Multi-satellite collaborative observation task conflict resolution method based on virtual resource pool

Abstract

The invention discloses a multi-satellite collaborative observation task conflict resolution method based on a virtual resource pool. The method mainly comprises the following steps: firstly, constructing a unified space-time coverage description model, discretizing the earth surface into a multi-level grid, allocating codes, segmenting a time domain into discrete time slices, and preprocessing satellite data to obtain a grid-time slice two-tuple index; establishing a virtual resource pool which comprises a primary table and a secondary table, writing the primary table and the secondary table into a two-tuple in batches and dynamically maintaining the two-tuple; then conflict pre-search is carried out to generate a candidate set; rejecting items which do not meet requirements through attribute and time screening, and detecting conflicts; and finally, combining the time sequence to generate a scheduling sequence, and outputting a scheme by taking earliest completion of global coverage as a target. Compared with the prior art, the method has the advantages that the planning efficiency and the dynamic response capability of large-scale multi-satellite collaborative observation are remarkably improved, the method has remarkable advantages in the aspects of efficiency, flexibility, expandability and modularization, and more reliable and lower-delay multi-satellite collaborative observation planning service can be provided in a large-scale dynamic scene.

Description

Technical Field

[0001] This invention relates to satellite remote sensing mission planning technology, and more particularly to a dynamic arbitration mechanism for observation conflicts in satellite constellations, and especially to a method for resolving conflicts in multi-satellite collaborative observation missions based on a virtual resource pool. Background Technology

[0002] In recent years, with the continuous expansion of satellite constellation scale and the increasing demand for high spatiotemporal resolution ground observation, multi-satellite collaborative observation mission planning has become a research hotspot in the field of remote sensing. Existing technologies mainly focus on how to decompose user-proposed point or area targets into sub-tasks that can be performed by different satellite resources, and how to quickly and accurately generate observation schemes under the constraints of visibility windows and sensor performance of each satellite.

[0003] Traditional methods first divide the target area into several sub-regions or point targets to accommodate different observation modes of each satellite. Then, for each satellite and sub-task, a visible time window is determined based on trajectory calculation and sensor field-of-view bandwidth. Finally, within these visible windows, a mixed-integer or nonlinear programming model is constructed with the goal of maximizing observation gains, and a global search is performed using commercial solvers or heuristic algorithms (such as tabu search and genetic algorithms). This process can achieve relatively good solutions in small- to medium-sized constellations and mission scenarios and has been validated in several studies. However, this "calculate first, then plan" approach suffers from an exponential increase in computational load with the number of satellites and sub-tasks. Furthermore, when the mission scale expands to hundreds or thousands, the solution time often fails to meet the response requirements of minutes or even seconds. In addition, the inconsistent visibility description specifications of different satellites necessitate repeated calculations of the visible window for each satellite after mission input, further exacerbating the computational burden.

[0004] Therefore, how to propose a more efficient and unified conflict resolution mechanism has become an urgent problem to be solved.

[0005] The main process of existing multi-satellite multi-task planning is as follows: (1) Input: User observation requirements (point target / regional target) → transformed into a spatial grid set.

[0006] (2) Decomposition: The regional target is divided into smaller sub-regions or point targets.

[0007] (3) Visibility judgment: For the decomposed sub-tasks, calculate the visible grid set Gi of each satellite at time Ti.

[0008] (4) Modeling: The decision variable represents "whether the task is executed within the visible window", and the objective function is to maximize the observed benefits.

[0009] (5) Solving: Use commercial solvers or heuristic algorithms to search for the optimal or near-optimal solution in the global solution space.

[0010] Existing multi-satellite collaborative observation methods have the following shortcomings: Inconsistent description specifications: Different satellites use "nadir trajectory + swath width + time point sequence" for description, requiring the visible window to be recalculated for each satellite and each mission after the mission input, and the amount of calculation increases exponentially with the number of satellites and missions.

