Virtual satellite-based observation task optimization method
By introducing the concept of virtual satellites and a hierarchical optimization framework, the computational complexity and dynamic response issues in large-scale satellite constellation observation missions are addressed, enabling efficient and flexible resource management and agile response.
Patent Information
- Application Number
- CN202511995817.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-26
- Publication Date
- 2026-11-03
AI Technical Summary
Existing technologies face challenges such as explosive computational complexity, insufficient dynamic response capabilities, and uneven resource utilization in large-scale, highly dynamic satellite constellation observation missions, making it difficult to achieve real-time planning, real-time response, and flexible resource coordination.
By introducing the concept of virtual satellites, the task is decomposed into virtual satellite generation and internal scheduling through a hierarchical optimization framework. A dynamic reorganization mechanism is adopted to quickly respond to emergencies and realize on-demand aggregation and collaboration of resources.
It reduces computational complexity, improves the system's dynamic adaptability and resource utilization efficiency, and achieves agile response and efficient resource management.
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Figure CN123032011A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to intelligent planning and resource scheduling technology for satellite observation missions, and in particular to an optimization method for observation missions based on virtual satellites. Background Technology
[0002] With the development of aerospace technology, single satellites can no longer meet the ever-increasing demand for Earth observation. Satellite constellations composed of multiple satellites, especially large-scale low Earth orbit (LEO) constellations, have become the mainstream of modern space missions. The core of satellite constellation observation mission planning is to solve a complex combinatorial optimization problem: how to allocate a series of observation missions to the physical satellites in the constellation under multiple constraints such as orbital dynamics, satellite platform performance, sensor capabilities, and mission priorities, in order to achieve globally optimal observation benefits.
[0003] Existing mission planning algorithms are mainly divided into two categories: centralized planning and distributed planning. Centralized planning methods treat the entire constellation as a whole resource pool and generate a unified scheduling scheme through global optimization algorithms (such as genetic algorithms, simulated annealing, integer linear programming, etc.). Distributed planning, on the other hand, distributes decision-making authority partially or entirely to individual satellites or subsystems, completing mission allocation through local negotiation or autonomous decision-making.
[0004] Traditional centralized task planning algorithms typically follow this process: (1) Problem modeling: Mathematicalize the observation task, satellite resources, time window and constraints, and construct a global objective function, such as maximizing the total benefit of the observation target, maximizing the coverage area or minimizing the task completion time.
[0005] (2) Solution space search: Heuristic or metaheuristic algorithms (such as genetic algorithms, ant colony algorithms, etc.) are used to search for the optimal or suboptimal task allocation and scheduling sequence in the huge solution space.
[0006] (3) Scheme generation and execution: After the algorithm converges, a detailed instruction sequence is generated and uploaded to each physical satellite in the constellation for execution.
[0007] While existing technologies have addressed constellation mission planning to some extent, their inherent limitations are becoming increasingly apparent as constellation sizes expand dramatically (from dozens to tens of thousands) and mission environments become more dynamic. Computational complexity explosion: Mission planning is essentially an NP-hard problem. For large-scale constellations, the possibilities for mission-satellite combinations increase exponentially, leading to excessively long computation times for centralized optimization algorithms, making it difficult to meet the demands of rapid response and replanning.
[0008] System rigidity and poor dynamic adaptability: Traditional methods typically generate static scheduling schemes. When unexpected events occur (such as new high-priority observation requests, satellite failures, or sudden weather changes that obstruct the observation area), the computational cost of global replanning of the entire constellation is extremely high, the response is slow, and it cannot effectively cope with dynamic changes.
[0009] Uneven resource utilization: Global optimization may lead to overuse of some satellite resources while others remain idle, making it difficult to coordinate and optimize resources flexibly and locally according to the specific needs of the mission, resulting in the "barrel effect" and limiting the overall effectiveness of the entire constellation.
[0010] Scalability and compatibility issues: In "system of systems" application scenarios (such as heterogeneous constellations operated by multiple parties working together), it is difficult to establish a unified centralized planning model, and it is also impossible to be compatible with satellite resources of different owners and different performance.
