observation task coding set {0, 1, 2}, and the data transmission task coding set {0, 3}; If a certain gene position i is the first observed strip in this area or the gene coding of the previous strip is 0, then randomly select a gene coding with the same probability, otherwise select the coding by roulette wheel; After generating the chromosome, perform the corresponding constraint check. For each task with a coding not equal to 0, judge whether the task can be executed and plan the start execution time of the task. If a constraint cannot be satisfied, set the gene coding to 0 and the task execution time to -1; Chromosome crossover selects by roulette wheel with the performance index of the parental task planning result as the weight, and crosses all subsequent genes with a random crossover position; Calculate the number of gene mutation points according to the chromosome length: Wherein: is the floor function; Select any position in the chromosome for mutation, and the mutation method is the same as the gene coding method; Perform constraint check on the generated offspring, calculate the performance index of the offspring that meet the constraints, and retain the larger one.

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