[0011] High computational complexity and low efficiency: Existing models and solution algorithms are mainly validated in scenarios with hundreds of satellites. Once the scale reaches thousands or more, traditional branch and bound or heuristic methods are prone to getting trapped in local optima and the solution time cannot meet the requirements of minute-level or second-level response.

[0012] In multi-satellite collaborative observation mission planning, the traditional "calculate first, plan later" approach faces bottlenecks in several aspects, including redundant calculations of visibility windows, conversion of heterogeneous description specifications, slow dynamic task response, and strong coupling between conflict resolution and global planning. With the explosive growth in constellation size and observation needs, the system can no longer meet the requirements of real-time and near-real-time application scenarios in terms of efficiency, flexibility, and scalability.

[0013] 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

[0014] The main objective of this invention is to overcome the deficiencies in the aforementioned background technology and provide a method for resolving conflicts in multi-satellite collaborative observation missions based on virtual resource pools, thereby improving the real-time performance, efficiency, and scalability of large-scale multi-satellite collaborative observation mission planning.

[0015] To achieve the above objectives, the present invention adopts the following technical solution: A method for resolving conflicts in multi-satellite collaborative observation missions based on a virtual resource pool includes the following steps: S1. Construct a unified spatiotemporal coverage description model: Discretize the area to be observed on the Earth's surface into a multi-level global grid, assign a spatial code to each grid, and divide the time domain into discrete time slices; preprocess the orbit and payload field of view data of each satellite, and map their visible intervals into a binary index of grid code and time slice; S2. Establish a virtual resource pool: Create a main table with grid code as the primary key to store the time list and the list of available sensors, and create a secondary table with sensor identifier as the primary key to record sensor attributes; write the binary index from step S1 into the resource pool in batches, and dynamically maintain the consistency between the resource pool and the actual constellation status. S3. Conflict Pre-Search: Receives the user's observation requirement grid set, searches the resource pool master table in parallel, and generates a candidate set containing sensor identifiers and time slices; S4. Attribute and Time Filtering: Sensors that do not meet the resolution or load requirements are removed based on the attributes in the sub-table. The optimal time segment is selected according to the time density requirements, and time conflicts with the locked task are detected. S5. Time Series Merging and Scheduling Generation: The discrete time slices of the same sensor are merged into a continuous window, a scheduling sequence is generated and globally sorted, and the observation scheme is output with the goal of achieving full coverage as early as possible.

[0016] Furthermore, in step S1: The multi-level global subdivision grid adopts a hierarchical spatial coding structure; The preprocessing includes calculating the satellite's coverage capability over the grid and mapping the coverage relationship to a grid code and a set of time slice tuples; Redundancy analysis is used to select the grid level to balance accuracy and storage overhead.

[0017] Furthermore, in step S2: The main table storage structure satisfies the following: using grid encoding as the primary key to associate the corresponding time list and the available sensor identifier list; The secondary table storage structure satisfies the following: using the sensor identifier as the primary key to record resolution, payload type, and field of view attributes; The main table and the secondary table establish a bidirectional index constraint relationship through sensor identifiers; Run-length encoding is used to compress the time list data in the main table.

[0018] Furthermore, in step S2: The dynamic maintenance includes: synchronously modifying the data in the main table and the sub-table through incremental updates in response to orbital fine-tuning or satellite changes.

[0019] Furthermore, in step S3: The parallel retrieval specifically includes: traversing the user demand grid set and extracting all associated sensor identifiers and time slices from the main table; Record the earliest and latest observable time slices for each sensor identifier for subsequent optimization.

[0020] Furthermore, in step S4: The attribute filtering includes filtering the candidate set based on the resolution and load type attributes of the sub-table; The time density filtering includes: for scenes that need to be observed repeatedly, retaining the optimal coverage segment according to the time interval and frequency requirements.

[0021] Furthermore, in step S4: The time conflict detection includes: determining whether the candidate sensor time slice overlaps with the locked task, and eliminating conflicting items according to priority or the earliest completion time.