[0011] Existing technologies face a triple bottleneck in handling large-scale, highly dynamic satellite constellation observation missions: computational efficiency, dynamic adaptability, and system scalability. Specifically: 1. Computational efficiency bottleneck: With the advent of the "mega-constellation" era, traditional centralized optimization algorithms, due to their exponential computational complexity, are no longer able to plan missions for hundreds or thousands of satellites within an acceptable timeframe. This makes it difficult to achieve the capability of "real-time planning and real-time response".
[0012] 2. Insufficient dynamic response capability: Application scenarios such as disaster monitoring require mission systems to have extremely high agility. The replanning mode of existing technologies, which is "a change in one part affects the whole", results in slow response to sudden tasks or environmental changes, and easily misses critical observation windows.
[0013] 3. Rigid task and resource matching: Existing methods typically assign tasks directly to individual physical satellites, lacking an intermediate abstraction layer to aggregate resources to handle complex tasks. For example, a task may require multiple satellites to conduct collaborative observations from different angles within a short period of time, and traditional methods struggle to efficiently organize such temporary, task-driven "virtual observation clusters."
[0014] It should be noted that the information disclosed in the background section above is only for understanding the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention
[0015] The main objective of this invention is to overcome the deficiencies in the aforementioned background technology and provide a virtual satellite-based observation task optimization method that efficiently coordinates large-scale heterogeneous constellation resources to achieve agile and high-yield observations.
[0016] To achieve the above objectives, the present invention adopts the following technical solution: A virtual satellite-based observation task optimization method includes the following steps: S1. Physical resource modeling and task clustering analysis: Model the orbital parameters, sensor capabilities, and on-board resources of the physical satellite; perform clustering analysis on the set of tasks to be observed according to geographical location, time window requirements, and priority; S2. Virtual Satellite Generation and Task Allocation: Based on the task requirements after clustering, virtual satellites are dynamically generated through optimization algorithms. Each virtual satellite is logically aggregated from physical satellites that meet specific task requirements. Task packages are allocated to the corresponding virtual satellites to complete strategic-level optimization. S3. Virtual satellite internal task scheduling: Start a local scheduling algorithm for each virtual satellite to assign specific observation tasks and execution sequences to its member physical satellites, maximize task benefits while satisfying physical constraints, and complete tactical layer optimization; S4. Status Monitoring and Dynamic Reorganization: Real-time monitoring of satellite status and mission environment; when an emergency mission, satellite failure, or sudden environmental change is triggered, local reorganization is performed by disbanding virtual satellites, allocating physical resources, or creating new virtual satellites to generate an updated scheduling plan.
[0017] Furthermore, in step S1: The task clustering analysis includes: based on the latitude and longitude coordinate distribution of the observation targets, the continuous time intervals during which the tasks are allowed to be executed, the task priority level, and the required observation mode type, tasks that are spatially adjacent, have overlapping time windows, or require multi-satellite coordination are aggregated into task clusters for unified scheduling.
[0018] Furthermore, in step S2: The optimization objective of virtual satellite generation is to maximize the weighted sum of the comprehensive capability satisfaction of the virtual satellite for the observation requirements of the mission cluster, while minimizing the total resource cost of building and maintaining the operation of the virtual satellite; wherein the comprehensive capability satisfaction includes the virtual field of view coverage formed by the collaboration of member satellites, the combination of multi-angle imaging capabilities, and the joint revisit cycle index.
[0019] Furthermore, in step S2: The allocation process of the task packages and virtual satellites is implemented through a high-order allocation algorithm, which is optimized based on the capability matching degree between the task clusters and virtual satellites.
[0020] Furthermore, in step S3: The local scheduling algorithm is implemented through the following steps: a) Establish task allocation decision variables for each physical satellite within the virtual satellite; b) Ensure that missions are assigned only to satellites whose time windows can be covered under visibility constraints; c) Control the energy consumption of single-satellite attitude maneuvering and the total amount of onboard resources under resource constraints; d) Generate a scheduling sequence with the goal of maximizing the total priority weighted value of the tasks undertaken by the virtual satellite.
