Regional target decomposition method for heterogeneous remote sensing satellite constellation resource collaborative scheduling

Abstract

This application discloses a regional target decomposition method for collaborative scheduling of satellite resources in heterogeneous remote sensing constellations, relating to the field of satellite remote sensing technology. The method includes: determining multiple stripes of satellite observation target areas in a heterogeneous remote sensing constellation; constructing triplet decision variables for each strip: strip start-point encoding, strip selection binary variable, and length normalization coefficient; constructing strip combination schemes based on the triplet decision variables; constructing a regional target decomposition model with the objective function of minimizing total satellite usage cost, combined with coverage constraints and strip-related constraints; solving the regional target decomposition model to obtain the optimal strip combination scheme; and obtaining the triplet decision variables for each strip based on the optimal strip combination scheme. This application provides reasonable input for subsequent single-satellite scheduling, helps generate better scheduling schemes, maximizes the fulfillment of mission requirements, fully utilizes the observation capabilities of heterogeneous remote sensing constellations, and ensures their efficient collaborative operation.

Description

Technical Field

[0001] This application relates to the field of satellite remote sensing technology, and in particular to a regional target decomposition method for collaborative scheduling of satellite resources in heterogeneous remote sensing constellations. Background Technology

[0002] Compared to traditional remote sensing constellations, heterogeneous remote sensing constellations possess stronger observational capabilities, higher revisit rates, and multi-source collaborative observation capabilities. However, due to the large constellation size, significant differences in satellite capabilities, and the massive nature of mission requirements, heterogeneous remote sensing constellation imaging mission planning needs to generate a scheduling scheme—"which satellite will perform which observation at what time"—under various resource and constraint conditions, in order to maximize the fulfillment of mission requirements. Therefore, imaging mission planning is a core element in ensuring the efficient collaborative operation of heterogeneous remote sensing constellations.

[0003] To improve scheduling efficiency, heterogeneous remote sensing constellations often employ a step-by-step strategy of "matching demand and resources first, then scheduling satellites," with regional target decomposition being a key preliminary step. This technology aims to rationally decompose a large observation area into suitable sub-tasks based on satellite capabilities (such as swath width, resolution, orbital inclination, yaw capability, and cost), and allocate them to a matching set of satellite resources, providing feasible input for subsequent single-satellite scheduling. Existing methods mainly include two categories: explicit decomposition and implicit decomposition. Explicit decomposition generates a candidate strip set in a two-stage framework and then optimizes the selection; implicit decomposition embeds regional division and resource scheduling into a unified model for simultaneous solution, offering greater flexibility.

[0004] However, existing decomposition techniques still have significant shortcomings: explicit methods rely on discretized candidate sets, and when faced with star clusters with mixed orbits and heterogeneous capabilities, the parallel strip strategy has poor adaptability and it is difficult to balance accuracy and efficiency; although implicit methods can achieve fine coverage by linking continuous / discrete variables, the decision variables expand rapidly with the surge in the number of satellites and observation opportunities, resulting in high solution complexity and making it difficult to support the engineering application of large-scale star clusters.

[0005] Therefore, there is currently a lack of a regional target decomposition method that balances efficiency and flexibility. Summary of the Invention

[0006] This application provides a regional target decomposition method for collaborative scheduling of satellite resources in heterogeneous remote sensing constellations, to overcome the shortcomings of the aforementioned related technologies. The technical solution is as follows: Firstly, this application provides a regional target decomposition method for collaborative scheduling of satellite resources in heterogeneous remote sensing constellations, including: To identify multiple stripes of the target area observed by each type of satellite in a heterogeneous remote sensing constellation; Construct a strip start-up code based on the starting position of the strip in the target region, construct a strip selection binary variable based on the strip selection result of the strip combination scheme, and construct a length normalization coefficient based on the strip length. Using the strip start-point code, the strip selection binary variable, and the length normalization coefficient as triplet decision variables, a strip combination scheme containing multiple stripes is constructed based on the triplet decision variables of each strip. With the goal of minimizing the total cost of satellite use, and with the constraints of coverage of the target area by the strip combination scheme and strip correlation constraints, a regional target decomposition model based on the strip combination scheme for observing the target area is constructed. The regional target decomposition model is solved to obtain the optimal strip combination scheme, and the triplet decision variables for each strip are extracted based on the optimal strip combination scheme.