[0022] Furthermore, in step S5: The time-series merging includes: aggregating discrete time slices of the same sensor into a set of continuous observation windows; The global sorting aims to minimize the time required to complete global coverage.

[0023] Furthermore, the method also includes: Steps S1-S5 are encapsulated as interface services, supporting on-demand invocation of functions such as request submission, index retrieval, attribute filtering, and time series merging.

[0024] A computer program product includes a computer program that, when executed by a processor, implements the aforementioned multi-satellite collaborative observation mission conflict resolution method based on a virtual resource pool.

[0025] The present invention has the following beneficial effects: This invention proposes a conflict resolution method for multi-satellite collaborative observation tasks based on a virtual resource pool, achieving real-time dynamic planning and scheduling with second-level accuracy, high reliability, and high resource utilization for large-scale multi-satellite collaborative observation tasks. Key innovations include the construction of a unified spatiotemporal coverage description model, a virtual resource pool architecture, an online conflict pre-screening and merging mechanism, and a dynamic incremental scheduling and caching strategy. Specifically, the unified spatiotemporal coverage description model constructs a grid-time series binary index structure by combining a multi-level global partitioned grid with discrete time segments, eliminating heterogeneous data compatibility and redundant computation. The virtual resource pool architecture supports high-concurrency, low-latency retrieval with a two-level storage system of main and secondary tables. The online conflict pre-screening and merging mechanism decouples conflict resolution from scheduling. The dynamic incremental scheduling and caching strategy ensures rapid system response to sudden demands. These innovations bring several beneficial effects, including eliminating redundant computation within the visible window, unifying heterogeneous description specifications, improving dynamic task response capabilities, decoupling conflict resolution from global solution, and optimizing large-scale scalability. By pre-storing satellite visibility, unifying the description method, enabling incremental retrieval and conflict pre-screening, removing the conflict resolution mechanism, and completing most of the screening work in the early stage, the planning efficiency and dynamic response capability of large-scale multi-satellite collaborative observation are significantly improved.

[0026] Compared with existing technologies, this invention has significant advantages in terms of efficiency, flexibility, scalability, and modularity, and can provide more reliable and lower-latency multi-satellite collaborative observation planning services in large-scale dynamic scenarios, specifically in the following aspects: 1. Significantly Reduced Time Complexity: Traditional solutions require repeated calculation of the visibility window for each satellite and each sub-task. This invention pre-indexes the visibility into the database, reducing the retrieval complexity from O(N•M) to near O(logK) or O(1) levels (N: number of satellites, M: number of tasks, K: index size). In scenarios with thousands of constellations and tens of thousands of tasks, the overall planning time can be reduced from minutes or hours to hundreds of milliseconds to seconds. This complexity optimization is based on the characteristics of index retrieval; the actual response time is related to the number of grids involved in a single query, achieving a near-constant level of high efficiency.

[0027] 2. Eliminate heterogeneous data compatibility overhead: Existing technologies require separate conversion of visibility formats for different sensors or manufacturers. This invention uses a unified grid-time series model for one-time mapping, avoiding multiple rounds of data adaptation and error accumulation. The data preprocessing and scheduling stages are decoupled, reducing system complexity and potential failure points caused by interface conversion.

[0028] 3. Enhanced dynamic adjustment and online response capabilities: In the past, global planning often required a full recalculation for new tasks or priority changes. This invention supports incremental retrieval and local merging based on resource pools, without restarting the underlying solution, ensuring the system can respond to emergencies within seconds.

[0029] 4. Decoupling of conflict resolution and solution process: Traditional conflict resolution is deeply embedded in scheduling algorithms, making it difficult to reuse or optimize independently; This invention modularizes mechanisms such as conflict pre-screening, attribute filtering, and time merging, which can be seamlessly integrated with various backend optimization algorithms, improving the maintainability and scalability of the system.

