Satellite imaging and data transmission joint scheduling method based on brainstorm optimization

Abstract

The invention provides a satellite imaging and data transmission joint scheduling method based on brainstorm optimization. The method comprises the following steps: generating an initial request population; adjusting the execution sequence of each request in the initial request population to obtain a target request population; determining a conflict request population corresponding to the current request in the target request population, and performing iterative optimization on the conflict request population through a niche-based brainstorm optimization algorithm, so that the current request and the requests in the corresponding conflict request population are successfully scheduled; and removing the current request and the conflict request corresponding to the current request from the target request population, and continuing to determine a conflict request population corresponding to the next request in the new target request population until the target request population is empty, so as to complete the joint scheduling of satellite imaging and data transmission. According to the method, the feasibility and the effectiveness of the algorithm can be remarkably improved, so that the joint scheduling of satellite imaging and data transmission is realized.

Description

Technical Field

[0001] The present invention relates to the field of satellite technology, and in particular to a satellite imaging and data transmission joint scheduling method based on brainstorming optimization. Background Art

[0002] As the level of satellite hardware continues to improve, the number of user-specified requirements is also increasing. In order to meet the diverse and large-scale observation needs, efficient satellite mission scheduling technology is particularly critical. However, today's satellites are not only diverse but also complex in constraints, which makes the algorithm requirements for satellite mission planning gradually increase. In particular, as more and more satellites observe a large number of targets and seek to transmit data to ground stations, the research on satellite imaging and data transmission scheduling problems has become increasingly important.

[0003] Earth observation satellites are equipped with different onboard optical sensors to detect the earth's surface and obtain relevant information, playing an important role in many fields such as industry, science, and military. The main task of an earth observation satellite is to obtain images of designated targets on the ground and then download them to one or more ground stations. In general, the image data is first stored in the onboard recorder and then transmitted back after the satellite enters the range of the ground station, which is referred to as the storage-transmission mode. If the transmission payload sends the data obtained by the observation payload directly to the ground station in real time, it is referred to as the real transmission mode. The goal of Earth observation satellite task scheduling is to generate the best time-ordered activity sequence within a predetermined time range, arrange appropriate resources for each activity, and meet the mission objectives and user preferences. Since imaging and data transmission are difficult to take into account at the same time, the scheduling process of the above two operations is usually implemented separately on the basis of abstracting and simplifying the actual problem. Many mature and efficient algorithms have been produced for the single satellite imaging scheduling problem and the single satellite data transmission scheduling problem. With the improvement and introduction of various algorithms, the number of algorithms for satellite mission planning is also increasing, such as genetic algorithms, simulated annealing algorithms, etc. However, since most engineering problems are modeled as continuity or integer programming problems, the real constraints involved are not comprehensive enough, and the engineering requirements for the algorithms have become more stringent.

[0004] In summary, the increasing number of selectable time windows for satellite mission execution has brought more complex constraints to satellite mission planning, and most of the existing optimization algorithms cannot be well applied to engineering problems, which affects the effective and quality completion of satellite mission planning. There is a practical problem that there are many algorithms but few usable algorithms. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide a satellite imaging and data transmission joint scheduling method based on brainstorming optimization, which can significantly improve the feasibility and effectiveness of the algorithm, thereby realizing the joint scheduling of satellite imaging and data transmission.

[0006] In a first aspect, the present invention provides a satellite imaging and data transmission joint scheduling method based on brainstorming optimization, comprising:

[0007] For the requests to be scheduled, a joint scheduling scheme for satellite imaging and data transmission is generated in combination with the time window constraint. The joint scheduling scheme includes initial request populations corresponding to multiple satellites.

[0008] According to the target priority of each request in the initial request population, the execution order of each request in the initial request population is adjusted to obtain a target request population;

[0009] Determine the conflicting request population corresponding to the current request in the target request population, iteratively optimize the conflicting request population through a niche-based brainstorming optimization algorithm, so that the current request and the requests in its corresponding conflicting request population are successfully scheduled, and the individuals in the conflicting request population are used to describe the execution order of the conflicting requests corresponding to the current request;

[0010] The current request and its corresponding conflicting request are removed from the target request population, and the conflicting request population corresponding to the next request in the new target request population is continuously determined until the target request population is empty, thereby completing the joint scheduling of satellite imaging and data transmission.

[0011] In one implementation, the execution order of each request in the initial request population is adjusted according to the target priority of each request in the initial request population to obtain the target request population, including:

[0012] For any request in the initial request population, the ratio of the initial priority corresponding to the request to the observation time required for the request is used as the target priority corresponding to the request;

[0013] According to the target priority, the execution order of each request in the initial request population is arranged in descending order to obtain the target request population.

