Dual-objective collaborative multi-satellite resource allocation method based on spatial discretization

Abstract

The application discloses a kind of dual-target collaborative multi-satellite resource allocation methods based on spatial dispersion, effectively solve the problem of inconsistent specification in capability description, difficult calculation in multi-satellite resource allocation under large-scale remote sensing satellite scene.The method comprises the following steps: first, the earth surface is dispersed into grid, precalculates satellite coverage capability and is mapped to grid, realizes uniform description of coverage capability;Second, a satellite coverage capability master table is constructed with grid code as the primary key, for efficient retrieval;Finally, based on the master table, with optimal task completion time as the main target, the lowest energy consumption as the secondary target, the task completion time is compressed through time optimization stage, the total energy consumption is reduced under time constraint in energy consumption optimization stage, and the conflict of dual targets is balanced by means of dynamic relaxation mechanism, to generate multiple optimization schemes, realize the efficient allocation of large-scale remote sensing satellite resources.

Description

Technical Field

[0001] This invention relates to multi-satellite collaborative scheduling technology, and in particular to a method for dual-objective collaborative multi-satellite resource allocation based on spatial discreteness. Background Technology

[0002] Due to factors such as satellite orbit, the physical characteristics of onboard imaging payloads, and the physical characteristics of target points, a single satellite often cannot complete all imaging tasks within a specified timeframe. Therefore, multi-satellite network Earth observation has become an important means of improving telemetry capabilities both domestically and internationally. To enhance the imaging mission completion capability of a satellite constellation, rational planning is required. This involves the reasonable allocation of existing satellite resources to achieve high-frequency, multi-mission services at low cost; this process is known as multi-satellite resource allocation. Multi-satellite resource allocation optimizes the collaborative observation effects of multiple satellites by coordinating multiple observation missions and ground coverage opportunities, maximizing their rational utilization.

[0003] In describing spatiotemporal coverage capabilities, the Earth coverage performance of remote sensing satellites is described using parameters such as satellite swath width, coverage area polygon, and coverage time to indicate when a satellite can observe which location. This forms the basis for conducting multi-satellite, multi-mission collaborative research. Currently, based on different methodologies, there are two main categories of methods for calculating satellite Earth coverage areas: the first is analytical methods, which directly calculate the size and location of the Earth coverage area using the sensor's field of view characteristics and the dynamic geometric relationship between the satellite and the ground. These methods typically use the coordinates of points on the boundary line of the Earth coverage area to represent the calculation results. The second category is numerical methods, which use real ground targets or a group of uniformly distributed hypothetical target points generated in a limited area of ​​the Earth's surface in some way. Based on the field of view characteristics, upper and lower limits of the observation angle are established, and the satellite's instantaneous on-orbit position and the line of sight between the satellite and the target are determined to infer the size and performance of the satellite's Earth coverage area at a specific moment. Currently, internationally, remote sensing satellites generally use the World Reference System (WRS) or the Grid Reference System (GRS) to describe their Earth coverage capabilities. The World Reference System (WRS) identifies locations by Path and Row; the Grid Reference System (GRS) divides the globe into five regions: the equatorial region, the central region, the polar region, etc., and is divided by row (J) and column (K). In the equatorial and central regions, J is parallel to the latitude line and K is parallel to the nadir trajectory of the orbit.

[0004] In the field of multi-satellite multi-task collaborative scheduling, many researchers have established various task planning models under different practical constraints within the framework of multi-objective programming in operations research. First, they design a segmentation algorithm to decompose the target to be observed into individual meta-tasks. Then, they establish a multi-satellite multi-task planning problem model and use various deterministic algorithms, heuristic algorithms, or machine learning methods to solve the problem.

[0005] In describing spatiotemporal coverage capabilities, the Earth coverage performance of remote sensing satellites is described using parameters such as satellite swath width, coverage area polygon, and coverage time to indicate when a satellite can observe which location. This forms the basis for conducting multi-satellite, multi-mission collaborative research. Currently, based on different methodologies, there are two main categories of methods for calculating satellite Earth coverage areas: the first is analytical methods, which directly calculate the size and location of the Earth coverage area using the sensor's field of view characteristics and the dynamic geometric relationship between the satellite and the ground. These methods typically use the coordinates of points on the boundary line of the Earth coverage area to represent the calculation results. The second category is numerical methods, which use real ground targets or a group of uniformly distributed hypothetical target points generated in a limited area of ​​the Earth's surface in some way. Based on the field of view characteristics, upper and lower limits of the observation angle are established, and the satellite's instantaneous on-orbit position and the line of sight between the satellite and the target are determined to infer the size and performance of the satellite's Earth coverage area at a specific moment. Currently, internationally, remote sensing satellites generally use the World Reference System (WRS) or the Grid Reference System (GRS) to describe their Earth coverage capabilities. The World Reference System (WRS) identifies locations by Path and Row; the Grid Reference System (GRS) divides the globe into five regions: the equatorial region, the central region, the polar region, etc., and is divided by row (J) and column (K). In the equatorial and central regions, J is parallel to the latitude line and K is parallel to the nadir trajectory of the orbit.

