Optical remote sensing satellite in-orbit cross calibration task optimization scheduling method and device

Abstract

The invention discloses an optical remote sensing satellite in-orbit cross calibration task optimization scheduling method and device, and the method comprises the steps: setting decision variables, including a calibration field selection number, an imaging time window selection number and an imaging time normalization coefficient, and minimizing the observation time difference and observation angle difference between a reference satellite and a to-be-calibrated satellite as an objective function; constructing an imaging time / imaging angle optimal matching optimization scheduling model; solving the constructed imaging time / imaging angle optimal matching optimization scheduling model by using an improved particle swarm optimization algorithm, and introducing different particle updating strategies to different types of decision variables in the task scheduling model; and according to a model solving result, restoring to obtain a corresponding calibration field, a reference satellite imaging moment and a to-be-calibrated satellite imaging moment, and outputting an optimized in-orbit cross calibration task scheduling scheme. The method can be effectively suitable for optimal scheduling of the on-orbit cross calibration task of the optical remote sensing satellite.

Description

Technical Field

[0001] The present invention relates to the field of satellite remote sensing technology, and in particular to an on-orbit cross-calibration task optimization scheduling method and equipment for optical remote sensing satellites. Background Art

[0002] In recent years, with the increasingly quantitative application of remote sensing, remote sensing monitoring of global resources and environmental changes, and the development of comprehensive application and quantitative analysis technology of multi-spectral and multi-satellite remote sensor data, people have gradually realized that remote sensing for the accurate acquisition of ground object target information fundamentally depends on the radiation calibration of the remote sensing payload performance and its state changes. The radiation calibration of remote sensing payloads generally includes pre-launch calibration and post-launch on-orbit calibration. However, since remote sensing payloads need to acquire remote sensing data in actual environments, they are affected by factors such as vibration during flight, working environment, and component aging. If the pre-launch calibration coefficients are still used for data processing, the accuracy of the remote sensing data products obtained by the remote sensing payload will be reduced. Therefore, it is necessary to perform on-orbit radiation calibration under real satellite on-orbit operating conditions to determine the remote sensing payload parameters in order to meet observation needs. The cross-calibration method of using a high-precision radiation satellite (reference satellite) and a satellite to be calibrated to observe the same ground target scene can be used to accurately calculate the uncertainties in each link of the transfer path, and transfer the radiation calibration coefficient of the reference satellite's high-precision space radiation measurement benchmark to the satellite to be calibrated, thereby increasing the calibration frequency and ensuring the radiation quality of the satellite to be calibrated throughout its life cycle. Therefore, when planning the on-orbit cross-calibration mission of optical remote sensing satellites, it is necessary to consider how to grasp the on-orbit cross-calibration requirements of optical remote sensing satellites and formulate a reasonable imaging mission plan.

[0003] Existing optical satellite mission planning mainly focuses on optimizing imaging coverage benefits. In their 2002 paper "Selecting and scheduling observations of agile satellites", Lemaître et al. simplified the mission planning problem to orbit selection and planning to realize the planning of the Pléiades satellite, established a constraint satisfaction model, and used greedy, local search, constraint programming and other algorithms to solve the model. In their 2020 paper "A multi-objective modeling method of multi-satellite imaging task planning for large regional mapping", Chen et al. constructed a planning model with orbit selection as a decision variable for the multi-satellite mission planning problem for large regional image acquisition, and used a non-dominated sorting genetic algorithm to solve the model. However, the optimal scheduling of satellite imaging tasks for on-orbit cross-calibration of optical remote sensing satellites aims to select an optimal matching area and observation time from multiple calibration fields that meet the observation time and observation geometry conditions, and no relevant scholars have studied it. Therefore, it is necessary to study and solve the relevant imaging task scheduling model in a targeted manner based on the observation requirements of on-orbit cross-calibration tasks of optical remote sensing satellites.

[0004] In view of the needs of on-orbit cross-calibration tasks for optical remote sensing satellites and the shortcomings of existing methods, new technical solutions are urgently needed in this field. Summary of the invention

[0005] In view of the problems in the prior art, the present invention proposes a technical solution for optimizing the scheduling of on-orbit cross-calibration tasks of optical remote sensing satellites.