[0014] In one embodiment, the conflicting request population is iteratively optimized by a niche-based brainstorming optimization algorithm so that the current request and the requests in the corresponding conflicting request population are successfully scheduled, including:

[0015] Clustering the conflicting request population to obtain multiple conflicting request sub-populations;

[0016] The following operations are performed on each conflict request subpopulation: a random number set corresponding to the conflict request subpopulation is determined, one or more target individuals are determined from the conflict request subpopulation based on the random number set and a preset threshold set, and a crossover mutation operation is performed on each target individual to obtain a new conflict request subpopulation;

[0017] Each new conflicting request sub-population is merged to obtain a new conflicting request population. If the new conflicting request population does not meet the optimization goal, the new conflicting request population is clustered until the new conflicting request population meets the optimization goal, so that the current request and the requests in its corresponding conflicting request population can be successfully scheduled.

[0018] In one implementation, clustering is performed on the conflicting request population to obtain a plurality of conflicting request sub-populations, including:

[0019] For any individual in the conflict request population, a neighborhood region is constructed for the individual, and the similarity between the individual in the conflict request population and the individual in the neighborhood region is determined, and the conflict request subpopulation is obtained based on the similarity;

[0020] The individuals included in the conflict request sub-population are removed from the conflict request population;

[0021] For any individual in the new conflict request population, continue to construct the neighborhood area of ​​the individual until the conflict request population is empty, and obtain multiple conflict request sub-populations.

[0022] In one embodiment, the random number set includes a first random number and a second random number, and the preset threshold set includes an individual quantity threshold and an individual type threshold; based on the random number set and the preset threshold set, determining one or more target individuals from the conflict request subpopulation includes:

[0023] Determine whether the first random number is less than the individual quantity threshold;

[0024] If yes, then when the second random number is less than the individual type threshold, a central solution is selected from the conflict request subpopulation as the target individual; when the second random number is greater than or equal to the individual type threshold, a common solution is selected from the conflict request subpopulation as the target individual;

[0025] If not, then when the second random number is less than the individual type threshold, two central solutions are selected from the conflict request subpopulation as target individuals; when the second random number is greater than or equal to the individual type threshold, two common solutions are selected from the conflict request subpopulation as target individuals.

[0026] In one embodiment, the optimization objective is to maximize the total number of successful scheduling requests. The expression of the optimization objective is as follows:

[0027]

[0028] Among them, N R Represents the number of requests; N S Represents the number of satellites; and Represent the starting orbit circle and the ending orbit circle respectively; is a binary variable indicating whether request i is observed by satellite j in its mth orbit. indicates that request i is observed by satellite j in its mth orbit, Indicates that request i is not observed by satellite j in its mth orbit.

[0029] In one embodiment, the time window constraints include:

[0030] Each request must be scheduled within its user-specified window of opportunity;

[0031] Each imaging activity must be carried out within the satellite visible light imaging time window;

[0032] Each data transmission activity must be carried out within the data transmission time window visible to the ground station.

[0033] In a second aspect, the present invention further provides a satellite imaging and data transmission joint scheduling device based on brainstorming optimization, comprising:

[0034] A scheme generation module is used to generate a joint scheduling scheme for satellite imaging and data transmission in combination with a time window constraint for the request to be scheduled, wherein the joint scheduling scheme includes an initial request population corresponding to multiple satellites;

[0035] A population adjustment module, used to adjust the execution order of each request in the initial request population according to the target priority of each request in the initial request population, so as to obtain a target request population;

[0036] The first population optimization module is used to determine the conflicting request population corresponding to the current request in the target request population, and iteratively optimize the conflicting request population through a microhabitat-based brainstorming optimization algorithm so that the current request and the requests in its corresponding conflicting request population can be successfully scheduled. The individuals in the conflicting request population are used to describe the execution order of the conflicting requests corresponding to the current request;

[0037] The second population optimization module is used to remove the current request and its corresponding conflicting request from the target request population, and continue to determine the conflicting request population corresponding to the next request in the new target request population until the target request population is empty, so as to complete the joint scheduling of satellite imaging and data transmission.

[0038] In a third aspect, the present invention further provides an electronic device, comprising a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement any one of the methods provided in the first aspect.

[0039] In a fourth aspect, the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement any one of the methods provided in the first aspect.

