Wide remote sensing satellite constellation task allocation method and system based on particle swarm optimization

Abstract

The invention provides a wide remote sensing satellite constellation task allocation method and system based on a particle swarm algorithm, and relates to the technical field of remote sensing. The method comprises the following steps: performing time and space dimension decomposition on remote sensing observation requirements, performing access analysis on grid requirements by using a wide satellite constellation to generate candidate tasks, and on the basis, outputting a scheme with the highest requirement satisfaction degree in a particle swarm through multi-round iteration by means of a particle swarm algorithm and a greedy algorithm. The corresponding candidate remote sensing tasks are generated according to the characteristic of large breadth of the wide remote sensing satellite and the space-time attribute of the remote sensing observation requirement, the capability advantage of the wide remote sensing satellite can be brought into full play, and the generated remote sensing observation scheme can meet the requirements of large-area remote sensing observation and high time resolution.

Description

Technical Field

[0001] This invention relates to the field of remote sensing technology, and more specifically, to a method and system for allocating tasks for a wide-swath remote sensing satellite constellation based on particle swarm optimization. Background Technology

[0002] With the development of remote sensing technology, the number of remote sensing satellites with imaging swaths exceeding 100km is increasing. For example, the Pleiades-HR satellite or the GF-1 satellite in the Gaofen Earth Observation System have achieved wide-swath imaging, and some satellites have even achieved wide-swath meter-level resolution imaging. Constellations composed of wide-swath remote sensing satellites are an inevitable trend.

[0003] In the field of remote sensing satellite constellation mission allocation, research mainly focuses on ordinary wide-swath remote sensing satellites, with limited studies on wide-swath remote sensing satellite constellation mission allocation. Furthermore, the use of wide-swath remote sensing satellites is mostly limited to single-satellite deployments. Therefore, wide-swath remote sensing satellite constellation mission allocation suffers from the following problems: First, most remote sensing satellite constellation mission allocations do not consider the swath width characteristics of wide-swath remote sensing satellites, resulting in remote sensing observation schemes failing to fully utilize the advantages of wide-swath remote sensing satellites. Second, wide-swath remote sensing satellites used as single satellites cannot leverage the advantages of coordinated mission allocation under constellation conditions, causing remote sensing observation schemes to fail to simultaneously meet the requirements of large-area remote sensing observation and high temporal resolution. Summary of the Invention

[0004] The problem that this invention aims to solve is that existing remote sensing observation schemes fail to fully utilize the advantages of wide-swath remote sensing satellites and cannot simultaneously meet the requirements of large-area remote sensing observation and high temporal resolution.

[0005] To address the aforementioned problems, in a first aspect, this invention provides a method for allocating wide-swath remote sensing satellite constellation tasks based on particle swarm optimization, comprising: Initialize remote sensing observation requirements, wide-swath satellite constellation, and particle swarm state; The observation area is decomposed, the frequency requirement of remote sensing observation is decomposed into the time requirement of remote sensing observation, and the remote sensing observation requirement is decomposed into the spatiotemporal grid requirement using the remote sensing observation requirement decomposition model. Based on satellite access data, a remote sensing candidate task generation model is used to obtain remote sensing candidate tasks; Based on the particle swarm state, select the initial task grid and generate remote sensing observation schemes for each particle state at the current time. Based on the unmatched spatiotemporal grid requirements in the current remote sensing observation schemes for each particle state, determine the requirement satisfaction degree of the corresponding remote sensing observation schemes. Based on the current satisfaction of the remote sensing observation scheme requirements under each particle state, update the maximum satisfaction of each particle's requirements and the maximum satisfaction of the particle swarm. If the current iteration number is not equal to the preset iteration number, the iteration number is incremented by 1; Based on the maximum demand satisfaction of each particle and the maximum satisfaction of the particle swarm, the particle swarm state is updated according to the particle swarm state change model, and the remote sensing observation scheme under the updated particle state is generated again, and the maximum demand satisfaction of each particle and the maximum satisfaction of the particle swarm are updated. If the current iteration number is equal to the preset iteration number, output the remote sensing observation scheme with the maximum demand satisfaction in the particle swarm.