[0007] In one alternative embodiment of the first aspect, the generation process of the strip start-point code includes: The target region is meshed, and the target region is processed into a mesh region composed of multiple meshes of the same size; The starting point code of the strip is obtained by encoding the grid coordinates of the starting point of the strip within the grid region.

[0008] In one alternative embodiment of the first aspect, the process of generating the length normalization coefficient includes: Determine the maximum strip pushbroom length for each type of satellite in the heterogeneous remote sensing constellation during observation, and normalize the strip lengths obtained from observations by the same type of satellites based on the maximum strip pushbroom length, processing the strip lengths to the range of [0,1] to obtain the length normalization coefficients corresponding to the strips.

[0009] In one alternative to the first aspect, the stripe-related constraints are constructed based on the following steps: The strip length constraint for each type of satellite is constructed based on the maximum strip pushbroom length of each type of satellite in the heterogeneous remote sensing constellation. The maximum number constraint of the corresponding type of satellite strip is constructed based on the maximum number of each type of satellite in the heterogeneous remote sensing constellation. The maximum number of strip start points is determined based on the number of grids in the grid region, thus constructing a constraint on the number of strip start points. The coverage constraint is constructed based on the following steps: The coverage rate is obtained by the ratio of the total area of ​​the union region formed by the intersection of all strips in the strip combination scheme with the target region to the total area of ​​the target region. The coverage constraint is defined as a coverage value equal to 1.

[0010] In one alternative embodiment of the first aspect, solving the regional target decomposition model to calculate the optimal strip combination scheme includes: The regional target decomposition model is solved iteratively. In each iteration, the particle swarm optimization algorithm is used to optimize the global optimal particle output by the previous iteration to obtain the candidate global optimal particle for the current iteration. Based on the candidate global optimal particle, a preset number of local searches are performed. In each local search, a local search operator is selected based on the calling probability of each local search operator to adjust the candidate global optimal particle to obtain a new local search particle. The objective function is calculated based on all local search particles and the candidate global optimal particles, and the particle corresponding to the minimum value of the objective function is selected as the global optimal particle output in the current iteration. Determine if the ordinal number of the current iteration round is less than the iteration round threshold. If so, increment the ordinal number of the iteration round by 1 and proceed with the calculation of the next iteration round. The optimal strip combination scheme is determined based on the globally optimal particle of the last iteration round until the iteration round number equals the iteration round threshold. Each particle corresponds to a different strip combination scheme.

[0011] In one alternative embodiment of the first aspect, the method further includes: Before performing a local search, the probability difference between the preset late-stage iteration call probability and the preset early-stage iteration call probability is calculated. The weight coefficient is determined based on the ordinal number of the current iteration round. The call probability of each local search operator is obtained by weighting the probability difference based on the weight coefficient.

[0012] In one alternative of the first aspect, the local search operator includes a length swap operator, a length mutation operator, a strip selection mutation operator, a starting point mutation operator, and a length hill climbing operator; The length exchange operator is used to exchange the length normalization coefficients of any two strips in the strip combination scheme corresponding to the candidate global optimal particle. The length mutation operator is used to mutate the normalized coefficient of the length of any strip in the strip combination scheme corresponding to the candidate global optimal particle into a random number in the interval (0, 1); The strip selection mutation operator is used to adjust the strip selection binary variable of any strip in the strip combination scheme corresponding to the candidate global optimal particle, and adjust the value of the strip selection binary variable from 1 to 0, or from 0 to 1. The initiation mutation operator is used to assign the strip initiation code of any strip in the strip combination scheme corresponding to the candidate global optimal particle to a value in the set. A random integer within the range, where N-1 represents the maximum number of stripe start points; The length climbing operator is used to adjust the length normalization coefficient of any strip in the strip combination scheme corresponding to the candidate global optimal particle based on a preset adjustment step size.