[0021] Furthermore, the local scheduling algorithm employs a heuristic search strategy to optimize the task execution timing under the constraints of steps b)-c).
[0022] Furthermore, in step S4: Dynamic reconfiguration is triggered by the following conditions: the addition of a high-priority task that requires immediate response, physical satellite orbital anomalies or functional failures, and changes in the external environment that render the original task unexecutable. The reconfiguration process only adjusts the affected virtual satellites and does not require global replanning.
[0023] Furthermore, the dynamic reorganization specifically performs any of the following operations: Disband the lowest priority virtual satellite to release its physical resources; Available satellites are drawn from virtual satellites performing non-urgent missions; Dynamically create dedicated virtual satellites and allocate physical resources for emergency missions.
[0024] Furthermore, the lifecycle of the virtual satellite is dynamically defined by the start and end times of the associated missions, and it is automatically disbanded when the mission ends to release resources for reorganization and reuse.
[0025] A computer program product includes a computer program that, when executed by a processor, implements the virtual satellite-based observation task optimization method.
[0026] The present invention has the following beneficial effects: This invention proposes a virtual satellite-based observation mission optimization method, a hierarchical and dynamic observation mission optimization method based on the concept of "virtual satellites," suitable for large-scale satellite constellations, and providing a system-level solution to comprehensively improve the observation efficiency of large-scale constellations. The core of this invention is the introduction of a novel "virtual satellite" abstraction layer and a corresponding hierarchical optimization scheduling framework, effectively overcoming the shortcomings of traditional methods: First, through problem decomposition and hierarchical optimization, the global optimization problem of large-scale constellations is transformed into multiple small-scale, parallel-processable local optimization problems, reducing the computational complexity of mission planning and improving solution efficiency. Second, a planning framework capable of rapidly responding to external changes is constructed; when the mission or resource status changes, only local adjustments are needed instead of global recalculation, improving the system's dynamic adaptability and robustness. Third, it can dynamically and logically combine the observation capabilities of different physical satellites according to specific mission requirements, forming temporary "virtual satellites," achieving flexible aggregation and efficient utilization of resources.
[0027] Key innovations of this invention include: proposing a satellite mission planning framework based on the concept of "virtual satellites," which dynamically and logically aggregates physical resources into mission-oriented "virtual satellites" as the basic unit for mission planning and resource scheduling. A hierarchical optimization method is designed to decompose the complex global scheduling problem into a high-level, coarse-grained "mission-virtual satellite" allocation problem and a low-level, fine-grained "virtual satellite internal" physical resource scheduling problem. A dynamic reorganization mechanism for mission planning is proposed, which can locally adjust mission plans based on real-time changes by rapidly generating, disbanding, or modifying virtual satellites, enabling agile responses to unforeseen events.
[0028] Based on the above technological innovations, the significant advantages of this invention compared to existing technologies are specifically reflected in the following aspects: Exceptional scalability: The computation time of traditional centralized algorithms increases exponentially with the number of satellites. This invention reduces the complexity from O(N) level (N is the number of physical satellites) to multiple parallel O(k) level (k is the number of virtual satellite members, and k ≠ N), thereby enabling efficient management of giant constellations composed of hundreds or thousands of satellites.
[0029] Unprecedented agility and dynamic response capabilities: When faced with sudden tasks, traditional methods require hours or even longer for global replanning. The dynamic reconfiguration mechanism of this invention only needs to adjust the affected local virtual satellites, and can generate new feasible solutions within minutes or even seconds, greatly improving the system's practicality and intelligence.
[0030] Higher resource utilization efficiency: This invention achieves on-demand aggregation and coordination of resources by "tailor-making" virtual satellites for specific missions, avoiding the resource mismatch problem existing in traditional methods, that is, avoiding the overuse of resources or insufficient resource capacity. It can more precisely leverage the unique advantages of different satellites in heterogeneous constellations to achieve synergistic effects.
