Double-target collaborative multi-satellite resource allocation method based on spatial discretization

Abstract

The invention discloses a double-target collaborative multi-satellite resource allocation method based on spatial discretization, and effectively solves the problems of inconsistent capability description specifications and difficult calculation in multi-satellite resource allocation in a large-scale remote sensing satellite scene. The method comprises the following steps: firstly, discretizing the earth surface into a grid, pre-calculating the satellite coverage capability and mapping the satellite coverage capability to the grid to realize unified description of the coverage capability; secondly, a satellite coverage capability large table with grid codes as main keys is constructed and used for efficient retrieval; and finally, based on the large table, taking the optimal task completion time as a main target and the lowest energy consumption as a secondary target, compressing the task completion time through a time optimization stage, reducing the total energy consumption under the time constraint through an energy consumption optimization stage, balancing the double-target conflict by means of a dynamic relaxation mechanism, and generating a plurality of optimization schemes. And efficient allocation of large-scale remote sensing satellite resources is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to multi-satellite cooperative scheduling technology, in particular to a double-target cooperative multi-satellite resource allocation method based on spatial dispersion. BACKGROUND

[0002] Due to the influence of factors such as satellite orbit, physical characteristics of on-board imaging load, and physical characteristics of target points, a single satellite often cannot complete all imaging tasks within the specified time limit, so multi-satellite networking for ground observation has become an important means for improving remote sensing capabilities at home and abroad. In order to improve the imaging task completion capability of the satellite group, it is necessary to make reasonable planning, and to allocate existing satellite resources reasonably to achieve multi-task high-frequency service at low cost. This process is called multi-satellite resource allocation. Multi-satellite resource allocation coordinates multiple observation tasks and ground coverage opportunities of multiple satellites to make the cooperative observation effect of multiple satellites as optimal as possible, and to achieve the most reasonable use.

[0003] In terms of space-time coverage capability description, remote sensing satellite ground coverage performance description is to describe when the satellite can observe where by using parameters such as satellite swath, coverage area polygon, and coverage time. It is the basis for multi-satellite multi-task cooperative research. At present, according to different method paths, there are two main categories of satellite ground coverage area calculation methods: the first category is the analytical method, that is, the size and position of the ground coverage area are directly calculated by the field of view characteristics of the sensor and the dynamic geometric relationship between the satellite and the ground. This method usually uses the coordinates of the points on the boundary line of the ground coverage area to represent the calculation results; the other category is the numerical method, that is, according to the real ground target, or a certain way to generate a number of uniformly distributed imaginary target point groups on the limited ground surface, according to the field of view characteristics to set the upper and lower limits of the observation angle, and by judging the visibility between the satellite instantaneous on-orbit position and the target, the size and performance of the satellite ground coverage area at a certain time are inversely deduced. At present, internationally, remote sensing satellites generally use the World Reference System (WRS) or the Grid Reference System (GRS) to describe the ground coverage capability. The World Reference System (WRS) is identified by orbit number (Path) and row number (Row); the Grid Reference System (GRS) divides the global into five zones, including the equatorial zone, the middle zone, and the polar zone. In the equatorial zone and the middle zone, J is parallel to the latitude line, and K is parallel to the subsatellite point track of the orbit.

[0004] In terms of multi-satellite multi-task cooperative scheduling, many researchers have considered different actual constraint conditions under the framework of operational research multi-objective planning to establish various task planning models. First, a segmentation algorithm is designed to decompose the observed targets into individual tasks. Then, a multi-satellite multi-task planning problem model is established, and various deterministic algorithms, heuristic algorithms, or machine learning methods are used to solve the problem.

[0005] In terms of spatio-temporal coverage capability description, the coverage performance of remote sensing satellites is described by parameters such as satellite swath, coverage area polygon, and coverage time, which describes when and where the satellite can observe. It is the basis for collaborative research of multi-satellite and multi-mission. At present, according to different method paths, there are two main categories of satellite coverage area calculation methods: the first category is the analytical method, that is, the size and position of the coverage area are directly calculated by the field of view characteristics of the sensor and the dynamic geometric relationship between the satellite and the ground. This method usually uses the coordinates of the points on the boundary line of the coverage area to represent the calculation results; the other category is the numerical method, that is, according to the real ground target, or a certain way to generate a group of evenly distributed imaginary target points in a limited ground area, the upper and lower limits of the observation angle are set according to the field of view characteristics, and the size and performance of the satellite coverage area at a certain time are inversely deduced by judging the visibility between the satellite instantaneous on-orbit position and the target. At present, remote sensing satellites in the world generally use the World Reference System (WRS) or the Grid Reference System (GRS) to describe the ground coverage capability. The World Reference System (WRS) is identified by path and row; the Grid Reference System (GRS) divides the world into five zones, including the equatorial zone, the middle zone, and the polar zone. In the equatorial zone and the middle zone, J is parallel to the latitude line, and K is parallel to the subsatellite point track of the orbit.