[0013] Secondly, this application also provides a regional target decomposition device for collaborative scheduling of satellite resources in heterogeneous remote sensing constellations, comprising: Triple decision variable construction unit, used to determine multiple stripes of the observation target area of ​​each type of satellite in heterogeneous remote sensing constellations; The triplet decision variable construction unit is also used to construct a strip start-up code based on the starting position of the strip in the target region, construct a strip selection binary variable based on the strip selection result of the strip combination scheme, and construct a length normalization coefficient based on the strip length. The triplet decision variable construction unit is also used to construct a strip combination scheme containing multiple stripes based on the triplet decision variables of each strip, using the strip start-point code, the strip selection binary variable, and the length normalization coefficient as triplet decision variables. The regional target decomposition model construction unit is used to construct a regional target decomposition model based on the strip combination scheme for observing the target area, with the objective function being to minimize the total cost of satellite use and the constraints being the coverage of the strip combination scheme on the target area and the strip correlation constraints. The regional target decomposition model calculation unit solves the regional target decomposition model, calculates the optimal strip combination scheme, and extracts the triplet decision variables for each strip based on the optimal strip combination scheme.

[0014] Thirdly, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method provided by the first aspect of this application or any implementation thereof.

[0015] Fourthly, this application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided by the first aspect of this application or any implementation thereof.

[0016] The beneficial effects of the technical solution provided in this application include at least the following: This application provides a regional target decomposition method for the collaborative scheduling of satellite resources in heterogeneous remote sensing constellations, offering a solution that balances efficiency and flexibility. By constructing a triplet decision variable consisting of strip start-point encoding, strip selection binary variables, and length normalization coefficients, a regional target decomposition model is built. This model comprehensively considers differences in total satellite resources, imaging capabilities, orbital orientation, and usage costs, achieving efficient resource allocation oriented towards missions while meeting regional coverage requirements. Compared to explicit decomposition, this method eliminates the dependence on the discretization degree of candidate subtask sets, avoiding the difficulty of balancing accuracy and efficiency. Compared to implicit decomposition, it effectively controls the scale of decision variables, reduces the solution difficulty, and overcomes the difficulties caused by the surge in the number of satellites and observation opportunities. Finally, the optimal strip combination scheme is obtained through solving the model, providing reasonable input for subsequent single-satellite scheduling, helping to generate better scheduling schemes, maximizing the fulfillment of mission requirements, fully leveraging the observation capabilities of heterogeneous remote sensing constellations, and ensuring their efficient collaborative operation. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating a regional target decomposition method for collaborative scheduling of satellite resources in heterogeneous remote sensing constellations, provided in an embodiment of this application. Figure 2 This is a schematic diagram of the structure of a regional target decomposition device for collaborative scheduling of satellite resources in heterogeneous remote sensing constellations, provided in an embodiment of this application. Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or modules is not limited to the steps or modules listed, but may optionally include steps or modules not listed, or may optionally include other steps or modules inherent to such process, method, product, or apparatus.

[0021] It should be noted that the terms "first" and "second" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects. It is understood that "first" and "second" can be interchanged in a specific order or sequence where permitted. It should be understood that the objects distinguished by "first" and "second" can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in an order other than those described or illustrated herein.

[0022] It should be noted that a strip refers to a continuous area covered by a satellite sensor when observing the ground, typically a long strip-shaped area extending along the direction of the satellite's orbit. This application's embodiments are used to decompose the observed target area into a set of strips observed by multiple remote sensing satellites. Each strip is equivalent to an observation sub-task. Thus, the regional target decomposition method provided in this application can divide the observation of a target area into several observation sub-tasks corresponding to the strips.

[0023] Currently, related technologies can employ both explicit and implicit decomposition methods. Explicit decomposition serves a two-stage framework of "decomposition first, scheduling later": the decomposition stage independently generates a set of candidate subtasks (such as stripe sets), and the scheduling stage then optimizes and allocates resources based on this set. Explicit decomposition methods can be mainly categorized into two types: 1) First, generate a set of candidate subtasks, then construct a 0 / 1 combinatorial optimization model. However, this approach heavily relies on the discretization degree of the candidate subtask set, making it difficult to achieve a balance between accuracy and efficiency. 2) For traditional constellations (with almost identical orbits and swath widths), a parallel stripe decomposition strategy is used. This method is less applicable to constellations with mixed orbits and significant differences in satellite observation capabilities. Implicit decomposition, on the other hand, serves an integrated decomposition-scheduling framework: key decisions such as how to divide regions, how to allocate tasks, and how to arrange time and resources are solved synchronously within the same optimization model. Implicit decomposition directly links continuous / discrete decision variables such as strip direction and lateral tilt angle to the optimization model, completing the implicit decomposition of regional targets during the solution process and achieving coverage optimization, thus offering greater flexibility and continuous adjustability. However, in remote sensing constellation scenarios, with the surge in the number of satellites and available observation opportunities, the scale of decision variables can easily expand rapidly, significantly increasing the solution difficulty and making this method difficult to directly apply to remote sensing constellations.