[0006] In the field of multi-satellite multi-task collaborative scheduling, many researchers have established various task planning models under different practical constraints within the framework of multi-objective programming in operations research. First, they design a segmentation algorithm to decompose the target to be observed into individual meta-tasks. Then, they establish a multi-satellite multi-task planning problem model and use various deterministic algorithms, heuristic algorithms, or machine learning methods to solve the problem.

[0007] The current inconsistent models and specifications for describing the spatiotemporal coverage capabilities of satellite Earth observations have led to the fragmentation and insufficient utilization of existing satellite observation resources. Current satellite coverage capabilities are generally described using a "nadir trajectory + swath width + time point sequence" model, which is complex and inconsistent across satellites. This results in fragmented and uncoordinated planning of observation missions for each satellite, hindering the realization of satellite observation potential and causing a certain degree of resource waste. The current multi-satellite, multi-mission resource allocation problem is modeled as a time-dependent optimization problem based on polygon coverage computation, a typical NP-hard problem. As the number of satellite resources continues to increase, the problem scale expands dramatically, and existing models and solution methods are insufficient to meet the rapid scheduling needs of hundreds or even thousands of remote sensing satellite resources.

[0008] 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

[0009] The main objective of this invention is to overcome the deficiencies in the aforementioned background technology and provide a spatially discrete dual-target collaborative multi-satellite resource allocation method to better meet the needs of efficient and collaborative scheduling of large-scale remote sensing satellite constellation resources.

[0010] To achieve the above objectives, the present invention adopts the following technical solution:

[0011] A spatially discrete dual-objective cooperative multi-satellite resource allocation method includes the following steps:

[0012] S1. Satellite coverage capability calculation based on spatial discretization: The Earth's surface is discretized into a grid, and the satellite coverage capability is pre-calculated. The satellite coverage strips are mapped to the grid to achieve a unified description of the coverage capability.

[0013] S2. Construction of a large table for satellite coverage capability: Design and store the coverage capability information in a large table, with grid coding as the primary key, for efficient retrieval of satellite coverage information;

[0014] S3. Dual-target multi-satellite resource allocation: Based on the aforementioned capability table, with the optimal task completion time as the primary objective and the lowest energy consumption as the secondary objective, a time optimization phase and an energy consumption optimization phase are executed to allocate satellite resources and generate multiple optimization schemes. The time optimization phase prioritizes compressing the overall task completion time, while the energy consumption optimization phase reduces the total system energy consumption under time constraints and balances the dual-objective conflict through a dynamic relaxation mechanism.

[0015] Furthermore, in step S1, the calculation of satellite coverage capability based on spatial discreteness specifically includes:

[0016] Set an update cycle and use an orbit recursive model to calculate the satellite's nadir trajectory;

[0017] Based on satellite sensor parameters, calculate the instantaneous coverage stripe of the satellite at a set time resolution;

[0018] The latitude and longitude of the corner points of the instantaneous coverage strip polygon are input into the global subdivision grid system. According to the grid calculation rules of the system, they are mapped into a grid set, and the satellite coverage capability is output as a time-grid set representation.

[0019] Further, in step S2, the structure of the large table includes:

[0020] Using grid coding as the primary key, each grid is associated with at least one coverage capability vector;

[0021] The coverage capability vector includes satellite identifier, coverage time window set, sensor type, and sensor state vector, wherein the sensor state vector is used to mark the attitude adjustment information required for the satellite to cover the grid.

[0022] Furthermore, in step S2, the construction of the satellite coverage capability table also includes the following storage method:

[0023] Iterate through the coverage capability information of each satellite and merge the coverage time windows of consecutive identical grids;

[0024] The coverage information with satellites as the primary key is converted into the covered information with grids as the primary key, and the satellite identifier, time window, and sensor status parameter vector are updated and written.

