Large-area multi-satellite cooperative rapid full-coverage observation method for disaster rescue

Abstract

The invention discloses a large-area multi-satellite cooperative rapid full-coverage observation method for disaster rescue, and provides a multi-satellite cooperative observation model based on a multi-level disaster area, and the method comprises the steps: 1, inputting a target observation area, and carrying out the multi-level hexagonal discretization processing; 2, satellite orbit parameters are read in, and real-time satellite sub-satellite point coordinates are calculated through six elements; 3, dividing satellite earth imaging into a tetragonal imaging model and a pentagonal imaging model according to sensor imaging types, and calculating respective minimum observation functions; step 4, calculating an earth observation coverage zone model of each satellite by using a line-of-sight equation; and 5, taking the calculated satellite earth observation coverage band model as an optimization target. The invention relates to the technical field of remote sensing satellites. According to the method, a model is established through a multi-level hexagonal discretization technology and an adaptive evolution search algorithm, a fastest observation scheme meeting full coverage of a target area is solved, and the timeliness of disaster rescue is greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of remote sensing satellite technology, and in particular to a large-area multi-satellite coordinated rapid full-coverage observation method for disaster relief. Background Art

[0002] Satellite collaborative observation technology refers to the statistics of satellite real-time ground coverage belts, and the provision of collaborative observation plans for target areas, taking into account factors such as satellite orbital parameters and the rotation of the earth. It is of great significance in the field of disaster relief. However, the satellite collaborative observation technology currently used in the field of emergency rescue often adopts the quadrilateral discretization method and does not consider the severity segmentation problem within the disaster area, resulting in poor overall observation accuracy. At the same time, existing satellite collaborative observation methods often use annealing algorithms and only consider the minimum number of satellites. This is very different from the requirement of time being the most important in disaster relief. It is impossible to calculate the minimum time plan to achieve full coverage of the target area, which seriously affects the timeliness and emergency response of disaster relief. Summary of the invention

[0003] The purpose of the present invention is to provide a large-area multi-satellite coordinated rapid full-coverage observation method for disaster relief, so as to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a large-area multi-satellite collaborative rapid full-coverage observation system for disaster relief, including an algorithm model, the algorithm model includes a multi-level hexagonal discretization model and an adaptive evolutionary search algorithm, and the algorithm model is used to perform full coverage solution calculations for the target area and obtain the fastest observation plan.

[0005] As a further solution of the present invention: a large-area multi-satellite coordinated rapid full-coverage observation method for disaster relief, comprising:

[0006] Step S1: input the target observation area and perform multi-level hexagonal discretization processing;

[0007] Step S2: Read in the satellite orbit parameters and use six numbers to calculate the real-time satellite sub-satellite point coordinates;

[0008] Step S3: Divide satellite earth imaging into four-sided and five-sided imaging models according to the type of sensor imaging, and calculate the minimum observation function of each model;

[0009] Step S4: Calculate the earth observation coverage model of each satellite using the line of sight equation;

[0010] Step S5: Taking the calculated satellite earth observation coverage model as the optimization target, an improved adaptive evolutionary search algorithm is used to search for a satellite collaborative observation solution that can fully cover the target observation area in the shortest time.

[0011] As a further solution of the present invention: a large-area multi-satellite coordinated rapid full-coverage observation method for disaster relief, the step S1 includes

[0012] Step S11: evenly divide the target disaster area into hexagonal grids;

[0013] Step S12: setting disaster level coefficients, and defining level coefficients for general disaster areas, major disaster areas, core disaster areas, and other disaster areas respectively;

[0014] Step S13: Setting disaster level grids, and planning polygonal grids for general disaster areas, major disaster areas, and core disaster areas respectively.

[0015] As a further solution of the present invention: a large-area multi-satellite coordinated rapid full-coverage observation method for disaster relief, the step S2 includes

[0016] Step S21: sampling the sub-satellite point coordinates of the sampling satellite and establishing a sub-satellite point coordinate system;

[0017] Step S22: quantitatively rotating the sub-satellite point coordinate system and obtaining a rotated coordinate system;

[0018] Step S23: Substitute the rotating coordinate system into the true anomaly angle formula and the eccentric anomaly angle formula for calculation;

[0019] Step S24: Obtain the eccentricity, revolution period, revolution time and satellite orbit period of the sampling satellite.