[0006] The present invention provides an optimization scheduling method for an on-orbit cross-calibration task of an optical remote sensing satellite, comprising the following process: The decision variables in the optimal scheduling of the on-orbit cross calibration task of the optical remote sensing satellite are set to include the calibration field selection number, the imaging time window selection number and the imaging time normalization coefficient; The calibration field selection number, imaging time window selection number, and imaging time normalization coefficient are used as decision variables, and the observation time difference and observation angle difference between the reference star and the star to be calibrated are minimized as the objective function to build an imaging time / imaging angle optimal matching optimization scheduling model. The improved particle swarm optimization algorithm is used to solve the constructed imaging time / imaging angle optimal matching optimization scheduling model. The winner particles in the population are updated using a differential evolution strategy to generate winner' particles. The updated winner' particles are used to guide the update of loser particles. Different particle update strategies are introduced for different types of decision variables in the task scheduling model to ensure the stability of the solution. According to the model solution results, the corresponding calibration field, reference star imaging time, and to-be-calibrated star imaging time are restored, and the optimized on-orbit cross-calibration task scheduling plan is output.

[0007] Moreover, the imaging time normalization coefficient represents the time interval between the start time of the imaging time window for cross-radiation calibration of the reference star and the to-be-calibrated star determined by the calibration field selection number and the imaging time window selection number and the satellite on-orbit imaging time.

[0008] Moreover, the imaging time / imaging angle optimal matching optimization scheduling model adopts the formula and The optimization objectives represent minimizing the observation time difference and minimizing the observation angle difference respectively; in, , and are the observation angles of the reference star and the target star, respectively, which can be determined according to their respective observation times. and They respectively represent the observation times when the reference star and the target star are imaging the steady-state matching area determined by the decision variables.

[0009] Moreover, the improved particle swarm optimization algorithm is used to solve the constructed imaging time / imaging angle optimal matching optimization scheduling model, and the implementation method includes the following sub-steps: Step 3.1, randomly initialize the individuals in the population, including setting the population size and the maximum number of optimization iterations to be N and T , each individual in the population represents an optimal scheduling scheme for imaging tasks when the reference star and the star to be calibrated are cross-calibrated; Among them, the position of the particle represents the decision variable in the constructed imaging time / imaging angle optimal matching optimization scheduling model; the initial value and initial velocity of each particle are randomized within their respective value ranges; Step 3.2, calculate the optimal scheduling scheme for the cross-calibration task of the reference star and the to-be-calibrated star for each individual in the current optimization solution, and determine the individual with the best fitness value, where the fitness value is the observation time difference and the observation angle difference; Step 3.3, update the speed and position of individuals in the population, including adopting the following learning strategy, First, two different particles are randomly selected from the population, and the winner particle and the loser particle are distinguished according to the fitness values ​​of the two particles; Secondly, the differential evolution strategy is used to update the winner particle to generate the winner' particle; Finally, the updated speed and position of the winner' particle are used to guide the learning of the loser particle, and different iterative update methods are adopted for different types of decision variables: for continuous decision variables, the winner' particle and position and the average position of the population are used to update; for discrete decision variables, the bare-bone strategy is used to update only its position without updating the speed; Step 3.4, continue to optimize and solve, and obtain the optimal scheduling plan for the cross-calibration task of the reference star and the star to be calibrated with the smallest imaging time difference and the smallest imaging angle difference.

[0010] Moreover, when judging the fitness value, comparing two individuals A and B in the population, the imaging time difference of the two individuals is compared first. If the imaging time difference of A is smaller than the imaging time difference of B, the fitness value of A is better than that of B. If the imaging time difference of A and B is equal, the imaging angle difference of individuals A and B is compared. If the imaging angle difference of A is smaller than the imaging angle difference of B, the fitness value of A is better than that of B.

[0011] On the other hand, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for optimizing and scheduling the in-orbit cross-calibration task of the optical remote sensing satellite as described above is implemented.

[0012] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for optimizing the scheduling of in-orbit cross-calibration tasks of optical remote sensing satellites.

[0013] On the other hand, the present invention also provides a computer program product, including a computer program, characterized in that when the computer program is executed by a processor, the above-mentioned method for optimizing the scheduling of in-orbit cross-calibration tasks of optical remote sensing satellites is implemented.