[0006] Optionally, the remote sensing observation demand decomposition model includes: in, and These represent the observation grids after Q-decomposition of the observation region. The central longitude and central latitude; and These represent the z-th observation grid. The start and end times of the demand for the y-th observation demand period. Indicates the start time of the observation requirement. Let X represent the duration of the observation requirement, and X represent the number of observations within that duration. Represents the observation grid The observation time of the y-th observation, where i represents the total number of observation grids after Q decomposition of the observation region.

[0007] Optionally, the remote sensing candidate task generation model includes: in, Indicates satellite For the i-th observation grid The time of the mth visit; and They represent satellites The observation start time and observation end time of the kth remote sensing candidate task Indicates satellite Maximum observation duration constraint; Indicates when satellite During the observation time of remote sensing candidate tasks Inner time, satellite The k-th remote sensing candidate task can cover the grid set; This represents the c-th observation grid. Indicates satellite For the c-th observation grid The time of the mth visit.

[0008] Optionally, the step of selecting an initial task grid and generating a remote sensing observation scheme for each particle state based on the particle swarm state includes: S401, Based on particle state Select observation grid As the initial task grid; S402. Select the time period of unmet observation requirements for the task grid. According to the remote sensing candidate task matching model based on the greedy algorithm, remote sensing candidate tasks are matched. ; S403, Based on remote sensing candidate tasks Observation time and the covered grid Based on the judgment conditions of spatiotemporal grid requirements, remove the spatiotemporal grid requirements that have been met, and complete the spatiotemporal grid requirement update; S404. Determine whether the spatiotemporal grid requirement has been traversed. If not, proceed to S405; otherwise, proceed to S406. S405. Select the grid with the highest demand in the spatiotemporal grid requirements as the task grid, and then proceed to S402. S406. Summarize the matched remote sensing candidate tasks and generate remote sensing observation schemes. The generation of remote sensing observation schemes for each particle state is now complete.

[0009] Optionally, the remote sensing candidate task matching model based on a greedy algorithm includes: in, and They represent satellites The observation start time and observation end time of the kth remote sensing candidate task This represents the Xth observation requirement time period of the task grid. Indicates satellite The kth remote sensing candidate task can cover the grid set. Represents the task grid; Satellite constraints are used to verify the observation time of the matching remote sensing candidate tasks. If the result is 1, the verification is successful; otherwise, it is unsuccessful. This represents the Kth remote sensing candidate task; This indicates the task with the most covered grid cells among the remote sensing candidate tasks.

[0010] Optionally, the spatiotemporal grid requirement satisfies the following judgment conditions: in, This represents the Xth observation requirement time period of the task grid. and They represent satellites The observation start time and observation end time of the kth remote sensing candidate task Indicates when satellite During the observation time of remote sensing candidate tasks Inner time, satellite The k-th remote sensing candidate task can cover the grid set; This represents the task grid.

[0011] Optionally, the particle swarm state change model includes: in, Indicated by observation grid The requirement satisfaction of generating remote sensing observation schemes under the initial task grid; , and These represent the first weighting coefficient, the second weighting coefficient, and the third weighting coefficient, respectively. Represents a random number between 0 and 1; This is the floor function.

[0012] Secondly, the present invention also provides a wide-swath remote sensing satellite constellation task allocation system based on particle swarm optimization algorithm, which implements the wide-swath remote sensing satellite constellation task allocation method based on particle swarm optimization algorithm as described above. The task allocation system includes: The parameter initialization module is used to initialize remote sensing observation requirements, wide-swath satellite constellation, and particle swarm state. The observation requirement decomposition module is used to decompose the observation area, decompose the remote sensing observation frequency requirement into the remote sensing observation time requirement, and use the remote sensing observation requirement decomposition model to decompose the remote sensing observation requirement into the spatiotemporal grid requirement. The candidate task generation module is used to obtain remote sensing candidate tasks based on satellite access information and using a remote sensing candidate task generation model. The observation scheme generation module is used to select the initial task grid based on the particle swarm state and generate remote sensing observation schemes for each particle state at the current time. The requirement satisfaction analysis module is used to determine the requirement satisfaction of the corresponding remote sensing observation scheme based on the unmatched spatiotemporal grid requirements in the remote sensing observation schemes under the current particle state. The demand satisfaction update module is used to update the maximum demand satisfaction of each particle and the maximum satisfaction of the particle swarm based on the demand satisfaction of the remote sensing observation scheme under the current state of each particle. The loop module is used to determine whether the current iteration number is equal to the preset iteration number. If yes, it switches to the observation scheme output module; otherwise, it increments the iteration number by 1 and switches to the particle swarm update module. The particle swarm update module is used to update the particle swarm state based on the maximum demand satisfaction of each particle and the maximum demand satisfaction of the particle swarm, according to the particle swarm state change model, and then transfers to the observation scheme generation module, demand satisfaction analysis module, demand satisfaction update module and loop module. The observation scheme output module is used to output the remote sensing observation scheme with the maximum demand satisfaction in the particle swarm, and the task allocation ends.