[0020] As a further solution of the present invention: a large-area multi-satellite coordinated rapid full-coverage observation method for disaster relief, the step S3 includes

[0021] Step S31: selecting a synthetic aperture radar according to the earth imaging characteristics of the satellite sensor;

[0022] Step S32: using synthetic aperture radar to divide the satellite sub-satellite trajectory into a model and set an observation function to determine the satellite imaging situation;

[0023] Step S33: Calculate and integrate the azimuth and elevation angles of each vertex of the satellite according to the minimum observation function to calculate the satellite imaging model.

[0024] As a further solution of the present invention: a large-area multi-satellite coordinated rapid full-coverage observation method for disaster relief, the step S5 is calculated using a satellite earth observation coverage belt model, specifically including:

[0025] Step S51: Generate a total population S and an initial population S', and generate an observation time T = 0. The population S contains M individuals, the initial value in S' is 0, each individual corresponds to a ground coverage belt model of a satellite, and each individual includes 1 chromosome, which is composed of N quantum bits and a time parameter t. One quantum bit corresponds to a ground coverage belt of a sensor in the satellite. The time parameter t stores the observation time of a single ground coverage belt model. The time length T is set as the optimal observation target, and the probability amplitudes k of all quantum bits in the initial population are initialized to 0;

[0026] Step S52: Measure the individuals in the population: compare the probability amplitude k of the quantum bit with the disaster coefficient α of the ground observation area grid. If the disaster coefficient α is greater than the probability amplitude k of the quantum bit and α≠4, the measurement value hi of the quantum bit is +1, indicating that the observation task of the target grid is performed. Otherwise, the measurement value h of the quantum bit remains unchanged, indicating that the observation task of the target grid is not performed. When the target grid completes α observations, α=4, indicating that the observation is completed.

[0027] Step S53: Calculate the final value of the measured value h of the individuals in the population, h = ∑hi, take the size of the final measured value h and the size of the time parameter t as the optimization target, and retain the best individual, consider the maximum h and the minimum t of each individual at the same time, the individual corresponding to the highest comprehensive consideration is the best individual, and input the individual into S', and add the t corresponding to the individual to T;

[0028] Step S54: Determine whether the individual termination condition is triggered, that is, all quantum bits in the same individual have been detected. If triggered, jump to the next step. If not triggered, continue to use the adaptive evolution method to search for the individual;

[0029] Step S55: Determine whether the population termination condition is triggered, that is, all individuals in S are retrieved, and all α=4 in the original grids of α≠0 in the target observation area. If not triggered, return directly to step S52. If triggered, end the iteration and output all the optimal satellite combinations and the shortest observation time T in the population S'.

[0030] As a further solution of the present invention: a large-area multi-satellite coordinated rapid full-coverage observation method for disaster relief also includes non-SAR imaging, and the non-SAR imaging is calculated through a quadrilateral imaging model.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] The present invention establishes a model through multi-level hexagonal discretization technology and adaptive evolutionary search algorithm to solve the fastest observation plan that meets the full coverage of the target area. Specifically, an improved adaptive evolutionary search algorithm is used to search for a satellite collaborative observation plan that can fully cover the target observation area in the shortest time. The present invention establishes a model through multi-level hexagonal discretization technology and adaptive evolutionary search algorithm to solve the fastest observation plan that meets the full coverage of the target area, greatly improving the timeliness of disaster relief. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A flow chart of a large-area multi-satellite coordinated rapid full-coverage observation method for disaster relief provided in an embodiment of the present invention;

[0034] Figure 2 A schematic diagram of a multi-level hexagonal grid effect provided in an embodiment of the present invention;

[0035] Figure 3 A schematic diagram of the effect of six satellite orbit numbers provided in an embodiment of the present invention;

[0036] Figure 4 This is a schematic diagram of the effect of the satellite rectangular imaging model provided in an embodiment of the present invention;

[0037] Figure 5 A schematic diagram of the satellite pentagonal imaging model effect provided in an embodiment of the present invention;

[0038] Figure 6 Schematic diagram of the effect of the multi-level hexagonal grid full coverage analysis method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] In the description of the present invention, it should be noted that, for ordinary technicians in the field, the specific meanings of the above terms in the present invention can be understood in specific circumstances. The following is an explanation of the embodiments of the present invention based on the overall content. In addition, the technical features in the various embodiments or single embodiments provided by the invention can be arbitrarily combined with each other to form a feasible technical solution. This combination is not subject to the constraints of the sequence of steps and / or the structural composition mode, but must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that this combination of technical solutions does not exist and is not within the protection scope required by the present invention.