[0014] Compared with the prior art, the present invention has the following advantages and beneficial effects: By taking the calibration field selection number, the imaging time window selection number and the imaging time normalization coefficient as decision variables, a one-to-one correspondence with the optimal scheduling scheme for the cross-calibration task of the reference star and the satellite to be calibrated is realized, and the integrated optimization modeling of "calibration field selection" and "satellite imaging timing grasp" is realized; constructing the minimization of imaging time difference and minimization of imaging angle difference as objective functions, the conversion of the cross-calibration imaging task requirements of the reference star and the satellite to be calibrated to modeling is realized; further, in view of the fact that the imaging time / imaging angle optimal matching model constructed by the present invention has two types of decision variables, discrete and continuous, and algorithm solution stability requirements, the present invention adopts a differential strategy to update the winner particles in the population to generate winner' particles, uses the position of the winner' particles and the average position of the population to guide the learning of the loser particles, and introduces an iterative method for different types of decision variables, effectively overcoming the problem that the standard PSO is prone to fall into the local optimal state and the adaptability to solving problems with multiple types of decision variables. The constructed model is solved by the improved hybrid particle swarm optimization algorithm to obtain the optimal scheduling scheme for the cross-calibration task of the reference satellite and the satellite to be calibrated. The present invention can be effectively applied to the optimal scheduling of the on-orbit cross-calibration task of the optical remote sensing satellite. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 This is a schematic diagram of observation time / angle according to an embodiment of the present invention; Figure 2 The figure is a flowchart of the steps of an embodiment of the present invention.

[0017] Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are 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.

[0019] See also Figure 3The embodiment of the present invention provides an optimal scheduling method for an on-orbit cross-calibration task of an optical remote sensing satellite, comprising the following steps: Step S1, clarifying the decision variables in the optimal scheduling of the on-orbit cross calibration task of the optical remote sensing satellite: calibration field selection number, imaging time window selection number, and imaging time normalization coefficient; The preferred implementation scheme of S1 in the embodiment is as follows: Directly using the imaging time of the reference satellite and the satellite to be calibrated as decision variables will reduce the efficiency of model solution. The present invention introduces three types of decision variables as decision variables of the model, including calibration field selection number, time window selection number, and imaging time normalization coefficient. The three types of decision variables can form a one-to-one mapping relationship with the satellite imaging time, thereby avoiding the consumption of model solution.

[0020] The first type of decision variable, the calibration field selection number ( ind ):

[0021] The multiple calibration sites of the optical remote sensing satellite on-orbit cross-calibration mission are numbered as {1,2, . .., n}, each number corresponds to the position of a calibration field, then the decision variable ind Select which calibration field to use to achieve the radiation calibration between the reference star and the star to be calibrated. n Indicates the calibration site number. ind Indicates the calibration field selection number.

[0022] The second type of decision variables: imaging time window selection number ( drti ):

[0023] The premise of cross-calibration between the reference satellite and the satellite to be calibrated is that there is an observable time window between the satellite and the calibration field. However, for the observation of a calibration field, there are often multiple possible imaging time windows. Therefore, it is necessary to introduce the imaging time window selection number to select the imaging time window for the satellite to be cross-calibrated. i To identify the i satellites, ref Indicates the reference star, c Indicates that the star is pending. li Indicates i The number of imaging time windows for satellites, k Indicates i Satellite k Imaging time window, drti express i The imaging time window number of the star is selected.

[0024] The third type of decision variables: normalization coefficient at imaging time ( mi,k ):

[0025] The normalization coefficient at the imaging moment is represented by the decision variable ind and drti The time interval between the start time of the imaging time window of the determined reference star and the to-be-calibrated star and the imaging time of the satellite on orbit. i Indicates i satellites, ref Indicates the reference star, c Indicates that the star is pending. li Indicates i The number of imaging time windows for satellites, k Indicates i Satellite k Imaging time window, mi,k Indicates i Satellite No. k Normalization coefficient of imaging moments in an imaging time window.

[0026] Step S2, constructing an imaging time / imaging angle optimal matching optimization scheduling model with the calibration field selection number, the imaging time window selection number, and the imaging time normalization coefficient as decision variables, minimizing the observation time difference and the observation angle difference between the reference star and the star to be calibrated as the objective function; The preferred implementation scheme of S2 in the embodiment is as follows: To ensure the best observation angle consistency and time consistency between the reference star and the target star ( Figure 1 ), build an imaging time / imaging angle optimal matching optimization scheduling model to achieve the selection of calibration field and the determination of satellite observation timing.