[0013] Thirdly, the present invention provides an electronic device, including a memory and a processor; The memory is used to store computer programs; The processor is configured to, when executing the computer program, implement the wide-swath remote sensing satellite constellation task allocation method based on particle swarm optimization as described in the first aspect.

[0014] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the wide-swath remote sensing satellite constellation task allocation method based on particle swarm optimization as described in the first aspect.

[0015] This invention provides a method and system for mission allocation in a wide-swath remote sensing satellite constellation based on particle swarm optimization. Compared with existing technologies, it has the following advantages: By decomposing remote sensing observation requirements into temporal and spatial dimensions and analyzing grid requirements using a wide-swath satellite constellation, candidate tasks are generated. Based on this, a particle swarm optimization (PSO) algorithm and a greedy algorithm are employed, and through multiple iterations, the solution with the highest requirement satisfaction is output from the PSO pool. By fully leveraging the advantages of wide-swath remote sensing satellites, the generated remote sensing observation scheme can meet the requirements of large-area remote sensing observation and high temporal resolution. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating a wide-swath remote sensing satellite constellation task allocation method based on particle swarm optimization algorithm provided in an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the decomposition of remote sensing observation requirements provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of remote sensing candidate task generation provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of a wide-swath remote sensing satellite constellation task allocation system based on particle swarm optimization algorithm, provided as an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application are described clearly and completely. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0020] The technical solution in this application is to solve the above-mentioned technical problems, and the general idea is as follows: Considering the characteristics of wide-swath remote sensing satellites and the spatiotemporal characteristics of remote sensing mission observation requirements, and fully taking into account the degree of requirement satisfaction, the applicant designed a constellation task allocation method that can fully utilize the capabilities of wide-swath remote sensing satellites, based on particle swarm optimization (PSO) and greedy algorithms. This method implements efficient task allocation and improves the task satisfaction rate under the requirements of large-area remote sensing observation and high temporal resolution. Specifically, based on the known spatiotemporal requirements of remote sensing observation, the requirements are decomposed into spatiotemporal grid requirements. After analyzing the grid requirements using the wide-swath satellite constellation, candidate tasks are generated. On this basis, using PSO and greedy algorithms, through multiple rounds of iteration, the solution with the highest requirement satisfaction rate in the particle swarm is output.

[0021] like Figure 1 As shown in the embodiment of this application, a wide-swath remote sensing satellite constellation mission allocation method based on particle swarm optimization algorithm is provided, including: S1. Initialize remote sensing observation requirements, wide-swath satellite constellation, and particle swarm state, assuming the observation area is Q, the observation frequency is X times / T, and the particle swarm state... Wide-area satellite constellation The preset number of iterations is M, where, Indicates the first l One satellite.

[0022] S2, Decompose the observation region Q into The frequency requirement for remote sensing observations is broken down into the time requirement for remote sensing observations. Using a remote sensing observation demand decomposition model, remote sensing observation demands are decomposed into spatiotemporal grid demands. ,in, This represents the i-th observation grid. This represents the Xth observation requirement time period. This represents the Xth observation requirement time period for the i-th observation grid.

[0023] S3. Based on satellite access data, use the remote sensing candidate task generation model to obtain remote sensing candidate tasks. ,in, Indicates satellite The observation start time of the kth remote sensing candidate task. Indicates satellite The observation end time of the kth remote sensing candidate task. Indicates satellite The kth remote sensing candidate task can cover the grid.