[0024] The embodiments of this application are used to solve the defects of the above-mentioned related technologies. The present application will be described in detail below with reference to specific embodiments.

[0025] Next, combine Figure 1 This application introduces a regional target decomposition method for collaborative scheduling of satellite resources in heterogeneous remote sensing constellations, provided by an embodiment of this application. For details, please refer to... Figure 1 , Figure 1 This illustration shows a flowchart of a regional target decomposition method for collaborative scheduling of satellite resources in heterogeneous remote sensing constellations, provided in an embodiment of this application. Figure 1 As shown, the method includes the following steps: S101, which identifies multiple stripes of the target area observed by each type of satellite in a heterogeneous remote sensing constellation; S102, construct the strip start-up code based on the starting position of the strip in the target area, construct the strip selection binary variable based on the strip selection result of the strip combination scheme, and construct the length normalization coefficient based on the strip length; S103, using the strip start-point code, the strip selection binary variable, and the length normalization coefficient as triplet decision variables, a strip combination scheme containing multiple strips is constructed based on the triplet decision variables of each strip. S104. With minimizing the total cost of satellite use as the objective function and the coverage constraints and related constraints of the strip combination scheme on the target area as the constraints, a regional target decomposition model for observing the target area based on the strip combination scheme is constructed. S105, Solve the regional target decomposition model to obtain the optimal strip combination scheme, and extract the triplet decision variables for each strip based on the optimal strip combination scheme.

[0026] In some embodiments, in S101, a certain town, city, province, or other region can be selected as the research object, that is, as the target area for observation. This application embodiment does not limit this.

[0027] Specifically, in S101, the total amount of satellite resources, imaging capabilities, and orbital orientation of each type of satellite in the heterogeneous remote sensing constellation can be considered to determine multiple stripes of the observation target area for each type of satellite. The specific parameters of the stripes can be set by setting the three decision variables in S102.

[0028] In some embodiments, each decision variable in the triplet decision variables can be determined separately.

[0029] The strip start code can be represented as: The construction process includes: The target region is meshed, which is a grid region composed of multiple grids of the same size; the number of grids can be denoted as N. The starting point code of the strip is obtained by encoding the grid coordinates of the starting point of the strip within the grid region.

[0030] Specifically, the range can be determined sequentially from left to right and from bottom to top according to the grid coordinates corresponding to the starting point of the strip. A positive integer is selected as the starting point code for the stripe.

[0031] The strip selection binary variable can be represented as follows: , The value is 0 or 1. A value of 1 indicates that the corresponding stripe has been selected for the stripe combination scheme. A value of 0 indicates that the strip combination scheme did not select a corresponding strip, meaning the strip combination scheme does not contain... The band with a value of 0.

[0032] The length normalization coefficient can be expressed as: The process of generating the length normalization coefficient includes: Determine the maximum strip pushbroom length for each type of satellite in the heterogeneous remote sensing constellation during observation, and normalize the strip lengths obtained from observations by the same type of satellites based on the maximum strip pushbroom length, processing the strip lengths to the range of [0,1] to obtain the length normalization coefficients corresponding to the strips.

[0033] Specifically, in S103, by using the strip start-point encoding, the strip selection binary variable, and the length normalization coefficient as triplet decision variables, different strip combination schemes can be obtained by adjusting the triplet decision variables of each strip, which facilitates the subsequent steps of solving the regional target decomposition model.