[0027] Considering that there are often multiple possible imaging time windows when the reference star and the calibration star are imaged, it is difficult to determine the upper and lower bounds of the time window when the optimization algorithm is used to solve the problem by directly using the imaging time as the decision variable. At the same time, there are often multiple possible observation targets when the reference star and the calibration star are imaged. This paper decomposes it into several sub-optimization problems and determines the optimal solution of each sub-problem, that is, determines the optimal observation time and observation angle for each observation target, so as to finally obtain the optimal solution under multiple possible observation target conditions by horizontally comparing the optimal solutions of all sub-problems.

[0028] The formalized digital model of the imaging time / imaging angle optimal matching optimization scheduling model constructed by the present invention is expressed as follows:

[0029]

[0030] st

[0031]

[0032]

[0033]

[0034] in, n Indicates the calibration site number. i Indicates i satellites, ref Indicates the reference star, c Indicates that the star is pending. li Indicates i Satellite li Imaging time window, k Indicates i Satellite k Imaging time window; formula , and They represent the value ranges of the decision variable calibration field selection number, the imaging time window selection number and the imaging time normalization coefficient respectively; formula The constraint condition represents the imaging time window constraint, Ti,k Indicates i Satellite No. k At a specific moment in an imaging time window, [ ITWi,k-s , ITWi,k-e ] indicates the i Satellite k Imaging time window, ITWi,k-s Indicates i Satellite k The starting time of the imaging time window, ITWi,k-e Indicates i Satellite k The end time of an imaging time window; formula and The optimization objectives represent minimizing the observation time difference and minimizing the observation angle difference respectively; , and are the observation angles of the reference star and the target star, respectively, which can be determined according to their respective observation times. and They represent the observation time when the reference star and the target star are imaging the steady-state matching area determined by the decision variable. The specific imaging time when the reference star and the target star are cross-radiation calibrated can be restored according to the following formula ( T i,k ).

[0035]

[0036] in, i Indicates i satellites, k Indicates i Satellite k Imaging time window, m i,k Indicates i Satellite No. k Normalization coefficient of imaging moments in an imaging time window.

[0037] Step S3, using the improved particle swarm optimization algorithm to solve the constructed imaging time / imaging angle optimal matching optimization scheduling model, using the differential evolution strategy to update the winner particle in the population to generate the winner' particle, using the updated winner' particle to guide the update of the loser particle, and introducing different particle update strategies for the discrete and continuous decision variables in the task scheduling model to ensure the stability of the algorithm when solving; The preferred implementation scheme of S3 in the embodiment is as follows: After clarifying the decision variables in the model through step 1 and building the optimal scheduling model through step 2, the model needs to be solved to obtain the final task planning solution. Considering the discontinuous and non-differentiable characteristics of the objective function of the constructed model, the present invention adopts a population-based evolutionary algorithm to solve it, and in view of the problems of poor diversity, easy to fall into local minima, slow convergence speed, weak stability, etc. of the classic population-based algorithm, a better performance scheduling model based on population evolutionary algorithm is studied and constructed.

[0038] The specific implementation of the hybrid particle swarm optimization algorithm for adaptive updating of decision variable types preferably constructed in the embodiment is as follows: Step 3.1, randomly initialize the individuals in the population; The population size and maximum number of optimization iterations when setting the algorithm to solve are N and T , each individual in the population represents an optimal scheduling scheme for imaging tasks when the reference star and the star to be calibrated are cross-calibrated; Among them, the position of the particle represents the decision variable in the imaging time / imaging angle optimal matching optimization scheduling model constructed in step 2; that is,indn,t , drti,n,t , mi,k,n,t , the initial value and initial velocity of each particle are randomized within their respective value ranges; in, t Indicates the algorithm solution t Suboptimal solution, t ∈{ 1, 2, ..., t, ..., T}, n Indicated in t Suboptimal solution N The first n Individuals, n ∈{ 1, 2, ..., n, ..., N}; indn,t Indicated in t Suboptimal solution N The first n The calibration field selection number of each individual; drti,n,t Indicated in t Suboptimal solution N The first n The individual i The imaging time window selection number of the satellite; mi,k,n,t Indicated in t Suboptimal solution N The first n The individual i Satellite k The imaging time normalization coefficient of each imaging time window; Step 3.2, calculate the optimal scheduling scheme for the cross-calibration task of the reference star and the star to be calibrated for each individual in the current optimization solution, and determine the individual with the best fitness value, where the fitness value is the observation time difference and the observation angle difference.