[0024] S4. Based on the particle swarm state, select the initial task grid and generate the remote sensing observation scheme for each particle state at the current time.

[0025] S5. Based on the unmatched spatiotemporal grid requirements in the current remote sensing observation schemes under each particle state, determine the requirement satisfaction degree of the corresponding remote sensing observation schemes. The requirement satisfaction degree is the requirement of the matched spatiotemporal grids divided by the total number of spatiotemporal grids.

[0026] S6. Based on the demand satisfaction of the remote sensing observation scheme under the current particle states determined in S5, update the maximum demand satisfaction of each particle. and the maximum satisfaction of particle swarm ,in, This represents the maximum demand satisfaction in the nth particle state.

[0027] S7. Determine if the current iteration number j is equal to the preset iteration number M. If yes, proceed to S9; otherwise, ... And then transitioned to S8.

[0028] S8. Based on the maximum demand satisfaction of each particle and the maximum satisfaction of the particle swarm obtained in S6, update the particle swarm state according to the particle swarm state change model. Then switch to S4.

[0029] S9. Output the remote sensing observation scheme with the maximum demand satisfaction in the particle swarm, and the task allocation ends.

[0030] In this embodiment, remote sensing observation requirements are decomposed into temporal and spatial dimensions. Candidate tasks are generated by analyzing grid access requirements using a wide-swath satellite constellation. Based on this, a particle swarm optimization (PSO) algorithm and a greedy algorithm are employed, and through multiple iterations, the solution with the highest requirement satisfaction is output from the PSO pool. By fully leveraging the advantages of wide-swath remote sensing satellites, the generated remote sensing observation scheme can meet the requirements of large-area remote sensing observation and high temporal resolution.

[0031] The following is a detailed description of each step.

[0032] S1. Initialize remote sensing observation requirements, wide-swath satellite constellation, and particle swarm state, assuming the observation area is Q, the observation frequency is X times / T, and the particle swarm state... Wide-area satellite constellation The preset number of iterations is M, where, Indicates the first l One satellite.

[0033] Specifically, suppose the spatiotemporal grid requirement decomposition under a certain remote sensing observation requirement is as follows: Figure 2 As shown, the requirements for remote sensing observation areas Decomposed into The remote sensing observation frequency requirement X times / T is decomposed into Summarize and generate spatiotemporal grid requirements .

[0034] S2, Decompose the observation region Q into The frequency requirement for remote sensing observations is broken down into the time requirement for remote sensing observations. Using a remote sensing observation demand decomposition model, remote sensing observation demands are decomposed into spatiotemporal grid demands. ,in, This represents the i-th observation grid. This represents the Xth observation requirement time period. This represents the Xth observation requirement time period for the i-th observation grid.

[0035] Specifically, suppose a candidate remote sensing mission for a wide-swath remote sensing satellite is generated as follows: Figure 3 As shown, the observation time of the candidate wide-swath remote sensing satellite mission is known. By intersecting the observation time with the satellite's access time to the grid, the coverage grid for candidate remote sensing tasks at the observation time can be obtained. Similarly, the observation time can be obtained. Coverage grid for candidate remote sensing tasks .

[0036] The remote sensing observation demand decomposition model includes: Wherein, equation (1) represents the z-th observation grid after decomposition. In the observation area Inside, and These represent the observation grids after the observation region is decomposed. The center longitude and center latitude. Equations (2), (3), and (4) represent the methods for calculating the observation time requirements of the grid. and These represent the z-th observation grid. The start and end times of the demand for the y-th observation. X represents the start time of the observation requirement, and X represents the number of observations (i.e., the observation frequency) during the duration of the observation requirement. For the duration required for observation, Represents the observation grid The observation time of the y-th observation, where i represents the total number of observation grids after the Q-decomposition of the observation region, and y represents any number between 1 and X, i.e., the number of observation grids. Under frequency requirement X, the y-th observation requirement time period is obtained by time dimension decomposition.