[0025] Furthermore, in step S2, the construction of the satellite coverage capability table also includes the following update mechanism:

[0026] It employs a dual mechanism of periodic full updates and event-based incremental updates;

[0027] The periodic full update periodically clears outdated information, recalculates coverage capability based on the latest orbital parameters and writes it to a temporary table, and switches to the formal table through transactions.

[0028] The event incremental update locates the satellite when its parameters change, recalculates its coverage capability grid representation, deletes old records, and writes new results.

[0029] Furthermore, in step S3, the time optimization stage of the dual-target multi-satellite resource allocation specifically includes:

[0030] The target area of ​​the observation task is discretized into a grid set, and candidate coverage records are obtained by searching the capability table.

[0031] Generate an initialization allocation scheme and identify the critical grid with the latest end time;

[0032] Iteratively search the capability table to allocate satellites that can provide earlier coverage windows to the key grid;

[0033] Each iteration optimizes a predetermined proportion of the latest grid cells until the overall completion time can no longer be compressed.

[0034] Furthermore, in step S3, the energy consumption optimization stage of the dual-target multi-satellite resource allocation specifically includes:

[0035] Based on the time optimization results, grids whose observation end time is significantly earlier than the task completion time are selected;

[0036] Search the capability table and attempt to replace it with a low-energy satellite. The following conditions must be met simultaneously: the replacement does not delay the overall mission completion time, and the energy consumption reduction of a single replacement reaches a significant threshold.

[0037] The total energy consumption of the system can be reduced cumulatively through partial replacement.

[0038] Furthermore, in step S3, the dynamic relaxation mechanism includes:

[0039] When the time optimization phase fails to compress the completion time after a predetermined number of consecutive attempts, the energy consumption reduction threshold for the energy consumption optimization phase is automatically relaxed.

[0040] When the average remaining energy of the satellite is lower than the safety threshold, the time optimization phase is paused and the system is forced to enter the energy consumption protection mode.

[0041] Furthermore, in step S3, the dual-target multi-satellite resource allocation also includes:

[0042] Three scheduling schemes are generated: the time-optimal scheme aims to minimize the completion time, the energy-optimal scheme aims to minimize the total energy consumption, and the balanced scheme aims to maximize the weighted comprehensive score of time and energy consumption.

[0043] Update the status flags of the capability table and output the user-selected scheme.

[0044] A computer program product includes a computer program that, when executed by a processor, implements the spatially discrete dual-objective cooperative multi-satellite resource allocation method.

[0045] The present invention has the following beneficial effects:

[0046] This invention addresses the challenges of inconsistent capability descriptions and computational difficulties in multi-satellite resource allocation under large-scale remote sensing satellite scenarios. It proposes a spatially discrete multi-satellite resource allocation method. This method takes the target location of the observation area as input, the resource allocation scheme as output, and the optimal task completion time as the objective and the lowest energy consumption as a secondary objective. By discretizing the Earth's surface into a grid and pre-computing and storing satellite coverage capabilities, the satellite ground coverage capabilities are abstracted using consistent spatial discretization and pre-stored as a large capability table. Then, based on searching this large table, the complex satellite resource allocation problem is transformed into an efficient gridded query problem, significantly reducing computational complexity. A dual-objective multi-satellite resource allocation algorithm is designed to solve the resource allocation task, achieving efficient processing of large-scale remote sensing satellite resource allocation tasks. Its key innovative contributions include a unified modeling method for satellite coverage capabilities based on spatial discretization, a storage, query, and update mechanism for the large satellite coverage capability table, and a dual-objective, hierarchically decoupled multi-satellite resource allocation method based on the large satellite capability table.

[0047] Compared with existing remote sensing satellite resource allocation technologies, the technical solution of this invention is proposed for large-scale satellite resource allocation application scenarios. Its approach of unifying capability description specifications, pre-storage, and on-demand retrieval trades space for time, eliminating the cumbersome pre-calculation steps of regional target polygon decomposition and time window calculation required by traditional methods, thus improving the computational efficiency of resource allocation. Through dual-objective hierarchical decoupling and dynamic relaxation mechanisms, it can meet the dual-objective optimization requirements of task completion time and energy consumption for different types of observation tasks, quickly generating three schemes: time-optimal, energy-optimal, and on-demand balanced. Furthermore, this method has good scalability; adding additional parameters based on actual conditions only affects the number of large table columns, with minimal impact on query computational complexity. This invention achieves efficient and collaborative scheduling of large-scale remote sensing satellite cluster resources, simultaneously optimizing task timeliness and system energy consumption while ensuring full coverage.