[0040] The invention provides a satellite imaging and data transmission joint scheduling method based on brainstorming optimization. Firstly, for a request to be scheduled, a joint scheduling scheme of satellite imaging and data transmission is generated in combination with a time window constraint, wherein the joint scheduling scheme includes initial request populations corresponding to a plurality of satellites; then, according to the target priority of each request in the initial request population, the execution order of each request in the initial request population is adjusted to obtain a target request population; finally, a conflict request population corresponding to a current request in the target request population is determined, and the conflict request population is iteratively optimized through a microhabitat-based brainstorming optimization algorithm, so that the current request and the requests in the corresponding conflict request population are successfully scheduled, and the current request and the requests in the corresponding conflict request population are removed from the target request population, and the conflict request population corresponding to the next request in the new target request population is continuously determined until the target request population is empty, so as to complete the joint scheduling of satellite imaging and data transmission. The above method solves the combinatorial optimization problem of allocating imaging and data transmission resources within the request visible time window, and optimizes it through a niche-based brainstorming optimization algorithm to maximize the total number of successfully scheduled requests; at the same time, through simulation experiments on this method, the feasibility and effectiveness of the niche-based brainstorming optimization algorithm are proved, thereby realizing the scheduling of satellite imaging and data transmission.

[0041] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0042] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 A schematic diagram of a flow chart of a satellite imaging and data transmission joint scheduling method based on brainstorming optimization provided in an embodiment of the present invention;

[0045] Figure 2 A technical framework diagram of a satellite imaging and data transmission joint scheduling method based on brainstorming optimization provided by an embodiment of the present invention;

[0046] Figure 3 A schematic diagram of a basic solution process of a microhabitat-based brainstorming algorithm provided in an embodiment of the present invention;

[0047] Figure 4 A schematic diagram of the structure of a satellite imaging and data transmission joint scheduling device based on brainstorming optimization provided by an embodiment of the present invention;

[0048] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described in combination with the embodiments below. 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.

[0050] At present, the increasing number of selectable time windows for satellite mission execution has brought more complex constraints to satellite mission planning, and most of the existing optimization algorithms cannot be well applied to engineering problems, which affects the effective and quality completion of satellite mission planning. There is a practical problem that there are many algorithms but few usable algorithms.

[0051] Based on this, the present invention provides a method for joint scheduling of satellite imaging and data transmission based on brainstorming optimization to solve the problems of multiple satellite mission constraints and complex scenarios, significantly improve the feasibility and effectiveness of the algorithm, and thus realize the joint scheduling of satellite imaging and data transmission. There are deficiencies in solving satellite mission planning, which is generally established as an integer programming problem. The use of a microhabitat-based brainstorming algorithm to model the mission problem as an integer problem not only reduces the complexity of task modeling, but also can effectively complete the mission planning, ensuring that the planning result has a higher benefit. The embodiment of the present invention shows that the algorithm can achieve better results within a limited execution cycle on the basis of ensuring that the optimization target is satisfied.

[0052] To facilitate understanding of this embodiment, a satellite imaging and data transmission joint scheduling method based on brainstorming optimization disclosed in an embodiment of the present invention is first introduced in detail. Figure 1 The flowchart of a satellite imaging and data transmission joint scheduling method based on brainstorming optimization is shown, and the method mainly includes the following steps S102 to S108:

[0053] Step S102: generating a joint scheduling scheme for satellite imaging and data transmission in combination with the time window constraint for the request to be scheduled.

[0054] The time window constraints include: each request must be scheduled within the opportunity window specified by its user; each imaging activity must be performed within the satellite visible light imaging time window; each data transmission activity must be performed within the data transmission time window visible to the ground station. The joint scheduling scheme includes the initial request population corresponding to multiple satellites, which is used to describe the imaging action sequence and data transmission action sequence of each satellite and each ground station. The initial request population is used to describe the imaging action sequence and data transmission action sequence of a single satellite and each ground station.

[0055] Step S104, adjusting the execution order of each request in the initial request population according to the target priority of each request in the initial request population to obtain a target request population.

[0056] The target request population is the request population after the requests are sorted in descending order according to the target priority. In one example, each request in the initial request population has a different initial priority. After adjusting the initial priority using the observation time required for the request, the target priority corresponding to each request in each initial request population can be obtained. After sorting the execution order of each request in descending order according to the target priority, the target request population can be obtained.

[0057] Step S106, determining the conflicting request population corresponding to the current request in the target request population, and iteratively optimizing the conflicting request population by a microhabitat-based brainstorming optimization algorithm, so that the current request and the requests in the corresponding conflicting request population are successfully scheduled.

[0058] Among them, the individuals in the conflict request population are used to describe the execution order of the conflict request corresponding to the current request. In one example, the conflict request population can be clustered using a neighborhood-based population partitioning strategy to obtain multiple conflict request sub-populations; for each conflict request sub-population, a specified number (one or two) of target individuals (central solutions or common solutions) are selected from the conflict request sub-population, and cross-mutation is performed on them to generate new individuals, thereby obtaining a new conflict request population; with the maximization of the total number of successfully scheduled requests as the optimization goal, the new conflict request population is clustered using a neighborhood-based population partitioning strategy to obtain multiple new conflict request sub-populations, and this process is repeated so that the current request and the requests in its corresponding conflict request population are successfully scheduled.