[0034] In some embodiments, the stripe-related constraints in S104 are constructed based on the following steps: Based on the maximum stripe pushbroom length of each type of satellite in the heterogeneous remote sensing constellation, the stripe length constraint for the corresponding type of satellite is constructed, expressed by the formula: ; Based on the maximum number of satellites of each type in the heterogeneous remote sensing constellation, the maximum number constraint of stripes for the corresponding type of satellite is constructed, expressed by the formula: ; The maximum number of strip start points is determined based on the number of grids in the grid region, thus constructing a constraint on the number of strip start points, expressed by the formula: ; in, Indicates the first Satellite-like The length of each strip. Indicates the first The maximum strip pushbroom length for satellite-like applications, Indicates the first The ordinal number of the j-th stripe of the satellite-like structure. Indicates the first The maximum number of stripes for satellite-like structures is also equivalent to the number of... The maximum number of satellites.

[0035] The coverage constraint is constructed based on the following steps: The coverage rate is obtained by dividing the total area of ​​the union region formed by the intersections of all strips in the strip combination scheme with the target region by the total area of ​​the target region. The formula is as follows: ; in, This represents the total area of ​​the union region. This represents the total area of ​​the target region. For coverage; The coverage constraint is defined as a coverage value equal to 1, and the formula is as follows: .

[0036] In some embodiments, the objective function in S104 can be expressed as the following formula: ; in, That is, the total cost of using the satellite; Indicates the first The cost of a single power-on for a satellite-like device Indicates the first The cost of push-broom per unit distance for satellite-like applications.

[0037] In some embodiments, in S105, the regional target decomposition model is solved to obtain the optimal strip combination scheme. The iterative solution of the regional target decomposition model specifically includes: S201, in the Gth iteration, the particle swarm optimization algorithm is used to optimize based on the globally optimal particle output in the G-1th iteration to obtain the candidate globally optimal particle in the Gth iteration.

[0038] It should be noted that the calculation process of the particle swarm optimization algorithm is the same as that in related technologies. In the embodiments of this application, each particle corresponds to a different strip combination scheme. In the first iteration, the population size of the particles can be preset, and the number of strip combination schemes with a population size can be generated by randomly selecting values ​​according to the constraints of the triplet decision variables. The particle swarm optimization algorithm is continuously iterated to obtain the candidate global optimal particles in the first iteration.

[0039] S202, perform a preset number of local searches based on the candidate global optimal particles. In each local search, select a local search operator based on the calling probability of each local search operator to adjust the candidate global optimal particles to obtain new local search particles.

[0040] For example, the preset number of searches can be set to 9, which means that 9 local searches are required to make local adjustments to the candidate global optimal particle.

[0041] Specifically, before each local search, the probability difference between the preset late-iteration call probability and the preset early-iteration call probability can be calculated. Weighting coefficients are determined based on the current iteration number. The call probability of each local search operator is then calculated by weighting the probability difference based on these weighting coefficients, using the following formula:

[0042]

[0043] in, Let G be the probability of invocation, and G be the ordinal number of the current iteration round. The threshold for the number of iteration rounds, These are the weighting coefficients. The pre-defined invocation probability in the early stages of iteration is based on the type of the local search operator. The probability of late-stage iteration is preset based on the type of the local search operator.

[0044] Optionally, local search operators This may include, but is not limited to, length swapping operators. Length mutation operator Strip selection mutation operator Origin mutation operator And length climbing operator .

[0045] Understandably, the probability of calling in the early stage of iteration and the probability of calling in the late stage of iteration can be preset according to the total number of corresponding iteration rounds. For example, the probability of calling in the early stage of iteration for five operators can be preset to 0.4, 0.2, 0.2, 0.05, and 0.15, and the probability of calling in the late stage of iteration can be preset to 0.4, 0.1, 0.1, 0.1, and 0.3.

[0046] In each local search, based on the determined invocation probability above, one of the five operators is selected to locally adjust the candidate global optimal particle.

[0047] Specifically, the length exchange operator is used to exchange the length normalization coefficients of any two stripes in the strip combination scheme corresponding to the candidate global optimal particle, as expressed by the formula: ; Among them, subscript and These represent different stripes on the same particle.

[0048] Specifically, the length mutation operator is used to mutate the normalized coefficient of the length of any strip in the strip combination scheme corresponding to the candidate global optimal particle into a random number in the interval (0, 1), and the mutation formula is: ; in, This represents a random number that is uniformly distributed between 0 and 1.