[0041] In order to achieve full coverage observation of the disaster area as quickly as possible, it is hoped to design a large-area multi-satellite collaborative rapid full coverage observation method, and establish a model through multi-level hexagonal discretization technology and adaptive evolutionary search algorithm to achieve the requirement of solving the fastest observation plan that meets the full coverage of the target area. This problem is the speed optimization problem of a large-area multi-satellite collaborative observation method without changing the resource input. In response to this problem, a model is established through multi-level hexagonal discretization technology and adaptive evolutionary search algorithm, and a new solution strategy is proposed. The satellite collaborative observation method is optimized and designed, which greatly improves the speed of full coverage observation. Based on this idea, an embodiment of the present invention provides a large-area multi-satellite collaborative rapid full coverage observation method for disaster relief.

[0042] See also Figure 1 , the method includes

[0043] Step S1, input the target observation area and perform multi-level hexagonal discretization processing;

[0044] Step S2, read in the satellite orbit parameters, and use six numbers to calculate the real-time satellite sub-satellite point coordinates;

[0045] Step S3, dividing satellite earth imaging into four-sided and five-sided imaging models according to the type of sensor imaging, and calculating the respective minimum observation functions;

[0046] Step S4, calculating the earth observation coverage model of each satellite using the line of sight equation;

[0047] Step S5: Taking the calculated satellite earth observation coverage model as the optimization target, an improved adaptive evolutionary search algorithm is used to search for a satellite collaborative observation solution that can fully cover the target observation area in the shortest time.

[0048] The present invention establishes a model through multi-level hexagonal discretization technology and adaptive evolutionary search algorithm to solve the fastest observation plan that meets full coverage of the target area, greatly improving the timeliness of disaster relief.

[0049] Based on the content of the above method embodiment, as an optional embodiment, the large-area multi-satellite coordinated rapid full-coverage observation method for disaster relief provided in the embodiment of the present invention, the multi-level hexagonal discretization technology in step S1 includes:

[0050] Step S11, evenly dividing the target disaster area into hexagonal grids;

[0051] Step S12, setting the disaster level coefficient α, the α of the general disaster area is 1, and the area is a hexagonal grid; the α of the major disaster area is 2, and the area is a parallelogram grid that divides the hexagonal grid into three equal parts; the α of the core disaster area is 3, and the area is a triangular grid that divides the parallelogram grid into two equal parts; the α of other non-disaster areas is 0.

[0052] This embodiment uses a fire to conduct an experiment, in which there are 9 transit satellites, and RADARSAT-1 is taken as an example to calculate the sub-satellite point coordinates of the satellite. According to the sub-satellite point coordinate calculation in step S2, the sub-satellite point coordinates Xr, Yr, and Zr of the RADARSAT-1 satellite can be obtained respectively by the following methods:

[0053]

[0054] Rotate the coordinate system X”Y”Z” around the Z” axis by ω r , get the coordinate system X'Y'Z',

[0055]

[0056] Rotate the coordinate system X'Y'Z' around the X' axis i r , rotate Ω around the Z' axis r Get the coordinate system XYZ,

[0057]

[0058] Among them, r r is the orbital height of RADARSAT-1 satellite, i r is the orbit inclination of RADARSAT-1 satellite, ω r is the perigee angular distance of RADARSAT-1 satellite, Ω r V is the right ascension of the ascending node of RADARSAT-1 satellite. r is the true anomaly of the RADARSAT-1 satellite.

[0059] In the embodiment of the present invention, a large-area multi-satellite coordinated rapid full-coverage observation method for disaster relief is provided, wherein the true anomaly angle, V r There is a formula:

[0060]

[0061] Among them, e r is the eccentricity of RADARSAT-1 satellite, E r is the anomaly angle of RADARSAT-1 satellite.