[0048] Other beneficial effects of the embodiments of the present invention will be further described below. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the mapping calculation of the overlay strip to the discrete spatial grid according to an embodiment of the present invention.

[0050] Figure 2 This is a schematic diagram of the satellite coverage capability storage process according to an embodiment of the present invention.

[0051] Figure 3 This is a flowchart of the dual-target multi-satellite resource allocation calculation based on a satellite capability table, according to an embodiment of the present invention.

[0052] Figure 4 This is a flowchart illustrating the overall process of the spatially discrete dual-objective collaborative multi-satellite resource allocation method according to an embodiment of the present invention. Detailed Implementation

[0053] 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.

[0054] This invention addresses the challenges of inconsistent capability descriptions and computational difficulties in multi-satellite resource allocation under large-scale remote sensing satellite scenarios. It proposes a spatially discrete multi-satellite resource allocation method, abstracting satellite ground coverage capabilities using consistent spatial discreteness and pre-storing them in a large capability table. A dual-objective multi-satellite resource allocation algorithm is then designed based on a search of this table to solve the resource allocation task. This invention takes the target location of the observation area as input, the resource allocation scheme as output, the optimal task completion time as the objective, and the lowest energy consumption as a secondary objective, achieving efficient processing of large-scale remote sensing satellite resource allocation tasks.

[0055] See Figure 4This invention provides a method for dual-target cooperative multi-satellite resource allocation based on spatial discreteness, comprising the following steps:

[0056] Step S1: Calculation of satellite coverage capability based on spatial discretization: Discretize the Earth's surface into a grid, pre-calculate the satellite coverage capability, and map the satellite coverage strips onto the grid to achieve a unified description of coverage capability.

[0057] In some embodiments, the calculation of satellite coverage capability based on spatial discreteness specifically includes: setting an update period and calculating the satellite's nadir trajectory using an orbital recursive model; wherein the update period T and time resolution can be configured according to the satellite constellation size and mission accuracy requirements, such as T being set to 24 hours and the time resolution being set to the second to minute level; calculating the instantaneous coverage strip of the satellite at the set time resolution based on satellite sensor parameters; inputting the latitude and longitude of the polygon corner points of the instantaneous coverage strip into a global grid system, mapping it to a grid set according to the grid calculation rules of the system, and outputting the satellite coverage capability represented by the time-grid set.

[0058] Step S2: Constructing a large table of satellite coverage capabilities: Design and store the coverage capability information in a large table, which uses grid coding as the primary key for efficient retrieval of satellite coverage information.

[0059] In some embodiments, the structure of the satellite coverage capability table specifically includes: using grid encoding as the primary key, with each grid associated with at least one coverage capability vector; the coverage capability vector includes a satellite identifier, a set of coverage time windows, a sensor type, and a sensor state vector, wherein the sensor state vector is used to mark the attitude adjustment information required for the satellite to cover the grid. The sensor state vector may include, for example, side angle, pitch angle, and corresponding maneuvering energy consumption.

[0060] In some embodiments, the construction of the satellite coverage capability table further includes the following storage method: traversing the coverage capability information of each satellite, merging coverage time windows of consecutive identical grids; converting the coverage information with satellite as the primary key into covered information with grid as the primary key, and updating and writing the satellite identifier, time window, and sensor status parameter vector.

[0061] In some embodiments, the construction of the satellite coverage capability table further includes the following update mechanism: a dual mechanism of periodic full update and event incremental update; the periodic full update periodically clears outdated information, recalculates the coverage capability based on the latest orbital parameters and writes it into a temporary table, and switches to the formal table through transactions; the event incremental update locates the satellite when the satellite parameters change, recalculates its coverage capability grid representation, deletes old records and writes new results.

[0062] Step S3, Dual-Target Multi-Satellite Resource Allocation: Based on the aforementioned capability table, with the optimal task completion time as the primary objective and the lowest energy consumption as the secondary objective, a time optimization phase and an energy consumption optimization phase are executed to allocate satellite resources and generate multiple optimization schemes. The time optimization phase prioritizes compressing the overall task completion time, while the energy consumption optimization phase reduces the total system energy consumption under time constraints and balances the dual-objective conflict through a dynamic relaxation mechanism.