[0049] Specifically, the strip selection mutation operator is used to adjust the strip selection binary variable of any strip in the strip combination scheme corresponding to the candidate global optimal particle, and adjust the value of the strip selection binary variable from 1 to 0, or from 0 to 1; Specifically, the starting point mutation operator is used to assign the starting point code of any strip in the strip combination scheme corresponding to the candidate global optimal particle to a value in the set. A random integer within the range, where N represents the maximum number of stripe start points; Specifically, the length climbing operator is used to adjust the length normalization coefficient of any strip in the strip combination scheme corresponding to the candidate global optimal particle based on a preset adjustment step size.

[0050] In some embodiments, an adaptive search strategy can be adopted to continuously update the preset adjustment step size. For example, it can first try in random positive / negative directions with a very small step size. If a certain direction can optimize the quality of the solution, then the direction of progress is determined. Then, it can greedily advance along the direction with a fixed reference step size until the first case of no improvement or touching the edge occurs. The last successful solution is found, and the maximum step size that can still be improved is found by using the binary search method and the solution is updated to achieve fine adjustment of the continuous variable. The embodiments of this application do not limit this.

[0051] In this way, a local search particle is obtained after each local search.

[0052] S203, calculate the objective function based on all local search particles and the candidate global optimal particles respectively, and select the particle corresponding to the minimum value of the objective function as the global optimal particle output in the current iteration round.

[0053] S204, Determine whether the ordinal number G of the current iteration round is less than the iteration round threshold. If so, then let the ordinal number of the iteration round be... Then, the calculation of the next iteration round is performed, and the global optimal particle output by the current iteration round is transferred to the step S201; Continue until the iteration number equals the iteration threshold, then execute S205: S205, the optimal strip combination scheme is obtained based on the globally optimal particle determination in the last iteration round.

[0054] Specifically, based on the global optimal particle of the last iteration, the triplet decision variables corresponding to each strip can be extracted, and then the starting point, strip selection binary variables, and strip length of each strip can be determined. The direction of each strip can be easily determined according to the orbital direction of the satellite, thereby decomposing the target area into observation sub-tasks corresponding to each strip in the strip combination scheme corresponding to the global optimal particle of the last iteration, which serves as the regional target decomposition scheme.

[0055] In some specific embodiments, City A, City B, and Province C can be selected as target areas. Particle Swarm Optimization (PSO), Simulated Annealing Particle Swarm Optimization (SA-PSO), and Hybrid Local Search Particle Swarm Optimization (HLS-PSO) provided in this application embodiment are selected to solve the regional target decomposition model. 50 iterations are used as the target number of runs. Simulation experiments are conducted based on this, and the results are shown in Table 1. Table 1. Simulation results based on different solution algorithms

[0056] As shown in Table 1, as the area of ​​the target region gradually increases, PSO and SA-PSO have high redundancy in large-scale target stripes and high satellite imaging costs. However, the local search mechanism introduced by HLS-PSO enables the algorithm to still show more stable and higher quality optimization results when facing planning problems of large-area target regions, indicating that the method provided by the embodiments of this application is applicable to target region scenarios of different sizes.

[0057] The following are apparatus embodiments of this application, which can be used to execute the method embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the method embodiments of this application.

[0058] Please see below. Figure 2 The image shows a schematic diagram of a regional target decomposition device for collaborative scheduling of satellite resources in heterogeneous remote sensing constellations, provided as an exemplary embodiment of this application. The device includes: Triple decision variable construction unit, used to determine multiple stripes of the observation target area of ​​each type of satellite in heterogeneous remote sensing constellations; The triplet decision variable construction unit is also used to construct a strip start-up code based on the starting position of the strip in the target region, construct a strip selection binary variable based on the strip selection result of the strip combination scheme, and construct a length normalization coefficient based on the strip length. The triplet decision variable construction unit is also used to construct a strip combination scheme containing multiple stripes based on the triplet decision variables of each strip, using the strip start-point code, the strip selection binary variable, and the length normalization coefficient as triplet decision variables. The regional target decomposition model construction unit is used to construct a regional target decomposition model based on the strip combination scheme for observing the target area, with the objective function being to minimize the total cost of satellite use and the constraints being the coverage of the strip combination scheme on the target area and the strip correlation constraints. The regional target decomposition model calculation unit solves the regional target decomposition model, calculates the optimal strip combination scheme, and extracts the triplet decision variables for each strip based on the optimal strip combination scheme.