[0006] In terms of multi-satellite and multi-mission collaborative scheduling, many researchers have established various task planning models under the framework of operational research multi-objective planning, first designed a segmentation algorithm to decompose the observed targets into individual tasks, then established a multi-satellite and multi-mission planning problem model, and then used various deterministic algorithms, heuristic algorithms or machine learning methods to solve the problem.

[0007] The current satellite spatio-temporal coverage capability description model and description specification are inconsistent, resulting in scattered satellite observation resources and insufficient effective utilization. The current satellite coverage capability generally uses the description mode of "subsatellite point track + swath + time point string", the model description is complex, and the description specifications of each satellite are inconsistent, which will lead to separate observation task planning of each satellite, difficult to collaborate, unable to develop the observation potential of satellites, and cause a certain degree of resource waste. The current multi-satellite and multi-mission resource allocation problem is modeled as a time-dependent optimization problem based on polygon coverage calculation, which is a typical NP-HARD problem. With the increasing number of satellite resources, the problem size expands rapidly, and the existing model and its solving means are difficult 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 above background section is only for understanding the background of the present application, and therefore can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0009] The main purpose of the present application is to overcome the defects existing in the background art, provide a dual-target collaborative multi-satellite resource allocation method based on spatial dispersion, and better meet the efficient and collaborative scheduling requirements of large-scale remote sensing satellite cluster resources.

[0010] To achieve the above purpose, the present application adopts the following technical solutions: A dual-target collaborative multi-satellite resource allocation method based on spatial dispersion, comprising the following steps: S1, satellite coverage capacity calculation based on spatial dispersion: discretize the earth's surface into a grid, and pre-calculate the coverage capacity of the satellite, map the satellite coverage strip to the grid, and realize the unified description of the coverage capacity; S2, satellite coverage capacity table construction: design and store the coverage capacity information in a large table, the large table takes grid encoding as the primary key, and is used for efficient retrieval of satellite coverage information; S3, dual-target multi-satellite resource allocation: based on the capacity table, taking the optimal task completion time as the main target and the lowest energy consumption as the secondary target, executing the time optimization phase and the energy optimization phase to allocate satellite resources, and generating multiple optimization schemes, wherein the time optimization phase preferentially compresses the overall task completion time, the energy optimization phase reduces the total energy consumption of the system under the time constraint, and balances the conflict between the two targets through a dynamic relaxation mechanism.

[0011] Further, in step S1, the satellite coverage capacity calculation based on spatial dispersion specifically comprises: Set the update period, and calculate the subsatellite point trajectory of the satellite using an orbit recursion model; Based on the satellite sensor parameters, calculate the instantaneous coverage strip of the satellite under the set time resolution; Input the longitude and latitude of the polygon corner points of the instantaneous coverage strip into the global subdivision grid system, map them into a grid set according to the grid calculation rules of the system, and output the satellite coverage capacity characterized by time-grid set.

[0012] Further, in step S2, the structure of the large table comprises: Taking the grid encoding as the primary key, each grid is associated with at least one coverage capacity vector; The coverage capacity vector includes satellite identification, coverage time window set, sensor type and sensor state vector, wherein the sensor state vector is used to mark the attitude adjustment information required by the satellite to cover the grid.

[0013] Further, in step S2, the satellite coverage capacity table construction further comprises the following storage method: Iterate through the coverage capacity information of each satellite, and merge the coverage time windows of consecutive identical grids; Convert the satellite-primary key based coverage information into grid-primary key based covered information, update and write satellite identification, time window and sensor state parameter vector.

[0014] Further, in step S2, the satellite coverage capacity large table construction further includes the following update mechanism: Adopt a dual mechanism of periodic full update and event incremental update; The periodic full update periodically empties outdated information, recalculates coverage capacity based on the latest orbit parameters and writes to a temporary table, and switches the official table through transactions; The event incremental update locates the satellite when the satellite parameters change, recalculates its coverage capacity grid representation, deletes the old record and writes the new result.

[0015] Further, in step S3, the time optimization phase of the dual-target multi-satellite resource allocation specifically includes: Discretize the target area of the observation task into a grid set, retrieve the capacity large table to obtain candidate coverage records; Generate an initial allocation scheme and identify the key grid with the latest end time; Iteratively retrieve the capacity large table to assign satellites that can provide earlier coverage windows to the key grid; Optimize a predetermined proportion of the latest grid in each iteration until the overall completion time cannot be compressed.