[0063] In some embodiments, the time optimization phase of the dual-target multi-satellite resource allocation specifically includes: discretizing the target area of ​​the observation mission into a set of grids, retrieving candidate coverage records from the capability table; generating an initial allocation scheme and identifying the key grid with the latest completion time; iteratively retrieving the capability table and allocating satellites that can provide an earlier coverage window to the key grid; optimizing a predetermined proportion of the latest grid in each iteration until the overall completion time can no longer be compressed.

[0064] In some embodiments, the energy consumption optimization stage of the dual-target multi-satellite resource allocation specifically includes: based on the time optimization results, screening grids whose observation end time is significantly earlier than the mission completion time; searching the capability table and attempting to replace them with low-energy satellites, which must simultaneously meet the following conditions: the replacement does not delay the overall mission completion time, and the energy consumption reduction of a single replacement reaches a significant threshold; and cumulatively reducing the total system energy consumption through local replacement.

[0065] In some embodiments, the dynamic relaxation mechanism specifically includes: automatically relaxing the energy consumption reduction threshold of the energy consumption optimization phase when the time optimization phase cannot be compressed for a predetermined number of consecutive times; and pausing the time optimization phase and forcibly entering the energy consumption protection mode when the average remaining energy of the satellite is lower than the safety threshold.

[0066] The predetermined ratio and significant energy consumption threshold can be set according to computing resources and optimization benefits, while the safety threshold can be configured in combination with factors such as the satellite's remaining energy.

[0067] In some embodiments, the dual-objective multi-satellite resource allocation further includes: generating three scheduling schemes: the time-optimal scheme aims to minimize the completion time, the energy-optimal scheme aims to minimize the total energy consumption, and the balanced scheme aims to achieve the highest weighted comprehensive score of time and energy consumption; updating the status flag of the capacity table, and outputting the user-selected scheme.

[0068] The multi-satellite resource allocation method of this invention discretizes the Earth's surface into a uniform spatial grid and pre-calculates and stores satellite coverage capabilities. It innovatively constructs a large satellite coverage capability table with grid coding as the primary key, effectively solving the resource coordination difficulties caused by inconsistent satellite coverage capability descriptions in existing technologies. Based on this capability table, a hierarchical decoupled dual-objective optimization mechanism is designed. Prioritizing time optimization, it compresses the overall task completion time. Under time constraints, it reduces total system energy consumption through an energy consumption optimization phase and introduces a dynamic relaxation mechanism to adaptively balance dual-objective conflicts. This eliminates the complex target polygon decomposition and time window calculation steps of traditional methods, significantly reducing the computational complexity of large-scale satellite resource allocation. Simultaneously, this method flexibly adapts to different task requirements by generating three schemes: time-optimal, energy-optimal, and balanced. The capability table structure supports parameter expansion without significantly increasing the query burden, ultimately achieving efficient collaborative scheduling of resources for satellite clusters of over 100 levels, simultaneously improving task timeliness and energy utilization while ensuring full coverage.

[0069] The following describes specific embodiments of the present invention.

[0070] A multi-satellite resource allocation method based on spatial discretization transforms the complex satellite resource allocation problem into an efficient gridded query problem by discretizing the Earth's surface into a grid and pre-computing and storing satellite coverage capabilities, significantly reducing computational complexity. In its implementation, to address the current inconsistency in spatiotemporal coverage capability descriptions, remote sensing satellite capabilities are described based on spatial discretization; a large capability table is designed to achieve efficient task processing; and a multi-objective optimization method is designed to allocate satellite resources based on the large capability table to meet the needs of different types of observation tasks. Its implementation method mainly consists of three core parts:

[0071] (I) Calculation of satellite coverage capability based on spatial discreteness

[0072] This part achieves a precise mapping from satellite parameters to coverage capabilities represented by a grid, and includes a three-step calculation process.

[0073] (1) Satellite orbit recursion: Set the update period T and calculate the orbits of all satellites. Input the orbital parameters of the satellites, use the existing orbit recursion model to recursively calculate the position of the satellites in the next update period, calculate the nadir trajectory of the satellites during this period, and output it in the form of position coordinates + timestamp sequence;

[0074] (2) Sensor coverage strip calculation: set time resolution Based on satellite sensor parameters (field of view, swath width), the instantaneous coverage strip of the satellite at each moment is calculated:

[0075] (3) Mapping calculation of coverage strips to spatial grids: The GeoSOT global subdivision grid system is adopted, which has complete multi-scale grid calculation rules. The grid scale is set, and the latitude and longitude of the polygon corner points of the instantaneous coverage strip at each moment are used as input. According to the mutual calculation rules of latitude and longitude position and grid code in the GeoSOT grid system, the grid set corresponding to the instantaneous strip is calculated. The same calculation is performed on the coverage strip at each moment until all coverage strips within the update cycle are calculated as the grid set of the corresponding position. Thus, the satellite coverage strip in the form of "time-grid set" represented by spatial grids is obtained, realizing the unification of description specifications. This process is as follows: Figure 1 As shown. Those skilled in the art will understand that, in addition to GeoSOT, global mesh systems such as S2 and H3 can also be used to achieve coverage strip mapping.