[0031] Excellent openness and compatibility: The abstract layer design of virtual satellites naturally shields the heterogeneity of the underlying physical satellites. This allows satellite resources from different institutions, regions, and types to be easily and conveniently added to the collaborative mission network in a "plug-and-play" manner, providing a technological foundation for building an open and cooperative "space information network."
[0032] Other beneficial effects of the embodiments of the present invention will be further described below. Attached Figure Description
[0033] Figure 1 This is a flowchart of the virtual satellite-based observation task optimization algorithm according to an embodiment of the present invention.
[0034] Figure 2This is a schematic diagram of virtual satellite generation and dynamic reorganization in an embodiment of the present invention.
[0035] Figure 3 This is a flowchart of the observation task optimization method based on virtual satellites according to the present invention. Detailed Implementation
[0036] The embodiments of the present invention will be described in detail below. It should be emphasized that the following description is merely exemplary and not intended to limit the scope and application of the present invention.
[0037] In view of the above-mentioned technical pain points of the existing technology, the present invention aims to solve the following core technical problems: 1. Reduce the computational complexity of task planning: By using the ideas of problem decomposition and hierarchical optimization, the global optimization problem of large-scale constellations is transformed into multiple small-scale, parallel local optimization problems, thereby significantly improving the solution efficiency.
[0038] 2. Enhance the dynamic adaptability and robustness of the system: Build a planning framework that can quickly respond to external changes. When the status of tasks or resources changes, only local adjustments are needed instead of global recalculation, thus achieving agile task replanning.
[0039] 3. Achieve flexible aggregation and efficient utilization of resources: Based on the specific needs of the mission, dynamically and logically combine the observation capabilities from different physical satellites to form temporary, mission-specific "virtual satellites" to achieve refined resource management that precisely tilts resources toward key missions.
[0040] In summary, the core of this invention lies in introducing a novel "virtual satellite" abstraction layer and a corresponding hierarchical optimization scheduling framework to deconstruct the complexity of existing centralized planning, endowing satellite mission systems with unprecedented scalability, flexibility, and response speed, and greatly improving the computational efficiency of the algorithm and the dynamic response capability of the system while ensuring optimization quality.
[0041] See Figure 3 This invention provides a method for optimizing observation tasks based on virtual satellites, comprising the following steps: Step S1. Physical resource modeling and task clustering analysis: Standardize the orbital parameters, sensor capabilities, and on-board resources of the physical satellite; perform clustering analysis on the set of tasks to be observed according to geographical location, time window requirements, and priority.
[0042] In some embodiments, the task clustering analysis includes: aggregating tasks that are spatially adjacent, have overlapping time windows, or require multi-satellite coordination into a unified scheduling task cluster based on the latitude and longitude coordinate distribution of the observation targets, the continuous time intervals allowed for task execution, the task priority level, and the required observation mode type. Specifically, the Haversin formula can be used to calculate the distance between tasks, with an adjacency threshold set, for example, 100 kilometers, and a time window overlap threshold set, for example, 15 minutes, which can be adjusted as needed.
[0043] Step S2. Virtual Satellite Generation and Task Allocation: Based on the task requirements after clustering, virtual satellites are dynamically generated through optimization algorithms. Each virtual satellite is logically aggregated from physical satellites that meet specific task requirements. Task packages are allocated to the corresponding virtual satellites to complete strategic-level optimization.
[0044] In some embodiments, the optimization objective of virtual satellite generation is to maximize the weighted sum of the virtual satellite's overall capability satisfaction with the mission cluster's observation requirements, while minimizing the total resource cost of constructing and maintaining the virtual satellite. The overall capability satisfaction includes the virtual field of view coverage formed by the collaborative efforts of member satellites, the combination of multi-angle imaging capabilities, and the joint revisit period. The virtual field of view coverage can be quantified by the union area of the instantaneous coverage areas of member satellites; the joint revisit period can be calculated by combining the orbital period and phase difference; and the resource cost can be defined as the total attitude maneuver energy consumption or data transmission time cost.
[0045] In some embodiments, the allocation process of the task packages and virtual satellites is implemented using a higher-order allocation algorithm, which optimizes the matching degree between the capabilities of the task clusters and the virtual satellites. This higher-order allocation algorithm can be implemented using, for example (but not limited to), an auction algorithm or a Hungarian algorithm.