[0062] The satellite sub-satellite point prediction method based on real-time coverage provided in the embodiment of the present invention, the eccentric near point angle E r There is a formula:

[0063] E r -e r sin E r =n r (tT r )

[0064] Among them, e r is the eccentricity of RADARSAT-1 satellite, n r is the number of times the RADARSAT-1 satellite orbits the earth every day, t is the precise time, T r is the orbital period of RADARSAT-1 satellite.

[0065] Step S3: Divide satellite imaging of the earth into four-sided and five-sided imaging models according to the type of sensor imaging, and calculate the respective minimum observation functions.

[0066] According to the characteristics of the RADARSAT-1 satellite sensor's earth imaging, it can be known that it is a synthetic aperture radar (SAR), such as Figure 5 As shown, the satellite sub-satellite point trajectory is divided into a five-sided imaging model, and the minimum observation function f(Az,El) is set to determine the satellite imaging situation.

[0067] In the large-area multi-satellite coordinated rapid full-coverage observation method for disaster relief provided in an embodiment of the present invention, the minimum observation function of point A is expressed as:

[0068]

[0069] Among them, Az1 is the azimuth of point A in the satellite coordinate system, and El1 is the elevation of point A in the satellite coordinate system.

[0070] In the large-area multi-satellite coordinated rapid full-coverage observation method for disaster relief provided in an embodiment of the present invention, the azimuth and elevation angles of point A are expressed as follows:

[0071]

[0072] Among them, x1, y1, z1 are the coordinates of point A in the coverage zone.

[0073] In the large-area multi-satellite coordinated rapid full-coverage observation method for disaster relief provided in an embodiment of the present invention, the minimum observation function of point B is expressed as:

[0074]

[0075] Among them, Az2 is the azimuth of point B in the satellite coordinate system, and El2 is the elevation of point B in the satellite coordinate system.

[0076] In the large-area multi-satellite coordinated rapid full-coverage observation method for disaster relief provided in an embodiment of the present invention, the azimuth and elevation angles of point B are expressed as follows:

[0077]

[0078] Among them, x2, y2, z2 are the coordinates of point B in the coverage zone.

[0079] In the large-area multi-satellite coordinated rapid full-coverage observation method for disaster relief provided in an embodiment of the present invention, the minimum observation function of point C is expressed as:

[0080]

[0081] Among them, Az3 is the azimuth of point C in the satellite coordinate system, and El3 is the elevation of point C in the satellite coordinate system.

[0082] In the large-area multi-satellite coordinated rapid full-coverage observation method for disaster relief provided in an embodiment of the present invention, the azimuth and elevation angles of point C are given by the formula:

[0083]

[0084] Among them, x3, y3, z3 are the coordinates of point C in the coverage zone.

[0085] In the large-area multi-satellite coordinated rapid full-coverage observation method for disaster relief provided in an embodiment of the present invention, the minimum observation function of point D is expressed as:

[0086]

[0087] Among them, Az4 is the azimuth of point D in the satellite coordinate system, and El4 is the elevation of point D in the satellite coordinate system.

[0088] In the large-area multi-satellite coordinated rapid full-coverage observation method for disaster relief provided in an embodiment of the present invention, the azimuth and elevation angles of point D are given by the formula:

[0089]

[0090] Among them, x4, y4, z4 are the coordinates of point D in the coverage zone.

[0091] In the large-area multi-satellite coordinated rapid full-coverage observation method for disaster relief provided in an embodiment of the present invention, the minimum observation function of point E is expressed as:

[0092]

[0093] Among them, Az5 is the azimuth of point E in the satellite coordinate system, and El5 is the elevation of point E in the satellite coordinate system.

[0094] In the large-area multi-satellite coordinated rapid full-coverage observation method for disaster relief provided in an embodiment of the present invention, the azimuth and elevation angles of point E are given by the formula:

[0095]

[0096] Among them, x5, y5, z5 are the coordinates of point E in the coverage zone.

[0097] According to the above formula, the azimuth and elevation of each vertex can be calculated, and the RADARSAT satellite imaging model can be obtained.