[0076] (II) Satellite Coverage Capability Table Design

[0077] The purpose of the coverage capability table is to store satellite coverage capabilities for resource allocation calculations, achieving efficient resource allocation by trading space for time. Specific implementations include the capability table structure design, storage and query methods, and update mechanism.

[0078] (1) Capability table structure design

[0079] The coverage capability table uses grid coding as the primary key and records the coverage status of each grid in Earth's space. Each grid is associated with several coverage capability vectors (CVs), with the following structure:

[0080] CV = { Satellite ID, Coverage Time Window {[Start Time 1, End Time 1], [Start Time 2, End Time 2], ...}, Sensor Type, Sensor Status}

[0081] The sensor state vector (CV) indicates the attitude adjustment the sensor needs to make to cover the grid. Therefore, for satellites with sensor tilting capabilities, multiple CVs with different sensor states will be recorded. By building a large table with this structure, it is possible to directly search for locations around the world that can be covered by existing satellites from the grid encoding set.

[0082] (2) Capability table storage method

[0083] Based on the satellite coverage capability calculation results and the capability table structure, a method for storing and querying the capability table is designed, and the storage procedure is as follows: Figure 2As shown, the process essentially transforms satellite-based capability information into grid-based overlay information. The steps are as follows: For each satellite, iterates through the grids sequentially, merging grids that are identical for consecutive time periods. Then, it updates the information of the grids involved in the overlay, writing the satellite ID, time window, and the satellite's sensor performance and status parameter vector. The storage process ends once all information for the satellite's entire update cycle has been traversed.

[0084] (3) Capability table update mechanism

[0085] To ensure the long-term stability of the capability table, a dual update mechanism of periodic full updates and event-based incremental updates is adopted. Periodic full updates guarantee that the capability table always stores satellite capabilities for the next time interval T. A full update process is triggered every fixed time interval (e.g., 24 hours). This process is as follows: First, outdated information in the table is searched and cleared by time, and the latest orbital parameter TLE data is obtained. Then, following the spatially discrete satellite coverage capability calculation process, orbit recursion → coverage strip calculation → spatial grid mapping are performed. The calculation results are written to a temporary table, and a transaction is used to switch to the formal table to avoid query interruptions during the update process.

[0086] Event-based incremental updates enable the large table to add, delete, and modify satellite data. When sensor status or functionality changes or malfunctions for various reasons, the large table is updated via event-based incremental updates. The process is as follows: locate the satellite that has changed, recalculate the new coverage capability grid representation for that satellite, search and delete the coverage capability of the old satellite by satellite ID, and then write the new results into the large table. When writing to the large table, pay attention to updating the sensor status information in the vector.

[0087] (III) A dual-target, multi-satellite resource allocation method based on a large satellite capability table

[0088] A dual-objective, multi-satellite resource allocation method addresses the dual-objective synergistic optimization problem of time efficiency and energy consumption in large-scale satellite constellation observation missions. Its core idea is to reduce the overall mission completion time and decrease the total energy consumption of the satellite system through a hierarchical, progressive optimization strategy, ensuring full coverage of all target grids. Its specific implementation is as follows:

[0089] After a user submits an observation task request, the target area is first discretized into a grid set. Then, the satellite coverage capability table is accessed to retrieve all candidate coverage capability vectors. Next, energy consumption is calculated based on sensor type and sensor status. Energy consumption includes two aspects: normal operating energy consumption and maneuvering energy consumption. Normal operating energy consumption is only related to power-on time and is described as a linear relationship in this method. The linear energy consumption model can be replaced with a nonlinear model or a lookup table method based on the actual energy consumption characteristics of the satellite without affecting the overall optimization framework. Maneuvering energy consumption is determined by sensor status; different yaw angles correspond to different maneuvering energy consumptions. The calculated energy consumption replaces the sensor type and sensor status in the original CV to form the initial dataset. Based on this data, a two-stage optimization is performed:

[0090] Phase 1: Time Optimization. Identify the grid with the latest finish time in the current plan (referred to as the "critical grid") and search for earlier coverage opportunities for it using a capability table. If a satellite is found to provide an earlier coverage window (e.g., the original finish time is 9:30, but the new satellite can advance it to 9:00), immediately assign that satellite to that grid. This process is iterative, optimizing the latest 10% of grids each time, until the overall completion time cannot be further advanced. This phase ensures that, under satellite resource constraints, the mission completion time approaches the theoretical minimum.