[0046] This step can also introduce a rapid internal scheduling simulation module to feed back subsequent tactical-level feasibility predictions to the strategic level, and revise the optimization objectives to reduce the risk of local optima.
[0047] Step S3. Virtual satellite internal task scheduling: Start a local scheduling algorithm for each virtual satellite to assign specific observation tasks and execution sequences to its member physical satellites, maximize task benefits while satisfying physical constraints, and complete tactical layer optimization.
[0048] In some embodiments, the local scheduling algorithm is implemented through the following steps: a) establishing task allocation decision variables for each physical satellite within the virtual satellite; b) ensuring that tasks are only allocated to satellites whose time windows can be covered under visibility constraints; c) controlling the energy consumption of single-satellite attitude maneuvers and the total on-board resource consumption under resource constraints; d) generating a scheduling sequence with the optimization objective of maximizing the total priority weighted value of the tasks undertaken by the virtual satellite. In some embodiments, the local scheduling algorithm employs a heuristic search strategy to optimize the task execution timing under the constraints of steps b)-c).
[0049] Step S4. Status Monitoring and Dynamic Reorganization: Monitor satellite status and mission environment in real time; when an emergency mission, satellite failure, or sudden environmental change is triggered, perform local reorganization by disbanding virtual satellites, allocating physical resources, or creating new virtual satellites to generate an updated scheduling scheme.
[0050] In some embodiments, the triggering conditions for dynamic reorganization include: the addition of a high-priority task that requires immediate response, physical satellite orbital anomalies or functional failures, changes in the external environment that render the original task unexecutable, and poor task execution results; the reorganization process only adjusts the affected virtual satellites and does not require global replanning.
[0051] In some embodiments, the dynamic reorganization specifically performs any of the following operations: disbanding the lowest-priority virtual satellite to release its physical resources; drawing available satellites from virtual satellites performing non-urgent tasks to enhance the capabilities of high-priority virtual satellites; and dynamically creating high-priority dedicated virtual satellites for urgent tasks and allocating physical resources accordingly. Furthermore, before drawing satellites, it can be assessed whether the remaining capabilities of the original virtual satellites meet the core mission requirements; if not, their disbandment or reorganization is triggered simultaneously to ensure mission continuity.
[0052] In some embodiments, the lifecycle of a virtual satellite is dynamically defined by the start and end times of the associated mission, and it is automatically disbanded when the mission ends to release resources for reorganization.
[0053] The virtual satellite-based observation mission optimization method proposed in this invention significantly improves the efficiency of large-scale satellite constellation mission planning by introducing a virtual satellite abstraction layer and a hierarchical optimization framework. First, it adopts a virtual satellite dynamic logic aggregation to manage resources, decomposing the global NP-hard problem into a parallel optimization problem of strategic-level virtual satellite generation and allocation and tactical-level internal scheduling, greatly reducing computational complexity. Second, it designs a local dynamic reorganization mechanism, achieving second-level response to emergencies by simply disbanding / reassigning / creating virtual satellites, breaking through the bottleneck of traditional global replanning. At the same time, it customizes the combination of heterogeneous satellite capabilities based on mission requirements, realizing on-demand resource collaboration and refined utilization, eliminating resource mismatch. Finally, it relies on the virtualization abstraction layer to shield the underlying heterogeneity, providing a technical foundation for plug-and-play collaboration of multi-source heterogeneous constellations, and comprehensively achieving a system-level improvement in scalability, agility, and resource utilization efficiency.
[0054] The following describes specific embodiments of the present invention.
[0055] A virtual satellite-based observation mission optimization method is proposed, which achieves efficient and dynamic planning for large-scale satellite constellation observation missions by constructing a hierarchical optimization framework based on virtual satellites. For example... Figure 1 As shown, the specific steps of the solution are as follows: 1. Physical Layer and Virtual Layer Modeling Physical resource modeling: Standardize the description of each physical satellite (PS) in the constellation, including its orbital six-pointer, sensor parameters (field of view, resolution, spectral range, maneuverability (attitude adjustment angular velocity)), and on-board resources (energy, storage, computing power), etc.