[0059] Step S108, remove the current request and its corresponding conflicting request from the target request population, and continue to determine the conflicting request population corresponding to the next request in the new target request population until the target request population is empty, so as to complete the joint scheduling of satellite imaging and data transmission.

[0060] In one example, each request in the target request population is traversed. Assume that for the first request r in the target request population 1 (that is, the request with the highest target priority), determine the conflicting request population corresponding to the request, and after the request and its corresponding conflicting request are successfully scheduled, remove the successfully scheduled request from the target request population, continue to traverse the next request, and repeat the above process until the target request population is empty to complete the joint scheduling of satellite imaging and data transmission.

[0061] The satellite imaging and data transmission joint scheduling method based on brainstorming optimization provided by the embodiment of the present invention solves the combinatorial optimization problem of allocating imaging and data transmission resources within the visible time window of the request, and optimizes through the microhabitat-based brainstorming optimization algorithm to maximize the total number of successfully scheduled requests; at the same time, through the simulation experiment of the method, the feasibility and effectiveness of the microhabitat-based brainstorming optimization algorithm are proved, thereby realizing the scheduling of satellite imaging and data transmission.

[0062] To facilitate understanding, an embodiment of the present invention provides a specific implementation method of a satellite imaging and data transmission joint scheduling method based on brainstorming optimization, which is used to solve the problems of a large number of satellites, a large number of transmission tasks, and a large number of task constraints. These problems increase the difficulty of satellite mission planning and limit the feasibility of some algorithms.

[0063] First, establish a satellite set S = {1,2,…,N s}、Request set to be scheduled R = {1,2,…,N R}、Ground station set G={1,2,…,N G}.

[0064] Then, based on the number of requests N R 、Number of satellites s and the number of ground stations N g Establish an optimization goal to maximize the total number of successful scheduling requests within the planning range. The expression of the optimization goal is as follows:

[0065]

[0066] Among them, N R Represents the number of requests; N S Represents the number of satellites; and Represent the starting orbit circle and the ending orbit circle respectively; is a binary variable indicating whether request i is observed by satellite j in its mth orbit. indicates that request i is observed by satellite j in its mth orbit, Indicates that request i is not observed by satellite j in its mth orbit.

[0067] Finally, define the time window constraints:

[0068] The data transfer duration of request i depends on the data transfer method:

[0069]

[0070] Where R represents the number of requests; is the data transmission duration of request i; dM o is the imaging data rate; dM t is the transmission data rate; dur i is the observation time required for request i; Real i is a binary variable that indicates whether request i is observed and downloaded in real-transmit mode.

[0071] Each request must be scheduled within its user-specified window of opportunity:

[0072]

[0073] Where R represents the set of requests; S represents the set of satellites; G represents the set of ground stations; C j represents the orbit circle of satellite j; st i eti Respectively represent the earliest start time and the latest end time; i is the observation time required for request i; rist i is the start time of the observation activity for execution request i; is a binary variable indicating whether request i is observed by satellite j in its mth orbit; is a binary variable indicating whether the image data of request i is downloaded to ground station k by satellite j on its mth orbit circle.

[0074] Each imaging activity must be carried out within the satellite visible light imaging time window:

[0075]

[0076] Where R represents the set of requests; S represents the set of satellites; C j represents the orbit circle of satellite j; st i et i Respectively represent the earliest start time and the latest end time; i is the start time of the observation activity for execution request i; is a binary variable indicating whether request i is observed by satellite j in its mth orbit.

[0077] Each data transmission activity must be carried out within the data transmission time window visible to the ground station:

[0078]

[0079] Where R represents the set of requests; S represents the set of satellites; G represents the set of ground stations; C j represents the orbit circle of satellite j; st i et i Respectively represent the earliest start time and the latest end time; i is the observation time required for request i; rist i is the start time of the observation activity for execution request i; is a binary variable indicating whether request i is observed by satellite j in its mth orbit; is a binary variable indicating whether the image data of request i is downloaded to ground station k by satellite j on its mth orbit circle.

[0080] On this basis, the initial request population is generated. During the initialization process, in order to ensure the diversity of the initial request population, the algorithm is prevented from converging prematurely due to falling into a local optimum. The distribution of the initialized individuals during the search process should be more uniform. In the case of n satellites, m ground stations and l requests, assuming that each individual dimension corresponds to an integer randomly selected from [0, nm-l], initialize with l-dimensional nm individuals, where l must be less than nm.