[0037] S3. Based on satellite access data, use the remote sensing candidate task generation model to obtain remote sensing candidate tasks. ,in, Indicates satellite The observation start time of the kth remote sensing candidate task. Indicates satellite The observation end time of the kth remote sensing candidate task. Indicates satellite The kth remote sensing candidate task can cover the grid.

[0038] Specifically, the remote sensing candidate task generation model includes: Equation (5) represents the satellite The computational requirements for accessing the decomposed spatiotemporal grid. Indicates satellite For the i-th observation grid The time of the m-th visit. Equation (6) indicates that the observation duration of the remote sensing candidate task must meet the satellite observation duration constraint. and They represent satellites The observation start time and observation end time of the kth remote sensing candidate task Indicates satellite Maximum observation duration constraint. Indicates when satellite During the observation time of remote sensing candidate tasks Inner time, satellite The kth remote sensing candidate task can cover the grid set. This represents the c-th observation grid. Indicates satellite For the c-th observation grid The time of the mth visit.

[0039] S4. Based on the particle swarm state, select the initial task grid and generate remote sensing observation schemes for each particle state. This step specifically includes: S401, Based on particle state Select observation grid As the initial task grid.

[0040] S402. Select the time period of unmet observation requirements for the task grid. According to the remote sensing candidate task matching model based on the greedy algorithm, remote sensing candidate tasks are matched. .

[0041] Specifically, remote sensing candidate task matching models based on greedy algorithms include: Equation (8) represents the observation time that can be matched with remote sensing candidate tasks. With the observation period required There is overlap. and They represent satellites The observation start time and observation end time of the kth remote sensing candidate task. Equation (9) represents the coverable grid of the matching remote sensing candidate tasks. , Indicates satellite The kth remote sensing candidate task can cover the grid set. The task grid is represented by equation (10). Equation (10) indicates that the observation time for a matchable remote sensing candidate task must meet the requirements of the satellite. Satellite payload constraints, Satellite constraints are used to verify the observation time of the matching remote sensing candidate tasks. If the result is 1, the verification is passed; otherwise, it is not passed. Equation (11) means that among the remote sensing candidate tasks that satisfy equations (9), (10), and (11), the task with the most grid coverage is selected as the final matching remote sensing candidate task. This represents the Kth remote sensing candidate task; This indicates the task with the most covered grid cells among the remote sensing candidate tasks.

[0042] S403, Based on remote sensing candidate tasks Observation time and the covered grid Based on the judgment conditions of spatiotemporal grid requirements, the satisfied spatiotemporal grid requirements are removed, and the spatiotemporal grid requirement update is completed.

[0043] Specifically, the criteria for determining whether spatiotemporal grid requirements are met include: Equation (12) represents the time requirement of the spatiotemporal grid that can be satisfied. Observation time for matching remote sensing candidate tasks There is an intersection. Equation (13) represents the satisfyable spatiotemporal grid requirements. Coverable grids for matching remote sensing candidate tasks There is overlap. Among them, This represents the Xth observation requirement time period of the task grid. and They represent satellites The observation start time and observation end time of the kth remote sensing candidate task Indicates when satellite During the observation time of remote sensing candidate tasks Inner time, satellite The k-th remote sensing candidate task can cover the grid set; This represents the task grid.

[0044] S404. Determine whether the spatiotemporal grid requirement has been traversed. If not, proceed to S405; otherwise, proceed to S406.

[0045] S405. Select the grid with the most demand (i.e. the grid with the lowest demand satisfaction) from the spatiotemporal grid requirements as the task grid, and then proceed to S402.

[0046] S406. Summarize the matched remote sensing candidate tasks and generate remote sensing observation schemes. The generation of remote sensing observation schemes for each particle state is now complete.

[0047] S5. Based on the unmatched spatiotemporal grid requirements in the current remote sensing observation schemes under each particle state, determine the requirement satisfaction degree of the corresponding remote sensing observation schemes. The requirement satisfaction degree is the requirement of the matched spatiotemporal grids divided by the total number of spatiotemporal grids.

[0048] S6. Based on the demand satisfaction of the remote sensing observation scheme under the current particle states determined in S5, update the maximum demand satisfaction of each particle. and the maximum satisfaction of particle swarm ,in, This represents the maximum demand satisfaction in the nth particle state.