[0091] Phase Two: Energy Consumption Optimization Dominates. Based on the time optimization results, the system selects grids with time margins (i.e., grids whose observation end time is significantly earlier than the mission completion time) and attempts to replace them with lower-energy-consuming satellites. Replacement must meet two constraints: first, the coverage time window of the new satellite must not delay the overall mission completion time; second, maneuver energy consumption must be significantly reduced (the threshold is set at a single replacement saving ≥5% energy consumption). For example, if a grid was originally covered by satellite A (energy consumption 12 units), and satellite B can cover it in the same time period with only 9 units of energy, then replacement is performed. This phase cumulatively reduces the system's total energy consumption through local adjustments while maintaining the time optimization results of the first phase.

[0092] To balance the conflict between the two objectives, a dynamic relaxation mechanism is introduced: when the completion time cannot be reduced in three consecutive iterations during the time optimization phase, the energy-saving threshold for the energy consumption optimization phase is automatically relaxed (from 5% to 3%), allowing more grids to participate in the replacement; conversely, if the average remaining energy of the satellite is lower than the safety threshold (25%), time optimization is paused, and the system is forced into energy protection mode. Finally, a set of multiple solutions is generated for user decision-making: 1) Time-optimal solution: minimizing the completion time as the sole objective; 2) Energy-optimal solution: minimizing total energy consumption as the sole objective; 3) Balanced solution: selecting the solution with the highest weighted combined score of time and energy consumption (weights are preset according to the task type). All solutions guarantee 100% grid coverage, and the status flags of the capability table are updated via atomic transactions.

[0093] The main process of this multi-satellite resource allocation method is summarized as follows:

[0094] Step 1: Analyze the observation task and convert the target area into a set of grid IDs;

[0095] Step 2: Search the satellite coverage capability table to obtain candidate coverage records that meet the deadline;

[0096] Step 3: Execution time optimization phase, iteratively compressing the completion time of critical meshes;

[0097] Step 4: Perform the energy consumption optimization phase, replacing high-energy-consuming satellites under time constraints;

[0098] Step 5: Generate three scheduling schemes: time-optimal, energy-optimal, and balanced.

[0099] Step 6: Update the capability table status and output the user-selected solution.

[0100] Figure 3 The specific process of calculating resource allocation for two targets and multiple satellites based on a large satellite capability table is demonstrated.

[0101] In summary, this invention proposes a dual-objective collaborative multi-satellite resource allocation method based on spatial discreteness. The key innovative contributions of this invention include: 1. A unified modeling method for satellite coverage capabilities based on spatial discreteness; 2. A mechanism for storing, querying, and updating a large table of satellite coverage capabilities; 3. A multi-satellite resource allocation method based on a large table of satellite capabilities with hierarchical decoupling of dual objectives.

[0102] Compared to other typical remote sensing satellite resource allocation technologies, the technical solution of this invention is proposed for large-scale satellite resource allocation application scenarios. Its approach of unifying capability description specifications, pre-storage, and on-demand retrieval trades space for time, eliminating the cumbersome pre-calculation steps of regional target polygon decomposition and time window calculation required by traditional methods, thus improving resource allocation computational efficiency. Through dual-objective hierarchical decoupling and dynamic relaxation mechanisms, it can meet the dual-objective optimization requirements of task completion time and energy consumption for different types of observation tasks, quickly generating three schemes: time-optimal, energy-optimal, and on-demand balanced. Furthermore, this method has good scalability; adding additional parameters based on actual conditions only affects the number of large table columns, with minimal impact on query computational complexity.

[0103] This invention also provides a storage medium for storing a computer program, which, when executed, performs at least the methods described above.

[0104] 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.

[0105] This invention also provides a processor that executes a computer program, at least performing the methods described above.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The methods disclosed in the several method embodiments provided by this invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0113] The features disclosed in the several product embodiments provided by this invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0114] 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.