[0081] Since the single satellite imaging scheduling problem and the single satellite imaging and data transmission scheduling problem have been proven to be NP-Hard problems, there is no polynomial time algorithm to solve the more complex satellite imaging and data transmission scheduling problems. Therefore, various meta-heuristic algorithms with global search capabilities are usually used to solve such complex optimization problems and obtain the optimal solution. As one of the traditional optimization algorithms, the brainstorming optimization algorithm is widely used to solve various optimization problems. In the present invention, the satellite imaging and data transmission scheduling problem is regarded as a combinatorial optimization problem of allocating imaging and data transmission resources within the visible time window of the request, and the standard framework of the brainstorming optimization algorithm is used to solve it. In this invention, the idea of ​​unified encoding of requests and resources is adopted to represent multiple dimensions of individuals, and on this basis, the concept of conflicting request sets is adopted to reduce time complexity and uniformly initialize the population. Based on this, the embodiment of the present invention uses a microhabitat-based brainstorming optimization algorithm to optimize the initial request population, which includes: initializing the population, evaluating the population, clustering the population, updating the population, and outputting the results. For details, see Figure 2 The technical framework diagram of a satellite imaging and data transmission joint scheduling method based on brainstorming optimization shown in FIG. 1 includes:

[0082] Step 1: Input the initial request population R and the initial resource configuration Resource. The initial resource configuration Resource includes available satellites, ground stations, and time window resources.

[0083] Step 2, Req=Sort(R). That is, the successfully scheduled request set P is an empty set, and the initial request population R is reordered to obtain the target request population Req.

[0084] The embodiment of the present invention provides a specific implementation method for reordering the initial request population R to obtain the target request population Req: for any request in the initial request population, the ratio of the initial priority corresponding to the request to the observation time required for the request is used as the target priority corresponding to the request; according to the target priority, the execution order of each request in the initial request population is arranged in descending order to obtain the target request population. Specifically, the calculation process of the target priority is as follows:

[0085]

[0086] Among them, p i ′ is the target priority of request i, p i is the initial priority of request i, dur i is the required observation time for request i.

[0087] Step 3, CReq = conflict_set (Req, r1). CReq is a set of conflicting requests corresponding to the first request r1 in the target request population, and a conflicting request population P for the set is generated. sort .

[0088] Step 4, Temp_P = NBSO (CReq, Resource). That is, based on the set of conflicting requests CReq and the initial resource configuration Resource, the conflicting request population is iteratively optimized using the niche-based brainstorming optimization algorithm, and Temp_P is the temporary population. Figure 3 The basic solution process diagram of a microhabitat-based brainstorming algorithm shown in the figure includes: calculating the fitness value of each individual, using the neighborhood-based microhabitat method to cluster and solve the disturbance, selecting one (or two) common solutions (or central solutions) to generate new solutions, and other cyclic processes.

[0089] In the specific implementation, please refer to the following (1) to (3):

[0090] (1) Clustering the conflict request population to obtain multiple conflict request subpopulations. In one example, a neighborhood-based population division strategy can be used for clustering. The clustering process is as follows: for any individual in the conflict request population, a neighborhood region is constructed for the individual, and the similarity between the individual in the conflict request population and the individual in the neighborhood region is determined, and the conflict request subpopulation is obtained based on the similarity; the individuals included in the conflict request subpopulation are removed from the conflict request population; for any individual in the new conflict request population, the neighborhood region is continued to be constructed for the individual until the conflict request population is empty, and multiple conflict request subpopulations are obtained.

[0091] The embodiment of the present invention adopts a population division based on a domain to cluster the population, replacing the k-means clustering algorithm in the traditional algorithm, to prevent the population from falling into a local optimum. Specifically, a population division strategy based on a neighborhood is adopted for clustering, and the t most similar individuals in the conflict request population are found as a conflict request sub-population, and then t individuals are removed from the conflict request population. Repeating the above steps, the conflict request population will finally be divided into multiple conflict request sub-populations.

[0092] (2) Perform the following operations on each conflict request subpopulation: determine the random number set corresponding to the conflict request subpopulation, determine one or more target individuals from the conflict request subpopulation based on the random number set and the preset threshold set, perform a crossover mutation operation on each target individual, and obtain a new conflict request subpopulation. The random number set includes a first random number c 1 and the second random number c 2 , c 1 and c 2 is a random value between 0 and 1. The preset threshold set includes the individual number threshold p one and individual type threshold p center , the individual number threshold p one Used to determine the number of individuals to be extracted and the individual type threshold p center The type of individual to be extracted (common solution or central solution) can be obtained by data training.