[0039] In the current optimization solution of the calculation, the optimal scheduling scheme for each individual reference star and the cross calibration task to be calibrated is: According to the decision variables ( indn,t , drti,n,t , mi,k,n,t ) and the imaging time / imaging angle optimal matching optimization scheduling model in step 2, determine t Optimal scheduling scheme for cross-calibration tasks between reference satellites and satellites to be calibrated during sub-optimal solution; The optimal individual with the determined fitness value is: In the population when solving the current optimization N The fitness value of each individual Fitt,n Sort and determine the individual with the best fitness value pbest , that is, the fitness value corresponding to the individual with the smallest observation time difference and the smallest observation angle difference Fitt,pbest ; in, pbest Indicated in t The individual with the best fitness value during the sub-optimal solution, Fit t,n Indicates that the algorithm t When solving the second optimization, the n The fitness value of each individual, Fit t,pbest Indicated in t The optimal individual in suboptimal solution pbest The corresponding fitness value; The fitness value can be judged in the following way: compare two individuals A and B in the population, and give priority to comparing the imaging time difference of the two individuals. If the imaging time difference of A is smaller than the imaging time difference of B, then the fitness value of A is better than that of B. If the imaging time difference of A and B is equal, then compare the imaging angle difference of individuals A and B. If the imaging angle difference of A is smaller than the imaging angle difference of B, then the fitness value of A is better than that of B.

[0040] Step 3.3, update the speed and position of individuals in the population; The present invention adopts a learning strategy different from the traditional PSO algorithm when updating individuals in the population as follows: First, two different particles are randomly selected from the population, and the winner particle and the loser particle are distinguished according to the fitness values ​​of the two particles; Secondly, the winner particle is updated to generate the winner' particle by using the differential evolution strategy; the differential evolution strategy can adopt the existing technology, generally mutation, crossover and selection strategy, which will not be described in detail in the present invention; Finally, the updated speed and position of the winner' particle are used to guide the learning of the loser particle, while taking into account the existence of discrete variables in the model constructed in step 2 ( ind n,t , drt i,n,t ) and continuous variables ( m i,k,n,t ), the present invention adopts different iterative update methods for different types of decision variables: For continuous decision variables ( m i,k,n,t ), using the winner' particle and position and the average position of the population to update; the implementation is as follows:

[0041] in, Indicates that the loser particle is t The speed in the iteration, Indicates that the loser particle is t+ The speed in 1 iteration, Indicates the winner particle updated by differential evolution strategy in the t The position in the iteration, Indicates that the loser particle is t The position in the iteration, Indicates that the loser particle is t+ The position in 1 iteration, Indicated in t The average position of all particles in the iteration, c 1, c 2 and c 3∈[0, 1] represents the acceleration factor, w represents the inertia weight, represents the optimal average weight.

[0042] For discrete decision variables ( ind n,t , drt i,n,t ), the bare-bone strategy is used to update only its position without updating the speed; the implementation is as follows:

[0043] in, N (0, 1) represents Gaussian normal distribution, Indicated in t The best position of all particles in iterations, Indicated in t The position of the loser particle in the iteration, g Indicated in t The average of the best position of all particles and the position of loser particles in the iteration, Indicated in t The standard deviation of the best position of all particles and the position of the loser particles in iterations.