[0056] Definition of Virtual Satellite: The concept of a "Virtual Satellite" (VS) is introduced. A virtual satellite is a logical entity, a temporary collection of physical satellite observation resources dynamically constructed for a specific mission or class of missions. A virtual satellite is defined by the following elements: Member set: The list of physical satellites that make up this virtual satellite PS_set=PS_1,PS_2,...,PS_k.
[0057] Capability set: A "virtual capability" formed by the aggregation of capabilities of a member set, such as the "virtual field of view" covered by collaborative observation, multi-angle imaging capability, revisit cycle, etc.
[0058] Lifecycle: The time from the creation, activation, and disbandment of a virtual satellite.
[0059] 2. Dynamic Virtual Satellite Generation and Task Allocation This is the first layer of optimization in this framework (strategic layer optimization).
[0060] Task clustering: First, the input list of targets to be observed, T=t_1,t_2,...,t_m, is analyzed and clustered according to the target's geographical location, observation time window requirements, priority, and required observation mode (such as stereo imaging, continuous monitoring, etc.).
[0061] Virtual Satellite Generation: For each type of mission requirement, an optimal virtual satellite is created using a "virtual satellite generation algorithm." The algorithm's goal is to find the most cost-effective combination of physical resources for the mission cluster. Its objective function can be designed as follows: in, w_i represents the degree to which the virtual satellite VS satisfies the capability of the i-th type of task; Lambda_j represents the cost of the j-th type of resources (such as total maneuvering cost, communication bandwidth, etc.) required to build and operate the virtual satellite, where lambda_j is the cost coefficient.
[0062] Task-Virtual Satellite Assignment: Assign clustered task packages to the most suitable generated virtual satellites. This is a high-order assignment problem, but its solution complexity is significantly reduced because the number of virtual satellites is much smaller than that of physical satellites.
[0063] 3. Virtual satellite internal task scheduling This is the second layer of optimization in this framework (tactical layer optimization).
[0064] Problem decomposition: Once the task package is assigned to a virtual satellite VS_k, the global planning problem is decomposed into a small-scale scheduling problem limited to VS_k.
[0065] Internal Scheduling Algorithm: Within the virtual satellite, an efficient local task scheduling algorithm (such as an improved ant colony algorithm or constrained programming CP) is initiated. This algorithm assigns specific observation tasks and execution sequences to each member physical satellite PS_j of VS_k. Its optimization objective is to maximize the total benefit of the tasks undertaken by the virtual satellite while satisfying all physical constraints. Its mathematical model can be simplified as follows: Objective function: Constraints: Visibility constraint: X_tp=1, Rightarrowt is within the observable time window of p.
[0066] Resource constraints: The attitude maneuvering, energy, and other resource consumption of satellite p are within its capacity limits. Here, T_VS_k is the task set assigned to virtual satellite VS_k, PS_set_k is its member set, P_t is the priority of task t, and X_tp is the decision variable (1 if task t is executed by satellite p, 0 otherwise).
[0067] 4. Dynamic Reorganization and Feedback Mechanism Status monitoring: The system continuously monitors the health status of all physical satellites, mission progress, and changes in the external environment (such as new unforeseen missions).
[0068] Dynamic reconfiguration trigger: The dynamic reconfiguration mechanism is activated when one of the following trigger conditions is detected: a high-priority emergency task occurs; a physical satellite malfunctions or deviates from its orbit; or the mission performs poorly (e.g., due to weather conditions).
[0069] Rapid replanning: The reconfiguration process is local. For example, to address an urgent mission, the system does not need to recalculate the global plan; it can simply quickly generate a new, high-priority virtual satellite or "dig" physical resources from existing low-priority virtual satellites to enhance the capabilities of a critical virtual satellite. This local adjustment requires minimal computation and can achieve a response time within seconds.