[0093] For specific implementation, please refer to (a) to (c) below:

[0094] (a) determining whether the first random number is less than the individual quantity threshold;

[0095] (b) if yes, then when the second random number is less than the individual type threshold, select a central solution from the conflict request subpopulation as the target individual; when the second random number is greater than or equal to the individual type threshold, select a common solution from the conflict request subpopulation as the target individual;

[0096] (c) If not, then when the second random number is less than the individual type threshold, two central solutions are selected from the conflict request subpopulation as target individuals; when the second random number is greater than or equal to the individual type threshold, two common solutions are selected from the conflict request subpopulation as target individuals.

[0097] Please continue to see Figure 3 , if the first random number c 1 Less than p one , which means selecting one individual for crossover mutation, otherwise selecting two individuals for crossover mutation operation; if the second random number c 2 Less than p center , indicating that the central solution is selected for crossover and mutation operation, otherwise the common solution is selected for crossover and mutation operation.

[0098] (3) Each new conflicting request sub-population is merged to obtain a new conflicting request population. If the new conflicting request population does not meet the optimization goal, the new conflicting request population is clustered until the new conflicting request population meets the optimization goal, so that the current request and the requests in its corresponding conflicting request population can be successfully scheduled.

[0099] Step 5, Req = Req - CReq; Resource = Rnew (Resource, Temp_P); P = P + Temp_P. That is, the target request population, resource configuration and the set of requests that have been scheduled are updated.

[0100] Step 6, judgement If not, execute step 3 to iteratively optimize the conflicting request corresponding to the next request in the updated target population; if yes, output P.

[0101] The embodiment of the present invention takes maximizing the total number of successful scheduling requests as the algorithm optimization goal, and through continuous iterative evolution, selects individuals with a large total number of successful scheduling requests to update the population. Within a limited execution cycle, through the iterative process of initializing the population, evaluating individuals, clustering the population, updating the population, and outputting the final result, a satisfactory optimization goal is output.

[0102] In summary, this method can solve the problem that more and more satellites observe a large number of targets and seek to transmit them to ground stations. This method combines integer programming modeling and the global search capability of the brainstorming optimization algorithm to determine the imaging action sequence and data transmission action sequence of each satellite and each ground station, thereby accurately and reasonably planning the satellite data transmission tasks. The simulation experiment results verify the feasibility and effectiveness of this method, and the present invention can more effectively realize satellite imaging and data transmission scheduling.

[0103] Based on the above embodiments, the present invention provides a satellite imaging and data transmission joint scheduling device based on brainstorming optimization, see Figure 4 The structure diagram of a satellite imaging and data transmission joint scheduling device based on brainstorming optimization is shown in FIG. The device mainly includes the following parts:

[0104] A scheme generating module 402 is used to generate a joint scheduling scheme for satellite imaging and data transmission in combination with a time window constraint for a request to be scheduled, wherein the joint scheduling scheme includes an initial request population corresponding to a plurality of satellites;

[0105] A population adjustment module 404 is used to adjust the execution order of each request in the initial request population according to the target priority of each request in the initial request population to obtain a target request population;

[0106] The first population optimization module 406 is used to determine the conflicting request population corresponding to the current request in the target request population, and iteratively optimize the conflicting request population by a microhabitat-based brainstorming optimization algorithm, so that the current request and the requests in the corresponding conflicting request population are successfully scheduled, and the individuals in the conflicting request population are used to describe the execution order of the conflicting requests corresponding to the current request;

[0107] The second population optimization module 408 is used to remove the current request and its corresponding conflicting request from the target request population, and continue to determine the conflicting request population corresponding to the next request in the new target request population until the target request population is empty, so as to complete the joint scheduling of satellite imaging and data transmission.

[0108] The satellite imaging and data transmission joint scheduling device based on brainstorming optimization provided by the embodiment of the present invention solves the combinatorial optimization problem of allocating imaging and data transmission resources within the visible time window of the request, and optimizes through the microhabitat-based brainstorming optimization algorithm to maximize the total number of successfully scheduled requests; at the same time, through the simulation experiment of the method, the feasibility and effectiveness of the microhabitat-based brainstorming optimization algorithm are proved, thereby realizing the scheduling of satellite imaging and data transmission.

[0109] In one implementation, the population adjustment module 404 is specifically configured to:

[0110] For any request in the initial request population, the ratio of the initial priority corresponding to the request to the observation time required for the request is used as the target priority corresponding to the request;

[0111] According to the target priority, the execution order of each request in the initial request population is arranged in descending order to obtain the target request population.