[0049] S7. Determine if the current iteration number j is equal to the preset iteration number M. If yes, proceed to S9; otherwise, ... And then transitioned to S8.

[0050] S8. Based on the maximum demand satisfaction of each particle and the maximum satisfaction of the particle swarm obtained from S6, update the particle swarm state according to the particle swarm state change model. Then switch to S4.

[0051] Specifically, the particle swarm state change model includes: Equation (14) represents the method for calculating the velocity of particle n in round j. Indicated by observation grid The requirement satisfaction of generating remote sensing observation schemes under the initial task grid; , and These represent the first weighting coefficient, the second weighting coefficient, and the third weighting coefficient, respectively. Represents a random number between 0 and 1. Equation (15) represents the method for calculating the position of particle n in round j. This is the floor function.

[0052] S9. Output the remote sensing observation scheme with the maximum demand satisfaction in the particle swarm, and the task allocation ends.

[0053] In summary, compared with the prior art, this application has the following beneficial effects: 1. Based on the wide swath characteristics of wide-swath remote sensing satellites and the spatiotemporal attributes of remote sensing observation needs, corresponding candidate remote sensing tasks can be generated, which is conducive to giving full play to the capabilities of wide-swath remote sensing satellites.

[0054] 2. To address the issue that wide-swath remote sensing satellites operating under single-satellite conditions cannot fully leverage the advantages of mission coordination and allocation under constellation conditions, a mission allocation method for wide-swath satellite constellations based on particle swarm optimization is proposed. This method can effectively improve the remote sensing observation scheme's ability to meet the requirements of large-area remote sensing observation and high temporal resolution.

[0055] like Figure 4 As shown in the figure, an embodiment of this application provides a wide-swath remote sensing satellite constellation mission allocation system based on particle swarm optimization algorithm, comprising: The parameter initialization module 10 is used to initialize the remote sensing observation requirements, wide-swath satellite constellation, and particle swarm state.

[0056] The observation requirement decomposition module 20 is used to decompose the observation area, decompose the remote sensing observation frequency requirement into the remote sensing observation time requirement, and use the remote sensing observation requirement decomposition model to decompose the remote sensing observation requirement into spatiotemporal grid requirement.

[0057] The candidate task generation module 30 is used to obtain remote sensing candidate tasks based on satellite access information and using a remote sensing candidate task generation model.

[0058] The observation scheme generation module 40 is used to select the initial task grid based on the particle swarm state and generate remote sensing observation schemes for each particle state at the current time.

[0059] The requirement satisfaction analysis module 50 is used to determine the requirement satisfaction of the corresponding remote sensing observation scheme based on the unmatched spatiotemporal grid requirements in the remote sensing observation scheme under the current particle state.

[0060] The demand satisfaction update module 60 is used to update the maximum demand satisfaction of each particle and the maximum satisfaction of the particle swarm based on the demand satisfaction of the remote sensing observation scheme under the current state of each particle.

[0061] The loop module 70 is used to determine whether the current iteration number is equal to the preset iteration number. If yes, it will switch to the observation scheme output module; otherwise, the iteration number will be incremented by 1 and the module will switch to the particle swarm update module.

[0062] The particle swarm update module 80 is used to update the particle swarm state based on the maximum demand satisfaction of each particle and the maximum satisfaction of the particle swarm, according to the particle swarm state change model, and then transfer to the observation scheme generation module, demand satisfaction analysis module, demand satisfaction update module and loop module.

[0063] The observation scheme output module 90 is used to output the remote sensing observation scheme with the maximum demand satisfaction in the particle swarm. The task allocation is now complete.

[0064] In this embodiment, the beneficial effects of the wide-swath remote sensing satellite constellation task allocation system based on particle swarm optimization are similar to those of the wide-swath remote sensing satellite constellation task allocation method based on particle swarm optimization, and will not be repeated here.

[0065] An electronic device provided in this application includes a memory and a processor; the memory is used to store a computer program; the processor is used to implement the wide-swath remote sensing satellite constellation task allocation method based on particle swarm optimization algorithm as described above when the computer program is executed.

[0066] This application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the wide-swath remote sensing satellite constellation task allocation method based on particle swarm optimization algorithm as described above.