[0070] like Figure 2 The diagram illustrates the process of virtual satellite generation and dynamic reconfiguration. Initially, based on the requirements of Task A (high-resolution imaging) and Task B (wide-area surveillance), virtual satellites VS_A (members PS1 and PS3) and VS_B (members PS2, PS4, and PS5) are generated from the physical satellite constellation (P) resource pool to execute Task A and Task B, respectively. When a high-priority emergency task C occurs, the dynamic reconfiguration mechanism is triggered. Specifically, VS_A is disbanded, resource PS3 is reallocated, and a new virtual satellite VS_C (members PS3 and PS6) is created to execute emergency task C. This entire process demonstrates the system's rapid local replanning capability in response to sudden emergency tasks.
[0071] Through the above steps, this invention successfully decomposes a large-scale, integrated NP-hard problem into two much smaller optimization problems: "virtual satellite generation and allocation" and "virtual satellite internal allocation". This greatly improves the computational efficiency of the algorithm and the dynamic response capability of the system while ensuring the optimization quality.
[0072] In summary, this invention proposes a virtual satellite-based observation mission optimization method. Key innovations include: 1. A satellite mission planning framework based on the concept of "virtual satellites." Its core is the on-demand, dynamic, and logical aggregation of physical resources into mission-oriented "virtual satellites," serving as the basic unit for mission planning and resource scheduling. 2. A hierarchical optimization method. This method decomposes the complex global scheduling problem into two levels: a) a high-level, coarse-grained "mission-virtual satellite" allocation problem; b) a low-level, fine-grained "virtual satellite internal" physical resource scheduling problem. 3. A dynamic reorganization mechanism for mission planning. This mechanism can locally adjust mission plans based on real-time changes by rapidly generating, disbanding, or modifying virtual satellites, rather than global replanning, thereby achieving agile response to unforeseen events.
[0073] Compared with the prior art, the significant technical advantages of the present invention are specifically reflected in the following aspects: Exceptional scalability: The computation time of traditional centralized algorithms increases exponentially with the number of satellites. This invention reduces the complexity from O(N) level (N is the number of physical satellites) to multiple parallel O(k) level (k is the number of virtual satellite members, and k ≠ N), thereby enabling efficient management of giant constellations composed of hundreds or thousands of satellites.
[0074] Unprecedented agility and dynamic response capabilities: When faced with sudden tasks, traditional methods require hours or even longer for global replanning. The dynamic reconfiguration mechanism of this invention only needs to adjust the affected local virtual satellites, and can generate new feasible solutions within minutes or even seconds, greatly improving the system's practicality and intelligence.
[0075] Higher resource utilization efficiency: This invention achieves on-demand aggregation and coordination of resources by "tailor-making" virtual satellites for specific missions, avoiding the resource mismatch problem existing in traditional methods, that is, avoiding the overuse of resources or insufficient resource capacity. It can more precisely leverage the unique advantages of different satellites in heterogeneous constellations to achieve synergistic effects.
[0076] Excellent openness and compatibility: The abstract layer design of virtual satellites naturally shields the heterogeneity of the underlying physical satellites. This allows satellite resources from different institutions, regions, and types to be easily and conveniently added to the collaborative mission network in a "plug-and-play" manner, providing a technological foundation for building an open and cooperative "space information network."
[0077] This invention also provides a storage medium for storing a computer program, which, when executed, performs at least the methods described above.
[0078] This invention also provides a control device, including a processor and a storage medium for storing a computer program; wherein the processor executes the computer program by performing at least the method described above.
[0079] This invention also provides a processor that executes a computer program, at least performing the methods described above.
[0080] The storage medium can be implemented by any type of non-volatile storage device, or a combination thereof. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc or CD-ROM; magnetic surface memory can be disk storage or magnetic tape storage. The storage media described in the embodiments of this invention are intended to include, but are not limited to, these and any other suitable types of memory.
[0081] In the several embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.
[0082] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.
[0083] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.
[0084] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0085] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.
[0086] The methods disclosed in the several method embodiments provided by this invention can be arbitrarily combined without conflict to obtain new method embodiments.
[0087] The features disclosed in the several product embodiments provided by this invention can be arbitrarily combined without conflict to obtain new product embodiments.