[0112] In one implementation, the first population optimization module 406 is specifically configured to:

[0113] Clustering the conflicting request population to obtain multiple conflicting request sub-populations;

[0114] The following operations are performed on each conflict request subpopulation: a random number set corresponding to the conflict request subpopulation is determined, one or more target individuals are determined from the conflict request subpopulation based on the random number set and a preset threshold set, and a crossover mutation operation is performed on each target individual to obtain a new conflict request subpopulation;

[0115] Each new conflicting request sub-population is merged to obtain a new conflicting request population. If the new conflicting request population does not meet the optimization goal, the new conflicting request population is clustered until the new conflicting request population meets the optimization goal, so that the current request and the requests in its corresponding conflicting request population can be successfully scheduled.

[0116] In one implementation, the first population optimization module 406 is specifically configured to:

[0117] For any individual in the conflict request population, a neighborhood region is constructed for the individual, and the similarity between the individual in the conflict request population and the individual in the neighborhood region is determined, and the conflict request subpopulation is obtained based on the similarity;

[0118] The individuals included in the conflict request sub-population are removed from the conflict request population;

[0119] For any individual in the new conflict request population, continue to construct the neighborhood area of ​​the individual until the conflict request population is empty, and obtain multiple conflict request sub-populations.

[0120] In one embodiment, the random number set includes a first random number and a second random number, and the preset threshold set includes an individual quantity threshold and an individual type threshold; the first population optimization module 406 is specifically used to:

[0121] Determine whether the first random number is less than the individual quantity threshold;

[0122] If yes, then when the second random number is less than the individual type threshold, a central solution is selected from the conflict request subpopulation as the target individual; when the second random number is greater than or equal to the individual type threshold, a common solution is selected from the conflict request subpopulation as the target individual;

[0123] If not, then when the second random number is less than the individual type threshold, two central solutions are selected from the conflict request subpopulation as target individuals; when the second random number is greater than or equal to the individual type threshold, two common solutions are selected from the conflict request subpopulation as target individuals.

[0124] In one embodiment, the optimization objective is to maximize the total number of successful scheduling requests. The expression of the optimization objective is as follows:

[0125]

[0126] Among them, N R Represents the number of requests; N S Represents the number of satellites; and Represent the starting orbit circle and the ending orbit circle respectively; is a binary variable indicating whether request i is observed by satellite j in its mth orbit. indicates that request i is observed by satellite j in its mth orbit, Indicates that request i is not observed by satellite j in its mth orbit.

[0127] In one embodiment, the time window constraints include:

[0128] Each request must be scheduled within its user-specified window of opportunity;

[0129] Each imaging activity must be carried out within the satellite visible light imaging time window;

[0130] Each data transmission activity must be carried out within the data transmission time window visible to the ground station.

[0131] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0132] An embodiment of the present invention provides an electronic device. Specifically, the electronic device includes a processor and a storage device. The storage device stores a computer program, and when the computer program is executed by the processor, it executes the method described in any one of the above-mentioned implementation methods.

[0133] Figure 5 A structural diagram of an electronic device provided in an embodiment of the present invention, the electronic device 100 includes: a processor 50, a memory 51, a bus 52 and a communication interface 53, wherein the processor 50, the communication interface 53 and the memory 51 are connected via the bus 52; the processor 50 is used to execute an executable module stored in the memory 51, such as a computer program.

[0134] The memory 51 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 53 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0135] The bus 52 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0136] Among them, the memory 51 is used to store programs, and the processor 50 executes the program after receiving the execution instruction. The method executed by the device for flow process definition disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 50 or implemented by the processor 50.

[0137] The processor 50 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 50. The above processor 50 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor to be executed, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 51, and the processor 50 reads the information in the memory 51 and completes the steps of the above method in combination with its hardware.

[0138] The computer program product of the readable storage medium provided in the embodiment of the present invention includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the methods described in the previous method embodiments. The specific implementation can be referred to the previous method embodiments, which will not be repeated here.

[0139] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, 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. The computer software product is stored in a storage medium, including 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 methods 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 codes.

[0140] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A satellite imaging and data transmission joint scheduling method based on brainstorming optimization, characterized in that: include: For the request to be scheduled, a joint scheduling scheme for satellite imaging and data transmission is generated in combination with a time window constraint, wherein the joint scheduling scheme includes an initial request population corresponding to a plurality of satellites; According to the target priority of each request in the initial request population, the execution order of each request in the initial request population is adjusted to obtain a target request population; Determine a conflicting request population corresponding to a current request in the target request population, and iteratively optimize the conflicting request population by a microhabitat-based brainstorming optimization algorithm so that the current request and the requests in the corresponding conflicting request population are successfully scheduled, and the individuals in the conflicting request population are used to describe the execution order of the conflicting requests corresponding to the current request; The current request and its corresponding conflicting request are removed from the target request population, and the conflicting request population corresponding to the next request in the new target request population is continuously determined until the target request population is empty, so as to complete the joint scheduling of satellite imaging and data transmission.