[0067] In this embodiment, the beneficial effects of the electronic device and the computer-readable storage medium are similar to those of the wide-swath remote sensing satellite constellation task allocation method based on the particle swarm optimization algorithm described above, and will not be repeated here.

[0068] The present invention describes electronic devices that can serve as servers or clients of this application, which are examples of hardware devices that can be applied to various aspects of this application. Electronic devices are intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital assistant devices, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the application described and / or claimed herein.

[0069] Electronic devices include a computing unit that can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM can also store various programs and data required for device operation. The computing unit, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0070] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc. In this application, the separately described modules may or may not be physically separate. Some or all of the modules can be selected to achieve the purpose of the embodiments of this application according to actual needs. Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0071] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0072] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for mission allocation in a wide-swath remote sensing satellite constellation based on particle swarm optimization, characterized in that, include: Initialize remote sensing observation requirements, wide-swath satellite constellation, and particle swarm state; The observation area is decomposed, the frequency requirement of remote sensing observation is decomposed into the time requirement of remote sensing observation, and the remote sensing observation requirement is decomposed into the spatiotemporal grid requirement using the remote sensing observation requirement decomposition model. Based on satellite access data, a remote sensing candidate task generation model is used to obtain remote sensing candidate tasks; Based on the particle swarm state, select the initial task grid and generate remote sensing observation schemes for each particle state at the current time. Based on the unmatched spatiotemporal grid requirements in the current remote sensing observation schemes for each particle state, determine the requirement satisfaction of the corresponding remote sensing observation schemes. Based on the current satisfaction of the remote sensing observation scheme requirements under each particle state, update the maximum satisfaction of each particle's requirements and the maximum satisfaction of the particle swarm. If the current iteration number is not equal to the preset iteration number, the iteration number is incremented by 1; Based on the maximum demand satisfaction of each particle and the maximum satisfaction of the particle swarm, the particle swarm state is updated according to the particle swarm state change model, and the remote sensing observation scheme under the updated particle state is generated again, and the maximum demand satisfaction of each particle and the maximum satisfaction of the particle swarm are updated. If the current iteration number is equal to the preset iteration number, output the remote sensing observation scheme with the maximum demand satisfaction in the particle swarm.

2. The wide-swath remote sensing satellite constellation task allocation method based on particle swarm optimization algorithm as described in claim 1, characterized in that, The remote sensing observation demand decomposition model includes: in, , These represent the observation grids after Q-decomposition of the observation region. The central longitude and central latitude; and These represent the z-th observation grid. The start and end times of the demand for the y-th observation demand period. Indicates the start time of the observation requirement. Let X represent the duration of the observation requirement, and X represent the number of observations within that duration. Represents the observation grid The observation time of the y-th observation, where i represents the total number of observation grids after Q decomposition of the observation region.

3. The wide-swath remote sensing satellite constellation task allocation method based on particle swarm optimization algorithm as described in claim 1, characterized in that, The remote sensing candidate task generation model includes: in, Indicates satellite For the i-th observation grid The time of the mth visit; and They represent satellites The observation start time and observation end time of the kth remote sensing candidate task Indicates satellite Maximum observation duration constraint; Indicates when satellite During the observation time of remote sensing candidate tasks Inner time, satellite The k-th remote sensing candidate task can cover the grid set; This represents the c-th observation grid. Indicates satellite For the c-th observation grid The time of the mth visit.

4. The wide-swath remote sensing satellite constellation task allocation method based on particle swarm optimization algorithm as described in claim 3, characterized in that, The step of selecting an initial task grid based on the particle swarm state and generating a remote sensing observation scheme for each particle state includes: S401, Based on particle state Select observation grid As the initial task grid; S402. Select the time period of unmet observation requirements for the task grid. According to the remote sensing candidate task matching model based on the greedy algorithm, remote sensing candidate tasks are matched. ; S403, Based on remote sensing candidate tasks Observation time and the covered grid Based on the judgment conditions of spatiotemporal grid requirements, remove the spatiotemporal grid requirements that have been met, and complete the spatiotemporal grid requirement update; S404. Determine whether the spatiotemporal grid requirement has been traversed. If not, proceed to S405; otherwise, proceed to S406. S405. Select the grid with the highest demand in the spatiotemporal grid requirements as the task grid, and then proceed to S402. S406. Summarize the matched remote sensing candidate tasks and generate remote sensing observation schemes. The generation of remote sensing observation schemes for each particle state is now complete.