[0030] 5. Enhanced resource utilization and coverage efficiency: The optimized index based on a multi-level grid not only reduces storage redundancy but also allows for flexible selection of grid levels according to different resolution requirements, improving the precision of resource allocation. The conflict pre-screening and merging mechanism, while meeting coverage requirements, minimizes redundant observations, achieving the goal of "fewest satellites, shortest time" for full-area coverage.

[0031] Other beneficial effects of the embodiments of the present invention will be further described below. Attached Figure Description

[0032] Figure 1 This is a framework diagram for conflict resolution in multi-satellite collaborative observation missions based on virtual resource pools, according to an embodiment of the present invention.

[0033] Figure 2 This is a schematic diagram of a virtual resource pool model for multi-satellite Earth observation capabilities according to an embodiment of the present invention.

[0034] Figure 3 This is a schematic diagram of a multi-star conflict resolution method based on a virtual resource pool according to an embodiment of the present invention.

[0035] Figure 4 This is the overall flowchart of the multi-satellite collaborative observation mission conflict resolution method based on virtual resource pools of 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 is not intended to limit the scope and application of the present invention.

[0037] See Figure 4 This invention provides a method for resolving conflicts in multi-satellite collaborative observation missions based on a virtual resource pool, comprising the following steps: Step S1. Construct a unified spatiotemporal coverage description model: Discretize the Earth's surface into a multi-level global grid, assign a spatial code to each grid, and divide the time domain into discrete time slices; preprocess the orbit and payload field of view data of each satellite, and map their visible intervals into a binary index of grid code and time slice.

[0038] In some embodiments, the multi-level global subdivision grid adopts a hierarchical spatial coding structure; the preprocessing includes calculating the coverage capability of satellites on the grid and mapping the coverage relationship to a grid code and a set of time slice tuples; and selecting the grid level through redundancy analysis to balance accuracy and storage overhead.

[0039] Step S2. Establish a virtual resource pool: Create a main table with grid code as the primary key to store the time list and the list of available sensors, and create a secondary table with sensor identifier as the primary key to record sensor attributes; write the binary index from step S1 into the resource pool in batches, and dynamically maintain the consistency between the resource pool and the actual constellation status.

[0040] In some embodiments, the main table storage structure satisfies the following: a grid code is used as the primary key to associate the corresponding time list and the available sensor identifier list; the secondary table storage structure satisfies the following: a sensor identifier is used as the primary key to record resolution, payload type, and field of view attributes; the main table and the secondary table establish a bidirectional index constraint relationship through the sensor identifier; and run-length encoding is used to compress the time list data in the main table.

[0041] In some embodiments, the dynamic maintenance includes: synchronously modifying the data in the main table and the slave table through incremental updates in response to orbit fine-tuning or satellite changes. Dynamic maintenance can be implemented by monitoring the satellite status message queue, asynchronous background updates, or a read-write separation mechanism to ensure the continuity of query services during the update process.

[0042] Step S3. Conflict Pre-Search: Receive the user observation requirement grid set, search the resource pool master table in parallel, and generate a candidate set containing sensor identifiers and time slices.

[0043] In some embodiments, the parallel retrieval specifically includes: traversing the user demand grid set, extracting all associated sensor identifiers and time slices from the main table; and recording the earliest and latest observable time slices for each sensor identifier for subsequent optimization.

[0044] Step S4. Attribute and Time Filtering: Based on the attributes in the sub-table, sensors that do not meet the resolution or load requirements are removed. The optimal time segment is selected according to the time density requirements, and time conflicts with the locking task are detected.

[0045] In some embodiments, the attribute filtering includes: filtering the candidate set based on the resolution and load type attributes of the sub-table; the time density filtering includes: for scenes that need to be repeatedly observed, retaining the optimal coverage segment according to the time interval and number of observations required.

[0046] In some embodiments, the time conflict detection includes: determining whether candidate sensor time slices overlap with locked tasks, and eliminating conflicting items based on priority or earliest completion time. Conflict resolution may prioritize task priority rules, and under the same priority, filtering may be based on the earliest completion time. The specific strategy can be configured according to actual needs.