[0098] In other non-SAR imaging embodiments, the satellite adopts a quadrilateral imaging model, such as Figure 4 shown.

[0099] Step S4: Calculate the Earth observation coverage model of each satellite using the line of sight equation

[0100] Step S5: Taking the calculated satellite earth observation coverage model as the optimization target, an improved adaptive evolutionary search algorithm is used to search for a satellite collaborative observation solution that can fully cover the target observation area in the shortest time.

[0101] Based on the content of the above method embodiment, as an optional embodiment, the large-area multi-satellite coordinated rapid full-coverage observation method for disaster relief provided in the embodiment of the present invention, the step S5 uses the calculated satellite earth observation coverage belt model as the optimization target, adopts an improved adaptive evolutionary search algorithm, and searches for a satellite coordinated observation scheme that can fully cover the target observation area in the shortest time, including:

[0102] Step S51, generate a total population S and an initial population S', and generate an observation time T = 0, the population S contains M individuals, the initial value in S' is 0, each individual corresponds to a ground coverage belt model of a satellite, each individual includes 1 chromosome, the chromosome is composed of N quantum bits and a time parameter t, one quantum bit corresponds to a ground coverage belt of a sensor in the satellite, the time parameter t stores the observation time of a single ground coverage belt model, the time length T is set as the optimal observation target, and the probability amplitudes k of all quantum bits in the initial population are initialized to 0;

[0103] Step S52, measure the population individuals: compare the probability amplitude k of the quantum bit with the disaster coefficient α of the ground observation area grid. If the disaster coefficient α is greater than the probability amplitude k of the quantum bit and α≠4, the measurement value hi of the quantum bit is +1, indicating that the observation task of the target grid is performed. Otherwise, the measurement value h of the quantum bit remains unchanged, indicating that the observation task of the target grid is not performed. When the target grid completes α observations, α=4, indicating that the observation is completed.

[0104] Step S53, calculate the final value of the measured value h of the individuals in the population, h = ∑hi, take the size of the final measured value h and the size of the time parameter t as the optimization target, and retain the best individual, consider the maximum h and the minimum t of each individual at the same time, the individual corresponding to the highest comprehensive consideration is the best individual, and input the individual into S', and add the t corresponding to the individual to T;

[0105] Step S54: determine whether the individual termination condition is triggered, that is, all quantum bits in the same individual have been detected. If triggered, jump to step S55; if not triggered, continue to use the adaptive evolution method to search for the individual;

[0106] Step S55, determine whether the population termination condition is triggered, that is, all individuals in S are retrieved, and all α=4 of the grids with original α≠0 in the target observation area. If not triggered, directly return to step S52. If triggered, end the iteration and output all the optimal satellite combinations and the shortest observation time T in the population S'. The analysis results are as follows Figure 6 shown.

[0107] The working principle of the present invention is: the present invention establishes a model through multi-level hexagonal discretization technology and adaptive evolutionary search algorithm to solve the fastest observation plan that meets full coverage of the target area, thereby greatly improving the timeliness of disaster relief.

[0108] The flowcharts and block diagrams in the accompanying drawings show methods, functions and operations according to multiple embodiments of the present invention. Based on this understanding, each box in the flowchart or block diagram can represent a part of a module, and a part of the module contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the boxes can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and sometimes in the opposite order, depending on the functions involved.

[0109] In this patent, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process or method including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "includes..." do not exclude the presence of other identical elements in the process or method including the elements.

[0110] What is described above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A large-area multi-satellite coordinated rapid full-coverage observation system for disaster relief, characterized in that: The invention comprises an algorithm model, wherein the algorithm model comprises a multi-level hexagonal discretization model and an adaptive evolutionary search algorithm, and the algorithm model is used for performing full coverage solution calculations in the target area and obtaining the fastest observation plan.

2. The method for large-area multi-satellite coordinated rapid full coverage observation for disaster relief according to claim 1, characterized in that: include Step S1: input the target observation area and perform multi-level hexagonal discretization processing; Step S2: Read in the satellite orbit parameters and use six numbers to calculate the real-time satellite sub-satellite point coordinates; Step S3: Divide satellite earth imaging into four-sided and five-sided imaging models according to the type of sensor imaging, and calculate the minimum observation function of each model; Step S4: Calculate the earth observation coverage model of each satellite using the line of sight equation; Step S5: Taking the calculated satellite earth observation coverage model as the optimization target, an improved adaptive evolutionary search algorithm is used to search for a satellite collaborative observation solution that can fully cover the target observation area in the shortest time.