5. The wide-swath remote sensing satellite constellation task allocation method based on particle swarm optimization algorithm as described in claim 4, characterized in that, The remote sensing candidate task matching model based on the greedy algorithm includes: in, and They represent satellites The observation start time and observation end time of the kth remote sensing candidate task This represents the Xth observation requirement time period of the task grid. Indicates satellite The kth remote sensing candidate task can cover the grid set. Represents the task grid; Satellite constraints are used to verify the observation time of the matching remote sensing candidate tasks. If the result is 1, the verification is successful; otherwise, it is unsuccessful. This represents the Kth remote sensing candidate task; This indicates the task with the most covered grid cells among the remote sensing candidate tasks.

6. The wide-swath remote sensing satellite constellation task allocation method based on particle swarm optimization algorithm as described in claim 5, characterized in that, The conditions for satisfying the spatiotemporal grid requirement include: in, This represents the Xth observation requirement time period of the task grid. and They represent satellites The observation start time and observation end time of the kth remote sensing candidate task Indicates when satellite During the observation time of remote sensing candidate tasks Inner time, satellite The k-th remote sensing candidate task can cover the grid set; This represents the task grid.

7. The wide-swath remote sensing satellite constellation task allocation method based on particle swarm optimization algorithm as described in claim 4, characterized in that, The particle swarm state change model includes: in, Indicates observation grid The requirement satisfaction of generating remote sensing observation schemes under the initial task grid; , and These represent the first weighting coefficient, the second weighting coefficient, and the third weighting coefficient, respectively. Represents a random number between 0 and 1; This is the floor function.

8. A wide-swath remote sensing satellite constellation mission allocation system based on particle swarm optimization algorithm, characterized in that, The wide-swath remote sensing satellite constellation mission allocation method based on particle swarm optimization as described in any one of claims 1-7, wherein the wide-swath remote sensing satellite constellation mission allocation system based on particle swarm optimization includes: The parameter initialization module is used to initialize remote sensing observation requirements, wide-swath satellite constellation, and particle swarm state. The observation requirement decomposition module is used to decompose the observation area, decompose the remote sensing observation frequency requirement into the remote sensing observation time requirement, and use the remote sensing observation requirement decomposition model to decompose the remote sensing observation requirement into the spatiotemporal grid requirement. The candidate task generation module is used to obtain remote sensing candidate tasks based on satellite access information and using a remote sensing candidate task generation model. The observation scheme generation module is used to select the initial task grid based on the particle swarm state and generate remote sensing observation schemes for each particle state at the current time. The requirement satisfaction analysis module is used to determine the requirement satisfaction of the corresponding remote sensing observation scheme based on the unmatched spatiotemporal grid requirements in the remote sensing observation schemes under the current particle state. The demand satisfaction update module is used to update the maximum demand satisfaction of each particle and the maximum satisfaction of the particle swarm based on the demand satisfaction of the remote sensing observation scheme under the current state of each particle. The loop module is used to determine whether the current iteration number is equal to the preset iteration number. If yes, it will switch to the observation scheme output module; otherwise, the iteration number will be incremented by 1 and the module will switch to the particle swarm update module. The particle swarm update module is used to update the particle swarm state based on the maximum demand satisfaction of each particle and the maximum demand satisfaction of the particle swarm, according to the particle swarm state change model, and then transfers to the observation scheme generation module, demand satisfaction analysis module, demand satisfaction update module and loop module. The observation scheme output module is used to output the remote sensing observation scheme with the maximum demand satisfaction in the particle swarm, and the task allocation ends.

9. An electronic device, characterized in that, Including memory and processor; The memory is used to store computer programs; The processor is configured to, when executing the computer program, implement the wide-swath remote sensing satellite constellation task allocation method based on particle swarm optimization as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the wide-swath remote sensing satellite constellation task allocation method based on particle swarm optimization as described in any one of claims 1 to 7.

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