[0059] It should be noted that the apparatus provided in the above embodiments, when executing a regional target decomposition method for collaborative scheduling of satellite resources in heterogeneous remote sensing constellations, is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. Furthermore, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and their implementation process is detailed in the method embodiments, which will not be repeated here.

[0060] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of any of the methods described above.

[0061] Please see Figure 3 This is a structural block diagram of an electronic device provided in an embodiment of this application.

[0062] like Figure 3 As shown, the electronic device includes a processor and a memory.

[0063] In this embodiment, the processor is the control center of the computer system, and can be a processor of a physical machine or a processor of a virtual machine. The processor may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor can be implemented using at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array).

[0064] A processor can also include a main processor and a coprocessor. The main processor is used to process data in the wake-up state and is also called the CPU (Central Processing Unit). The coprocessor is a low-power processor used to process data in the standby state.

[0065] The memory may include one or more computer-readable storage media, which may be non-transitory. The memory may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments of this application, the non-transitory computer-readable storage media in the memory are used to store at least one instruction, which is executed by a processor to implement the methods in the embodiments of this application.

[0066] In some embodiments, the electronic device further includes a peripheral device interface and at least one peripheral device. The processor, memory, and peripheral device interface are connected via a bus or signal line. Each peripheral device is connected to the peripheral device interface via a bus, signal line, or circuit board. Specifically, the peripheral device includes: a display screen, a camera, and audio circuitry. The peripheral device interface can be used to connect at least one I / O (Input / Output) related peripheral device to the processor and memory.

[0067] In some embodiments of this application, the processor, memory, and peripheral device interfaces are integrated on the same chip or circuit board; in other embodiments of this application, any one or two of the processor, memory, and peripheral device interfaces can be implemented on separate chips or circuit boards. This application does not specifically limit the implementation in this regard.

[0068] The electronic device structural block diagrams shown in the embodiments of this application do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0069] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the methods in any of the foregoing embodiments. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0070] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of software products. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0071] Finally, it should be noted that 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 regional target decomposition method for collaborative scheduling of satellite resources in heterogeneous remote sensing constellations, characterized in that, include: To identify multiple stripes of the target area observed by each type of satellite in a heterogeneous remote sensing constellation; Construct a strip start-up code based on the starting position of the strip in the target region, construct a strip selection binary variable based on the strip selection result of the strip combination scheme, and construct a length normalization coefficient based on the strip length. Using the strip start-point code, the strip selection binary variable, and the length normalization coefficient as triplet decision variables, a strip combination scheme containing multiple stripes is constructed based on the triplet decision variables of each strip. With the goal of minimizing the total cost of satellite use, and with the constraints of coverage of the target area by the strip combination scheme and strip correlation constraints, a regional target decomposition model based on the strip combination scheme for observing the target area is constructed. The regional target decomposition model is solved to obtain the optimal strip combination scheme, and the triplet decision variables for each strip are extracted based on the optimal strip combination scheme.

2. The regional target decomposition method for collaborative scheduling of satellite resources in heterogeneous remote sensing constellations according to claim 1, characterized in that, The generation process of the strip start-point code includes: The target region is meshed, and the target region is processed into a mesh region composed of multiple meshes of the same size; The starting point code of the strip is obtained by encoding the grid coordinates of the starting point of the strip within the grid region.

3. The regional target decomposition method for collaborative scheduling of satellite resources in heterogeneous remote sensing constellations according to claim 2, characterized in that, The process of generating the length normalization coefficient includes: Determine the maximum strip pushbroom length for each type of satellite in the heterogeneous remote sensing constellation during observation, and normalize the strip lengths obtained from observations by the same type of satellites based on the maximum strip pushbroom length, processing the strip lengths to the range of [0,1] to obtain the length normalization coefficients corresponding to the strips.