3. The method for large-area multi-satellite coordinated rapid full coverage observation for disaster relief according to claim 2 is characterized in that: The step S1 includes Step S11: evenly divide the target disaster area into hexagonal grids; Step S12: setting disaster level coefficients, and defining level coefficients for general disaster areas, major disaster areas, core disaster areas, and other disaster areas respectively; Step S13: Setting disaster level grids, and planning polygonal grids for general disaster areas, major disaster areas, and core disaster areas respectively.

4. The method for large-area multi-satellite coordinated rapid full coverage observation for disaster relief according to claim 3 is characterized in that: The step S2 includes Step S21: sampling the sub-satellite point coordinates of the sampling satellite and establishing a sub-satellite point coordinate system; Step S22: quantitatively rotating the sub-satellite point coordinate system and obtaining a rotated coordinate system; Step S23: Substitute the rotating coordinate system into the true anomaly angle formula and the eccentric anomaly angle formula for calculation; Step S24: Obtain the eccentricity, revolution period, revolution time and satellite orbit period of the sampling satellite.

5. The method for large-area multi-satellite coordinated rapid full-coverage observation for disaster relief according to claim 4, characterized in that: The step S3 includes Step S31: selecting a synthetic aperture radar according to the earth imaging characteristics of the satellite sensor; Step S32: using synthetic aperture radar to divide the satellite sub-satellite trajectory into a model and set an observation function to determine the satellite imaging situation; Step S33: Calculate and integrate the azimuth and elevation angles of each vertex of the satellite according to the minimum observation function to calculate the satellite imaging model.

6. The method for large-area multi-satellite coordinated rapid full-coverage observation for disaster relief according to claim 5, characterized in that: The step S5 is to calculate using a satellite earth observation coverage model, specifically including: Step S51: Generate a total population S and an initial population S', and generate an observation time T = 0. The population S contains M individuals, the initial value in S' is 0, each individual corresponds to a ground coverage belt model of a satellite, and each individual includes 1 chromosome, which is composed of N quantum bits and a time parameter t. One quantum bit corresponds to a ground coverage belt of a sensor in the satellite. The time parameter t stores the observation time of a single ground coverage belt model. The time length T is set as the optimal observation target, and the probability amplitudes k of all quantum bits in the initial population are initialized to 0; Step S52: Measure the individuals in the population: compare the probability amplitude k of the quantum bit with the disaster coefficient α of the ground observation area grid. If the disaster coefficient α is greater than the probability amplitude k of the quantum bit and α≠4, the measurement value hi of the quantum bit is +1, indicating that the observation task of the target grid is performed. Otherwise, the measurement value h of the quantum bit remains unchanged, indicating that the observation task of the target grid is not performed. When the target grid completes α observations, α=4, indicating that the observation is completed. Step S53: Calculate the final value of the measured value h of the individuals in the population, h = ∑hi, take the size of the final measured value h and the size of the time parameter t as the optimization target, and retain the best individual, consider the maximum h and the minimum t of each individual at the same time, the individual corresponding to the highest comprehensive consideration is the best individual, and input the individual into S', and add the t corresponding to the individual to T; Step S54: Determine whether the individual termination condition is triggered, that is, all quantum bits in the same individual have been detected. If triggered, jump to the next step. If not triggered, continue to use the adaptive evolution method to search for the individual; Step S55: Determine whether the population termination condition is triggered, that is, all individuals in S are retrieved, and all α=4 in the original grids of α≠0 in the target observation area. If not triggered, return directly to step S52. If triggered, end the iteration and output all the optimal satellite combinations and the shortest observation time T in the population S'.

7. The method of large-area multi-satellite coordinated rapid full-coverage observation for disaster relief according to claim 5, characterized in that: Non-SAR imaging is also included, and the non-SAR imaging is calculated by a quadrilateral imaging model.

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