[0047] Step S5. Time series merging and scheduling generation: The discrete time slices of the same sensor are merged into a continuous window, a scheduling sequence is generated and globally sorted, and the observation scheme is output with the goal of achieving full coverage as early as possible.

[0048] In some embodiments, the time-series merging includes: aggregating discrete time slices of the same sensor into a set of continuous observation windows; the global sorting aims to minimize the time of completion of full coverage.

[0049] In some embodiments, the multi-satellite collaborative observation task conflict resolution method based on a virtual resource pool further includes: encapsulating steps S1-S5 into an interface service to support on-demand invocation of functions such as request submission, index retrieval, attribute filtering, and time series merging.

[0050] The multi-satellite collaborative observation task conflict resolution method proposed in this invention systematically solves the problems of computational redundancy, poor heterogeneous compatibility, response delay and strong coupling in traditional multi-satellite observation planning by constructing a unified multi-level grid-time index model and virtual resource pool architecture. Its innovations are reflected in the following aspects: It adopts a globally partitioned grid to uniformly map the visibility data of heterogeneous satellites, pre-stores it as a spatiotemporal binary index, completely eliminating the exponential overhead of repeatedly calculating the visibility window per satellite and per task in traditional methods; it designs a virtual resource pool with linked master and slave tables to achieve "storage for time," and combines run-length encoding compression and incremental update mechanisms to significantly reduce the complexity of real-time retrieval; it pioneers a conflict pre-screening and decoupling mechanism, rapidly generating candidate sets through parallel retrieval of the resource pool, achieving dynamic conflict detection based on sensor attribute filtering and temporal density analysis, and then independently generating executable schedules through time-series merging, separating conflict resolution from the global solution; ultimately achieving second-level response capability at a scale of thousands of constellations, while adapting to observation accuracy requirements through multi-level grids, reducing redundant coverage, optimizing the efficiency of global observation with "fewest satellites and shortest time," and comprehensively improving the system's real-time performance, scalability, and resource utilization.

[0051] The following describes specific embodiments of the present invention.

[0052] This invention focuses on constructing a unified spatiotemporal coverage index model and a virtual resource pool to achieve "storage for time," on-demand retrieval, and online conflict pre-screening, thereby significantly improving the planning efficiency and dynamic response capability of large-scale multi-satellite collaborative observations. Specifically, these objectives are achieved through the following aspects: 1. Eliminating Redundant Visibility Window Calculations: Traditional methods require trajectory and field-of-view analysis for all satellites and sub-tasks in each planning iteration, with the computational load increasing exponentially with the number of satellites and missions. This proposal addresses this by constructing a unified grid-temporal binary index to pre-store the visibility of each satellite in a resource pool, avoiding repetitive calculations in each planning iteration.

[0053] 2. Unified Heterogeneous Description Standard: Different satellite manufacturers in the market use different descriptions of visibility, often requiring format compatibility and conversion during the initial planning stages. This application proposes to map multiple description methods to a unified multi-level global mesh model, eliminating the need for repeated adaptation for each satellite and improving data processing consistency.

[0054] 3. Improve dynamic task response capabilities: Traditional global planning processes are sensitive to new urgent tasks or priority adjustments, often requiring the entire solution process to be re-executed. This application proposes to implement on-demand incremental retrieval and conflict pre-screening at the index level, supporting the rapid insertion and removal of tasks from the existing resource pool, ensuring a response time of seconds or sub-seconds.

[0055] 4. Decoupling Conflict Resolution from Global Solution: Existing conflict resolution methods typically eliminate non-compliant solutions through hard constraints in the global model, making it difficult to optimize independently of the planning algorithm. This application proposes to decouple conflict pre-screening, attribute filtering, and time merging mechanisms from the solution algorithm, forming reusable online service modules to enhance the loose coupling of the system.

[0056] 5. Optimized Large-Scale Scalability: For constellations with thousands of entries and observation tasks involving tens of thousands of entries, the efficiency of traditional heuristic algorithms and commercial solvers drops sharply. By relying on a virtual resource pool and index retrieval, this invention completes most of the filtering work during the retrieval and conflict resolution stages, inputting only a small subset of the filtered results into the backend scheduling, significantly reducing the solution complexity.