4. The regional target decomposition method for collaborative scheduling of satellite resources in heterogeneous remote sensing constellations according to claim 3, characterized in that, The strip-related constraints are constructed based on the following steps: The strip length constraint for each type of satellite is constructed based on the maximum strip pushbroom length of each type of satellite in the heterogeneous remote sensing constellation. The maximum number constraint of the corresponding type of satellite strip is constructed based on the maximum number of each type of satellite in the heterogeneous remote sensing constellation. The maximum number of strip start points is determined based on the number of grids in the grid region, thus constructing a constraint on the number of strip start points. The coverage constraint is constructed based on the following steps: The coverage rate is obtained by the ratio of the total area of ​​the union region formed by the intersection of all strips in the strip combination scheme with the target region to the total area of ​​the target region. The coverage constraint is defined as a coverage value equal to 1.

5. A regional target decomposition method for collaborative scheduling of satellite resources in heterogeneous remote sensing constellations according to claim 1, characterized in that, Solving the regional target decomposition model to calculate the optimal strip combination scheme includes: The regional target decomposition model is solved iteratively. In each iteration, the particle swarm optimization algorithm is used to optimize the global optimal particle output by the previous iteration to obtain the candidate global optimal particle for the current iteration. Based on the candidate global optimal particle, a preset number of local searches are performed. In each local search, a local search operator is selected based on the calling probability of each local search operator to adjust the candidate global optimal particle to obtain a new local search particle. The objective function is calculated based on all local search particles and the candidate global optimal particles, and the particle corresponding to the minimum value of the objective function is selected as the global optimal particle output in the current iteration. Determine if the ordinal number of the current iteration round is less than the iteration round threshold. If so, increment the ordinal number of the iteration round by 1 and proceed with the calculation of the next iteration round. The optimal strip combination scheme is determined based on the globally optimal particle of the last iteration round until the iteration round number equals the iteration round threshold. Each particle corresponds to a different strip combination scheme.

6. A regional target decomposition method for collaborative scheduling of satellite resources in heterogeneous remote sensing constellations according to claim 5, characterized in that, The method further includes: Before performing a local search, the probability difference between the preset late-stage iteration call probability and the preset early-stage iteration call probability is calculated. The weight coefficient is determined based on the ordinal number of the current iteration round. The call probability of each local search operator is obtained by weighting the probability difference based on the weight coefficient.

7. A regional target decomposition method for collaborative scheduling of satellite resources in heterogeneous remote sensing constellations according to claim 6, characterized in that, The local search operators include the length swap operator, the length mutation operator, the strip selection mutation operator, the starting point mutation operator, and the length hill climbing operator; The length exchange operator is used to exchange the length normalization coefficients of any two strips in the strip combination scheme corresponding to the candidate global optimal particle. The length mutation operator is used to mutate the normalized coefficient of the length of any strip in the strip combination scheme corresponding to the candidate global optimal particle into a random number in the interval (0, 1); The strip selection mutation operator is used to adjust the strip selection binary variable of any strip in the strip combination scheme corresponding to the candidate global optimal particle, and adjust the value of the strip selection binary variable from 1 to 0, or from 0 to 1. The initiation mutation operator is used to assign the strip initiation code of any strip in the strip combination scheme corresponding to the candidate global optimal particle to a value in the set. A random integer within the range, where N represents the maximum number of stripe start points; The length climbing operator is used to adjust the length normalization coefficient of any strip in the strip combination scheme corresponding to the candidate global optimal particle based on a preset adjustment step size.

8. A regional target decomposition device for collaborative scheduling of satellite resources in heterogeneous remote sensing constellations, characterized in that, include: Triple decision variable construction unit, used to determine multiple stripes of the observation target area of ​​each type of satellite in heterogeneous remote sensing constellations; The triplet decision variable construction unit is also used to construct a strip start-up code based on the starting position of the strip in the target region, construct a strip selection binary variable based on the strip selection result of the strip combination scheme, and construct a length normalization coefficient based on the strip length. The triplet decision variable construction unit is also used to construct a strip combination scheme containing multiple stripes based on the triplet decision variables of each strip, using the strip start-point code, the strip selection binary variable, and the length normalization coefficient as triplet decision variables. The regional target decomposition model construction unit is used to construct a regional target decomposition model based on the strip combination scheme for observing the target area, with the objective function being to minimize the total cost of satellite use and the constraints being the coverage of the strip combination scheme on the target area and the strip correlation constraints. The regional target decomposition model calculation unit solves the regional target decomposition model, calculates the optimal strip combination scheme, and extracts the triplet decision variables for each strip based on the optimal strip combination scheme.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

Images