[0088] The features disclosed in the several method or device embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method or device embodiments.
[0089] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various equivalent substitutions or obvious modifications can be made without departing from the concept of the present invention, and all such modifications, achieving the same performance or application, should be considered within the scope of protection of the present invention.
Claims
1. A method for optimizing observation tasks based on virtual satellites, characterized in that, Includes the following steps: S1. Physical resource modeling and task clustering analysis: Model the orbital parameters, sensor capabilities, and on-board resources of the physical satellite; perform clustering analysis on the set of tasks to be observed according to geographical location, time window requirements, and priority; S2. Virtual Satellite Generation and Task Allocation: Based on the task requirements after clustering, virtual satellites are dynamically generated through optimization algorithms. Each virtual satellite is logically aggregated from physical satellites that meet specific task requirements. Task packages are allocated to the corresponding virtual satellites to complete strategic-level optimization. S3. Virtual satellite internal task scheduling: Start a local scheduling algorithm for each virtual satellite to assign specific observation tasks and execution sequences to its member physical satellites, maximize task benefits while satisfying physical constraints, and complete tactical layer optimization; S4. Status Monitoring and Dynamic Reorganization: Real-time monitoring of satellite status and mission environment; when an emergency mission, satellite failure, or sudden environmental change is triggered, local reorganization is performed by disbanding virtual satellites, allocating physical resources, or creating new virtual satellites to generate an updated scheduling plan.
2. The method as described in claim 1, characterized in that, In step S1: The task clustering analysis includes: based on the latitude and longitude coordinate distribution of the observation targets, the continuous time intervals during which the tasks are allowed to be executed, the task priority level, and the required observation mode type, tasks that are spatially adjacent, have overlapping time windows, or require multi-satellite coordination are aggregated into task clusters for unified scheduling.
3. The method as described in claim 1 or 2, characterized in that, In step S2: The optimization objective of virtual satellite generation is to maximize the weighted sum of the comprehensive capability of the virtual satellite to meet the observation requirements of the mission cluster, while minimizing the total resource cost of building and maintaining the operation of the virtual satellite. The overall capability satisfaction includes the virtual field of view coverage formed by the collaboration of member satellites, the combination of multi-angle imaging capabilities, and the joint revisit cycle index.
4. The method as described in claim 3, characterized in that, In step S2: The allocation process of the task packages and virtual satellites is implemented through a high-order allocation algorithm, which is optimized based on the capability matching degree between the task clusters and virtual satellites.
5. The method as described in claim 1 or 2, characterized in that, In step S3: The local scheduling algorithm is implemented through the following steps: a) Establish task allocation decision variables for each physical satellite within the virtual satellite; b) Ensure that missions are assigned only to satellites whose time windows can be covered under visibility constraints; c) Control the energy consumption of single-satellite attitude maneuvering and the total amount of onboard resources under resource constraints; d) Generate a scheduling sequence with the goal of maximizing the total priority weighted value of the tasks undertaken by the virtual satellite.
6. The method as described in claim 5, characterized in that, The local scheduling algorithm employs a heuristic search strategy to optimize the task execution timing under the constraints of steps b)-c).
7. The method as described in claim 1 or 2, characterized in that, In step S4: Dynamic reconfiguration is triggered by the following conditions: the addition of a high-priority task that requires immediate response, physical satellite orbital anomalies or functional failures, and changes in the external environment that render the original task unexecutable. The reconfiguration process only adjusts the affected virtual satellites and does not require global replanning.
8. The method as described in claim 7, characterized in that, The dynamic reorganization specifically performs any of the following operations: Disband the lowest priority virtual satellite to release its physical resources; Available satellites are drawn from virtual satellites performing non-urgent missions; Dynamically create dedicated virtual satellites and allocate physical resources for emergency missions.
9. The method according to any one of claims 1-2, characterized in that, The lifecycle of a virtual satellite is dynamically defined by the start and end times of the associated missions. When a mission ends, the satellite is automatically disbanded to release resources for reorganization and reuse.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the virtual satellite-based observation task optimization method as described in any one of claims 1 to 9.