[0057] A framework for conflict resolution in multi-satellite collaborative observation missions based on virtual resource pools, such as... Figure 1 As shown, the specific steps of the solution are as follows: Step 1: Construct a unified spatiotemporal coverage description model: The spherical surface is discretized using a multi-level global mesh (e.g., S2 or H3 mesh), and a unique spatial code gcode is assigned to each level of mesh. The observation time domain is divided into discrete time slices Ti of fixed length; The orbit and payload field of view data of each satellite are preprocessed and mapped into several (gcode, Ti) tuples to form a satellite visibility index. Based on the redundancy analysis results, a suitable grid level is selected to balance coverage accuracy and storage overhead.

[0058] Step 2: Create a virtual resource pool: See Figure 2 A virtual resource pool model showcasing the Earth observation capabilities of multiple satellites (such as Sat1, Sat2, and Sat3); Create a master table MasterList, using gcode as the primary key, to store the corresponding discrete-time list TiList and a list of all SenIDs available within that spatiotemporal unit. Figure 2 The example of a specific spatial grid cell, Grid1, is shown in the image. Create a sub-table SubList with SenID as the primary key, recording the resolution, payload type, maximum field of view, and other attributes of each sensor. The primary table and the sub-table must satisfy the following constraints: ; The subscript i is used to distinguish different sensor identifiers (SenID) in the master list, and the subscripts j and k are used to distinguish different sensor association items in the sublist.

[0059] The constraint relationship can be: all sensor identifiers in the sub-table exist in the list of available sensors in the main table, and all sensor identifiers in the main table have corresponding attribute records in the sub-table.

[0060] Write all (gcode,Ti) tuples and their corresponding SenIDs obtained in step 1 into the virtual resource pool in batches, and apply run-length encoding to compress and store TiList in the main table. Figure 2 T1, T2, and T3 in TiList are instantiations of specific discrete time slices; During operation, for orbital fine-tuning or the addition / retirement of satellites, the MasterList / SubList is maintained synchronously through online incremental updates to ensure that the resource pool is always consistent with the actual constellation dynamics.

[0061] Step 3, Conflict Pre-Search: Receive the set of observation requests UG (represented by a grid gcode list) submitted by users or the superior scheduling system; Iterate through MasterList in parallel, retrieve all items (gcode∈ UG, anyTi), and collect a preliminary candidate set C0 = {(SenID,Ti)}; In the above retrieval, the earliest observable Ti_start and the latest observable Ti_end are recorded for each SenID for subsequent scheduling optimization.

[0062] Step 4: Filter by attribute and time: Remove SenIDs from SubList that do not meet the resolution or payload type requirements from the candidate set C0 to obtain C1; For scenarios that require repeated observation or continuous tracking, each group (SenID, TiList) is density-filtered based on the Ti time interval and number of times required, and the time segment that best meets the requirements of coverage uniformity or number of times is retained to generate C2; During the screening process, it is also determined whether the selected SenID overlaps with other locked task time periods. If so, the next best candidate is removed based on priority rules or the earliest completion time finishT metric. See the screening process below. Figure 3 A schematic diagram of a multi-star conflict resolution method based on a virtual resource pool.

[0063] Step 5: Timing merging and schedule generation: After filtering, the multiple Ti time points corresponding to each SenID are merged into a continuous or discrete time window set ReT; Calculate the observation start and end window [min(ReT), max(ReT)] for each SenID, and generate the final scheduling sequence ReSenT = {(SenID, ReT)}; The ReSenT is globally sorted, and the final observation execution plan is output with the goal of minimizing finishT at the earliest time when global coverage is completed.