[0044] Step 3.4, continue to optimize and solve, and obtain the optimal scheduling scheme for the cross calibration task of the reference star and the to-be-calibrated star with the minimum imaging time difference and the minimum imaging angle difference; When the number of optimizations is less than the maximum number of iterative optimizations, continue to execute steps 3.2 and 3.3, and calculate the fitness value of the best individual in the last optimization solution ( Fit t-1,pbest ) and the fitness value of the optimal individual of the current optimization solution ( Fit t,pbest ) for comparison, if Fit t-1,pbest Better than Fitt,pbest , then the fitness value of the best individual in the previous optimization solution is the optimal scheduling scheme for the cross-calibration task of the better benchmark star and the star to be calibrated. Conversely, the fitness value of the best individual in the current optimization solution is the optimal scheduling scheme for the cross-calibration task of the better benchmark star and the star to be calibrated. Continue to perform the optimization process until the maximum number of iterations is met. T , thus obtaining the optimal fitness value Fit T,gbest The optimization result corresponding to the individual is the optimal scheduling scheme for the cross-calibration task of the reference star and the star to be calibrated; in, pbest Indicated in t The individual with the best fitness value obtained by sub-optimization, Fit t,pbest Indicated in t Sub-optimization to find the best individual pbest The corresponding fitness value; Fit t-1,pbest Indicated in t-1 Sub-optimization to find the best individual pbest The corresponding fitness value; Fit T,gbest Indicates that the maximum number of iterative optimizations has been reached T When the global optimal individual gbest The corresponding fitness value is the optimal scheduling scheme for the cross-calibration task of the optimal reference satellite and the satellite to be calibrated; Step S4, reducing the obtained three types of decision variables to the corresponding calibration field, reference star imaging time, and to-be-calibrated star imaging time, to obtain an optimized on-orbit cross calibration task scheduling scheme.

[0045] The preferred implementation scheme of step S4 in the embodiment is as follows: Through the construction and solution of the model in steps 1, 2 and 3, an optimal scheduling scheme for the on-orbit cross-calibration task of optical remote sensing satellites is obtained. The scheme includes: The calibration field selected when the reference star and the to-be-calibrated star are cross-calibrated; the observation time difference of the reference star when cross-calibrating; the observation angle difference of the to-be-calibrated star when cross-calibrating; the observation time of the reference star and the to-be-calibrated star when cross-calibrating; in specific implementation, the above process can be realized by computer software technology to automatically run the process. The corresponding system device for running the process should also be within the protection scope of the present invention.

[0046] For the sake of reference, an example is now provided to illustrate the specific implementation of the embodiment of the present invention using 1 reference satellite, 1 to-be-calibrated satellite, and 10 calibration fields. The orbital parameters of the reference satellite and the to-be-calibrated satellite are shown in Table 1, and the geographical locations and global distribution of the 10 calibration fields are shown in Table 2.

[0047] Table 1 Orbital parameters of the reference star and the undetermined reference star

[0048] Table 2 Geographical location information of the 10 calibration sites

[0049] After building and solving the model in steps 1, 2 and 3, the final optimal scheduling scheme for the cross-calibration task of the reference satellite and the satellite to be calibrated is obtained as shown in Table 3.

[0050] Table 3. Optimal scheduling scheme for cross-calibration of reference satellites and satellites to be calibrated

[0051] Referring to Table 3, the selection of the calibration field and the determination of the observation timing when cross-calibrating the reference star and the star to be calibrated can be realized. The optical remote sensing satellite on-orbit cross-calibration task optimization scheduling device provided by the present invention is described below. The optical remote sensing satellite on-orbit cross-calibration task optimization scheduling device described below and the optical remote sensing satellite on-orbit cross-calibration task optimization scheduling method described above can be referred to each other.

[0052] Figure 3 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 3 As shown, the electronic device may include: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus. The processor may call the logic instructions in the memory to execute the optimization scheduling method for the on-orbit cross calibration task of the optical remote sensing satellite.

[0053] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0054] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the above-mentioned optical remote sensing satellite in-orbit cross-calibration task optimization scheduling method.

[0055] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the above-mentioned method for optimizing the scheduling of in-orbit cross-calibration tasks of optical remote sensing satellites.