2. The satellite imaging and data transmission joint scheduling method based on brainstorming optimization according to claim 1 is characterized in that: According to the target priority of each request in the initial request population, the execution order of each request in the initial request population is adjusted to obtain a target request population, including: For any request in the initial request population, a ratio of the initial priority corresponding to the request to the observation time required for the request is used as the target priority corresponding to the request; According to the target priority, the execution order of each request in the initial request population is arranged in descending order to obtain a target request population.

3. The satellite imaging and data transmission joint scheduling method based on brainstorming optimization according to claim 1 is characterized in that: Iteratively optimizing the conflicting request population by a niche-based brainstorming optimization algorithm so that the current request and the corresponding requests in the conflicting request population are successfully scheduled, including: Clustering the conflicting request population to obtain a plurality of conflicting request sub-populations; Performing the following operations on each of the conflict request subpopulations: determining a random number set corresponding to the conflict request subpopulation, determining one or more target individuals from the conflict request subpopulation based on the random number set and a preset threshold set, performing a crossover mutation operation on each of the target individuals to obtain a new conflict request subpopulation; Each new conflicting request sub-population is merged to obtain a new conflicting request population. If the new conflicting request population does not meet the optimization goal, the new conflicting request population is clustered until the new conflicting request population meets the optimization goal, so that the current request and the corresponding request within the conflicting request population are successfully scheduled.

4. The satellite imaging and data transmission joint scheduling method based on brainstorming optimization according to claim 3 is characterized in that: The conflicting request population is clustered to obtain a plurality of conflicting request sub-populations, including: For any individual in the conflict request population, construct a neighborhood area for the individual, determine the similarity between the individual in the conflict request population and the individual in the neighborhood area, and obtain a conflict request subpopulation based on the similarity; Eliminate the individuals included in the conflict request subpopulation from the conflict request population; For any individual in the new conflict request population, continue to construct a neighborhood area for the individual until the conflict request population is empty, thereby obtaining a plurality of conflict request sub-populations.

5. The satellite imaging and data transmission joint scheduling method based on brainstorming optimization according to claim 3 is characterized in that: The random number set includes a first random number and a second random number, and the preset threshold set includes an individual quantity threshold and an individual type threshold; Based on the random number set and the preset threshold set, determining one or more target individuals from the conflict request subpopulation includes: Determine whether the first random number is less than the individual quantity threshold; If yes, then when the second random number is less than the individual type threshold, a central solution is selected from the conflict request subpopulation as the target individual; when the second random number is greater than or equal to the individual type threshold, a common solution is selected from the conflict request subpopulation as the target individual; If not, then when the second random number is less than the individual type threshold, two central solutions are selected from the conflict request subpopulation as target individuals; when the second random number is greater than or equal to the individual type threshold, two common solutions are selected from the conflict request subpopulation as target individuals.

6. The satellite imaging and data transmission joint scheduling method based on brainstorming optimization according to claim 3 is characterized in that: The optimization goal is to maximize the total number of successful scheduling requests. The expression of the optimization goal is as follows: Among them, N R Represents the number of requests; N S Represents the number of satellites; and Represent the starting orbit circle and the ending orbit circle respectively; is a binary variable indicating whether request i is observed by satellite j in its mth orbit. indicates that request i is observed by satellite j in its mth orbit, Indicates that request i is not observed by satellite j in its mth orbit.

7. The satellite imaging and data transmission joint scheduling method based on brainstorming optimization according to claim 1 is characterized in that: The time window constraints include: Each request must be scheduled within its user-specified window of opportunity; Each imaging activity must be carried out within the satellite visible light imaging time window; Each data transmission activity must be carried out within the data transmission time window visible to the ground station.

8. A satellite imaging and data transmission joint scheduling device based on brainstorming optimization, characterized in that: include: A scheme generation module is used to generate a joint scheduling scheme for satellite imaging and data transmission in combination with a time window constraint for a request to be scheduled, wherein the joint scheduling scheme includes an initial request population corresponding to a plurality of satellites; A population adjustment module, used to adjust the execution order of each request in the initial request population according to the target priority of each request in the initial request population to obtain a target request population; A first population optimization module is used to determine a conflicting request population corresponding to a current request in the target request population, and iteratively optimize the conflicting request population by a microhabitat-based brainstorming optimization algorithm, so that the current request and the requests in the corresponding conflicting request population are successfully scheduled, and the individuals in the conflicting request population are used to describe the execution order of the conflicting requests corresponding to the current request; The second population optimization module is used to remove the current request and its corresponding conflicting request from the target request population, and continue to determine the conflicting request population corresponding to the next request in the new target request population until the target request population is empty, so as to complete the joint scheduling of satellite imaging and data transmission.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the method according to any one of claims 1 to 7.

Images