[0064] Step 6: System Deployment and Simulation Verification. The above steps are encapsulated into a unified interface service, including functional modules such as requirement submission, index retrieval, attribute filtering, and merging scheduling. Experiments on "rapid coverage of large areas", "high-density coverage of multiple targets" and "continuous tracking of moving targets" are carried out in typical simulation scenarios to verify the improvement effect of this method on the response time, resource utilization and coverage continuity compared with the existing technology.

[0065] In summary, this invention proposes a method for resolving conflicts in multi-satellite collaborative observation missions based on a virtual resource pool. The key innovative contributions of this invention include: A unified spatiotemporal coverage description model was designed. It utilizes a multi-level global mesh (such as S2 or H3 hierarchical coding) combined with discrete time segments to construct a mesh-time series binary index structure. The visibility information of each satellite is mapped and stored in a fixed format, which completely eliminates heterogeneous data compatibility and redundant calculations. A virtual resource pool architecture is proposed, which designs a two-level storage system: MasterList (main table) and SubList (secondary table). The former uses grid coding as the primary key, while the latter records attributes such as sensor resolution and payload type. By establishing indexes and inverted indexes between the tables, it supports high-concurrency and low-latency spatiotemporal coverage retrieval and is a basic component for elastic scheduling. An online conflict pre-screening and merging mechanism was designed. During the search phase, the candidate set of satellite-time that meets the requirements is quickly located through grid-time binary pairs. Then, based on the sensor attributes in SubList, the candidates that do not meet the resolution or payload requirements are eliminated. Finally, time merging is performed to merge multiple times of the same sensor into a continuous or discrete set to generate the final executable time sequence, ensuring that conflict resolution and scheduling are decoupled. A dynamic incremental scheduling and caching strategy is proposed: it supports online updates to the resource pool; it allows for the addition and deletion of urgent tasks and continuous reconfiguration of priorities to ensure the system responds quickly to sudden demands; and it combines a time-segment caching mechanism to preheat hot grids and time periods, reducing retrieval latency and memory fragmentation risks.

[0066] Compared with existing technologies, this invention has significant advantages in terms of efficiency, flexibility, scalability, and modularity, and can provide more reliable and lower-latency multi-satellite collaborative observation planning services in large-scale dynamic scenarios, specifically in the following aspects: 1. Significantly reduced time complexity: Traditional solutions require repeated calculation of the visibility window for each satellite and each sub-task. This invention pre-indexes the visibility into the database, reducing the retrieval complexity from O(N•M) to near O(logK) or O(1) levels (N: number of satellites, M: number of tasks, K: index size). In scenarios with thousands of constellations and tens of thousands of tasks, the overall planning time can be reduced from several minutes or hours to hundreds of milliseconds to several seconds.

[0067] 2. Eliminate heterogeneous data compatibility overhead: Existing technologies require separate conversion of visibility formats for different sensors or manufacturers. This invention uses a unified grid-time series model for one-time mapping, avoiding multiple rounds of data adaptation and error accumulation. The data preprocessing and scheduling stages are decoupled, reducing system complexity and potential failure points caused by interface conversion.

[0068] 3. Enhanced dynamic adjustment and online response capabilities: In the past, global planning often required a full recalculation for new tasks or priority changes. This invention supports incremental retrieval and local merging based on resource pools, without restarting the underlying solution, ensuring the system can respond to emergencies within seconds.

[0069] 4. Decoupling of conflict resolution and solution process: Traditional conflict resolution is deeply embedded in scheduling algorithms, making it difficult to reuse or optimize independently; This invention modularizes mechanisms such as conflict pre-screening, attribute filtering, and time merging, which can be seamlessly integrated with various backend optimization algorithms, improving the maintainability and scalability of the system.

[0070] 5. Enhanced resource utilization and coverage efficiency: The optimized index based on a multi-level grid not only reduces storage redundancy but also allows for flexible selection of grid levels according to different resolution requirements, improving the precision of resource allocation. The conflict pre-screening and merging mechanism, while meeting coverage requirements, minimizes redundant observations, achieving the goal of "fewest satellites, shortest time" for full-area coverage.