[0115] 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 dual-objective cooperative multi-satellite resource allocation based on spatial discreteness, characterized in that, Includes the following steps: S1. Satellite coverage capability calculation based on spatial discretization: The Earth's surface is discretized into a grid, and the satellite coverage capability is pre-calculated. The satellite coverage strips are mapped to the grid to achieve a unified description of the coverage capability. S2. Construction of Satellite Coverage Capability Table: Design and store the coverage capability information in a large table. The large table uses grid coding as the primary key. Each grid is associated with at least one coverage capability vector. The coverage capability vector includes satellite identifier, coverage time window set, sensor type and sensor state vector. The sensor state vector is used to mark the attitude adjustment information required for the satellite to cover the grid. The large table is used for efficient retrieval of satellite coverage information; S3. Dual-target multi-satellite resource allocation: Based on the aforementioned capability table, with the optimal task completion time as the primary objective and the lowest energy consumption as the secondary objective, a hierarchical progressive optimization strategy is adopted. Under the premise of ensuring full coverage of all target grids, time optimization and energy consumption optimization stages are executed to allocate satellite resources and generate multiple optimization schemes. The time optimization phase includes: discretizing the target area of ​​the observation task into a set of grids, retrieving candidate coverage records from the capability table; generating an initial allocation scheme and identifying the key grids with the latest completion time; iteratively retrieving the capability table and allocating satellites that can provide earlier coverage windows to the key grids; optimizing a predetermined proportion of the latest grids in each iteration until the overall completion time cannot be compressed. The energy consumption optimization phase includes: based on the time optimization results, screening grids whose observation completion time is significantly earlier than the task completion time; retrieving the capability table and attempting to replace them with low-energy-consumption satellites, where replacement must simultaneously meet the following conditions: the replacement does not delay the overall task completion time, and the energy consumption reduction of a single replacement reaches a significant threshold; cumulatively reducing the total system energy consumption through local replacements; and balancing the dual-objective conflict through a dynamic relaxation mechanism, which includes: automatically relaxing the energy consumption reduction threshold of the energy consumption optimization phase when the completion time cannot be compressed after a predetermined number of consecutive iterations in the time optimization phase; and pausing the time optimization phase and forcibly entering the energy consumption protection mode when the average remaining energy of the satellites is lower than a safe threshold.

2. The spatially discrete dual-objective cooperative multi-satellite resource allocation method as described in claim 1, characterized in that, In step S1, the calculation of satellite coverage capability based on spatial discreteness specifically includes: Set an update cycle and use an orbit recursive model to calculate the satellite's nadir trajectory; Based on satellite sensor parameters, calculate the instantaneous coverage stripe of the satellite at a set time resolution; The latitude and longitude of the corner points of the instantaneous coverage strip polygon are input into the global subdivision grid system. According to the grid calculation rules of the system, they are mapped into a grid set, and the satellite coverage capability is output as a time-grid set representation.

3. The method for dual-objective cooperative multi-satellite resource allocation based on spatial discreteness as described in claim 1, characterized in that, In step S2, the construction of the satellite coverage capability table also includes the following storage method: Iterate through the coverage capability information of each satellite and merge the coverage time windows of consecutive identical grids; The coverage information with satellites as the primary key is converted into the covered information with grids as the primary key, and the satellite identifier, time window, and sensor status parameter vector are updated and written.

4. The spatially discrete dual-objective cooperative multi-satellite resource allocation method as described in claim 1 or 2, characterized in that, In step S2, the construction of the satellite coverage capability table also includes the following update mechanism: It employs a dual mechanism of periodic full updates and event-based incremental updates; The periodic full update periodically clears outdated information, recalculates coverage capability based on the latest orbital parameters and writes it to a temporary table, and switches to the formal table through transactions. The event incremental update locates the satellite when its parameters change, recalculates its coverage capability grid representation, deletes old records, and writes new results.

5. The spatially discrete dual-objective cooperative multi-satellite resource allocation method as described in any one of claims 1 to 2, characterized in that, In step S3, the dual-target multi-satellite resource allocation further includes: Three scheduling schemes are generated: the time-optimal scheme aims to minimize the completion time, the energy-optimal scheme aims to minimize the total energy consumption, and the balanced scheme aims to maximize the weighted comprehensive score of time and energy consumption. Update the status flags of the capability table and output the user-selected scheme.

6. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the spatially discrete dual-target cooperative multi-star resource allocation method as described in any one of claims 1 to 5.

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