[0056] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0057] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0058] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the scheduling of on-orbit cross-calibration tasks of optical remote sensing satellites, characterized in that: The process includes: The decision variables in the optimal scheduling of the on-orbit cross calibration task of the optical remote sensing satellite are set to include the calibration field selection number, the imaging time window selection number and the imaging time normalization coefficient; The calibration field selection number, imaging time window selection number, and imaging time normalization coefficient are used as decision variables, and the observation time difference and observation angle difference between the reference star and the star to be calibrated are minimized as the objective function to build an imaging time / imaging angle optimal matching optimization scheduling model. The improved particle swarm optimization algorithm is used to solve the constructed imaging time / imaging angle optimal matching optimization scheduling model. The winner particles in the population are updated using a differential evolution strategy to generate winner' particles. The updated winner' particles are used to guide the update of loser particles. Different particle update strategies are introduced for different types of decision variables in the task scheduling model to ensure the stability of the solution. According to the model solution results, the corresponding calibration field, reference star imaging time, and to-be-calibrated star imaging time are restored, and the optimized on-orbit cross-calibration task scheduling plan is output.

2. The method for optimizing the scheduling of on-orbit cross-calibration tasks of an optical remote sensing satellite according to claim 1, characterized in that: The imaging time normalization coefficient represents the time interval between the start time of the imaging time window for cross-radiation calibration of the reference star and the to-be-calibrated star determined by the calibration field selection number and the imaging time window selection number and the satellite on-orbit imaging time.

3. The method for optimizing the scheduling of on-orbit cross-calibration tasks of an optical remote sensing satellite according to claim 2, characterized in that: The imaging time / imaging angle optimal matching optimization scheduling model adopts the formula and minimizeβ are the optimization objectives, which respectively represent minimizing the observation time difference and minimizing the observation angle difference; Where β=|β ref -β c |,β ref and β c are the observation angles of the reference star and the target star, respectively, which can be determined according to their respective observation times. and They respectively represent the observation times when the reference star and the target star are imaging the steady-state matching area determined by the decision variables.

4. The method for optimizing the scheduling of on-orbit cross-calibration tasks of an optical remote sensing satellite according to claim 3, characterized in that: The improved particle swarm optimization algorithm is used to solve the constructed imaging time / imaging angle optimal matching optimization scheduling model, and the implementation method includes the following sub-steps: Step 3.1, randomly initialize the individuals in the population, including setting the population size and the maximum number of optimization iterations to N and T respectively. Each individual in the population represents an optimal scheduling scheme for imaging tasks when cross-calibrating a reference star and a star to be calibrated; Among them, the position of the particle represents the decision variable in the constructed imaging time / imaging angle optimal matching optimization scheduling model; the initial value and initial velocity of each particle are randomized within their respective value ranges; Step 3.2, calculate the optimal scheduling scheme for the cross-calibration task of the reference star and the to-be-calibrated star for each individual in the current optimization solution, and determine the individual with the best fitness value, where the fitness value is the observation time difference and the observation angle difference; Step 3.3, update the speed and position of individuals in the population, including adopting the following learning strategy, First, two different particles are randomly selected from the population, and the winner particle and the loser particle are distinguished according to the fitness values ​​of the two particles; Secondly, the differential evolution strategy is used to update the winner particle to generate the winner' particle; Finally, the updated speed and position of the winner' particle are used to guide the learning of the loser particle, and different iterative update methods are adopted for different types of decision variables: for continuous decision variables, the winner' particle and position and the average position of the population are used to update; for discrete decision variables, the bare-bone strategy is used to update only its position without updating the speed; Step 3.4, continue to optimize and solve, and obtain the optimal scheduling plan for the cross-calibration task of the reference star and the star to be calibrated with the smallest imaging time difference and the smallest imaging angle difference.

5. The method for optimizing the scheduling of on-orbit cross-calibration tasks of an optical remote sensing satellite according to claim 4, characterized in that: When judging the fitness value, compare two individuals A and B in the population, and give priority to comparing the imaging time difference of the two individuals. If the imaging time difference of A is smaller than the imaging time difference of B, the fitness value of A is better than that of B. If the imaging time difference of A and B is equal, compare the imaging angle difference of individuals A and B. If the imaging angle difference of A is smaller than the imaging angle difference of B, the fitness value of A is better than that of B.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method for optimizing the scheduling of on-orbit cross-calibration tasks of an optical remote sensing satellite as described in any one of claims 1 to 5 is implemented.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for optimizing the scheduling of on-orbit cross-calibration tasks of an optical remote sensing satellite as claimed in any one of claims 1 to 5 is implemented.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for optimizing the scheduling of on-orbit cross-calibration tasks of an optical remote sensing satellite as claimed in any one of claims 1 to 5 is implemented.

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