[0071] This invention also provides a storage medium for storing a computer program, which, when executed, performs at least the methods described above.

[0072] 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.

[0073] This invention also provides a processor that executes a computer program, at least performing the methods described above.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] The methods disclosed in the several method embodiments provided by this invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0081] The features disclosed in the several product embodiments provided by this invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0082] 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.

[0083] 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 resolving conflicts in multi-satellite collaborative observation missions based on a virtual resource pool, characterized in that, Includes the following steps: S1. Construct a unified spatiotemporal coverage description model: Discretize the area to be observed on the Earth's surface into a multi-level global grid, assign a spatial code to each grid, and divide the time domain into discrete time slices; preprocess the orbit and payload field of view data of each satellite, and map their visible intervals into a binary index of grid code and time slice; S2. Establish a virtual resource pool: Create a main table with grid code as the primary key to store the time list and the list of available sensors, and create a secondary table with sensor identifier as the primary key to record sensor attributes; write the binary index from step S1 into the resource pool in batches, and dynamically maintain the consistency between the resource pool and the actual constellation status. S3. Conflict Pre-Search: Receives the user's observation requirement grid set, searches the resource pool master table in parallel, and generates a candidate set containing sensor identifiers and time slices; S4. Attribute and Time Filtering: Sensors that do not meet the resolution or load requirements are removed based on the attributes in the sub-table. The optimal time segment is selected according to the time density requirements, and time conflicts with the locked task are detected. S5. Time Series Merging and Scheduling Generation: The discrete time slices of the same sensor are merged into a continuous window, a scheduling sequence is generated and globally sorted, and the observation scheme is output with the goal of achieving full coverage as early as possible.

2. The method as described in claim 1, characterized in that, In step S1: The multi-level global subdivision grid adopts a hierarchical spatial coding structure; The preprocessing includes calculating the satellite's coverage capability over the grid and mapping the coverage relationship to a grid code and a set of time slice tuples; Redundancy analysis is used to select the grid level to balance accuracy and storage overhead.

3. The method as described in claim 1 or 2, characterized in that, In step S2: The main table storage structure satisfies the following: using grid encoding as the primary key to associate the corresponding time list and the list of available sensor identifiers; The secondary table storage structure satisfies the following: using the sensor identifier as the primary key to record resolution, payload type, and field of view attributes; The main table and the secondary table establish a bidirectional index constraint relationship through sensor identifiers; Run-length encoding is used to compress the time list data in the main table.

4. The method according to any one of claims 1-2, characterized in that, In step S2: The dynamic maintenance includes: synchronously modifying the data in the main table and the sub-table through incremental updates in response to orbital fine-tuning or satellite changes.

5. The method according to any one of claims 1-2, characterized in that, In step S3: The parallel retrieval specifically includes: traversing the user demand grid set and extracting all associated sensor identifiers and time slices from the main table; Record the earliest and latest observable time slices for each sensor identifier for subsequent optimization.

6. The method according to any one of claims 1-2, characterized in that, In step S4: The attribute filtering includes filtering the candidate set based on the resolution and load type attributes of the sub-table; The time density filtering includes: for scenes that need to be observed repeatedly, retaining the optimal coverage segment according to the time interval and frequency requirements.

7. The method according to any one of claims 1-2, characterized in that, In step S4: The time conflict detection includes: determining whether the candidate sensor time slice overlaps with the locked task, and eliminating conflicting items according to priority or the earliest completion time.

8. The method according to any one of claims 1-2, characterized in that, In step S5: The time-series merging includes: aggregating discrete time slices of the same sensor into a set of continuous observation windows; The global sorting aims to minimize the time required to complete global coverage.

9. The method according to any one of claims 1-2, characterized in that, Also includes: Steps S1-S5 are encapsulated as interface services, supporting on-demand invocation of functions such as request submission, index retrieval, attribute filtering, and time series merging.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the multi-satellite collaborative observation mission conflict resolution method based on a virtual resource pool as described in any one of claims 1 to 9.

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