[0016] Further, in step S3, the energy consumption optimization phase of the dual-target multi-satellite resource allocation specifically includes: Based on the time optimization result, filter the grids whose observation end time is significantly earlier than the task completion time; Retrieve the capacity large table and attempt to replace it with a low-energy satellite, which must meet the following conditions: the overall task completion time is not delayed after replacement, and the energy consumption reduction after single replacement reaches a significant threshold; Accumulatively reduce the total system energy consumption through local replacement.

[0017] Further, in step S3, the dynamic relaxation mechanism includes: When the time optimization phase cannot compress the completion time for a predetermined number of consecutive times, automatically relax the energy consumption reduction threshold of the energy consumption optimization phase; When the average remaining energy of the satellite is below a safety threshold, pause the time optimization phase and force it into energy consumption protection mode.

[0018] Further, in step S3, the dual-target multi-satellite resource allocation further includes: Three sets of scheduling schemes are generated: a time optimal scheme aiming to minimize the completion time, an energy optimal scheme aiming to minimize the total energy consumption, and a balanced scheme aiming to maximize the weighted comprehensive score of time and energy consumption. Update the state marker of the capability table, and output the user-selected scheme.

[0019] A computer program product comprises a computer program, which, when executed by a processor, implements the spatial-discrete-based dual-target collaborative multi-satellite resource allocation method.

[0020] The present application has the following beneficial effects: The present application proposes a spatial-discrete-based multi-satellite resource allocation method to solve the problem of inconsistent capability description specifications and difficult calculation in the large-scale remote sensing satellite scenario. The method takes the target position of the observation area as the input, the resource allocation scheme as the output, and the optimal task completion time as the target and the lowest energy consumption as the secondary target. The earth's surface is discretized into a grid, and the satellite coverage capability is pre-calculated and stored. The satellite ground coverage capability is abstracted by using a spatial discrete specification, and a capability table is stored in advance. Based on the search of the table, the complex satellite resource allocation problem is converted into an efficient grid query problem to significantly reduce the computational complexity. Then, a dual-target multi-satellite resource allocation algorithm is designed to solve the resource allocation task, realizing efficient processing of large-scale remote sensing satellite resource allocation tasks. The important contributions of the present application include a unified modeling method of satellite coverage capability based on spatial discretization, a satellite coverage capability table storage query and update mechanism, and a dual-target decoupled multi-satellite resource allocation method based on the satellite capability table.

[0021] Compared with existing remote sensing satellite resource allocation technologies, the technical scheme of the present application is proposed for large-scale satellite resource allocation application scenarios. The unified capability description specification, pre-storage, and on-demand calling idea saves time by using space, eliminating the complex pre-computation steps of traditional methods for regional target polygon decomposition and time window calculation, improving the resource allocation calculation efficiency. Through the dual-target decoupled and dynamic relaxation mechanism, the task completion time and energy consumption dual-target optimization requirements of different types of observation tasks can be met, and three schemes of time optimal, energy optimal, and balanced on demand can be quickly generated. Moreover, the method has good scalability, and adding additional parameters according to actual conditions only affects the number of table columns, and has little effect on the query and calculation complexity. The present application realizes efficient and collaborative scheduling of large-scale remote sensing satellite clusters, and synchronously optimizes the task timeliness and system energy consumption under the premise of ensuring full coverage.

[0022] Other beneficial effects of the embodiments of the present application will be further described below. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1is a mapping calculation schematic diagram of a coverage strip to a discrete space grid of an embodiment of the present application.

[0024] Figure 2 is a satellite coverage capacity storage process schematic diagram of an embodiment of the present application.

[0025] Figure 3 is a double-target multi-satellite resource allocation calculation flow chart based on a satellite capacity large table of an embodiment of the present application.

[0026] Figure 4 is a general flow chart of a double-target collaborative multi-satellite resource allocation method based on space discretization of an embodiment of the present application. DETAILED DESCRIPTION

[0027] The following will make a detailed description of the embodiments of the present application. It should be emphasized that the following description is merely exemplary, but is not intended to limit the scope of the present application and its applications.

[0028] The present application is directed to the difficulty of inconsistent capacity description specifications and difficult calculation faced by multi-satellite resource allocation in a large-scale remote sensing satellite scenario, proposes a multi-satellite resource allocation method based on space discretization, abstracts the satellite ground coverage capacity with a consistent specification of space discretization, stores it in advance as a capacity large table, and then searches the large table to design a double-target multi-satellite resource allocation algorithm to complete the solution of the resource allocation task. The present application takes the observation area target position as the input, takes the resource allocation scheme as the output, takes the optimal task completion time as the target, and takes the lowest energy consumption as the secondary target, and realizes the efficient processing of large-scale remote sensing satellite resource allocation tasks.

[0029] Referring to Figure 4 , the embodiment of the present application provides a double-target collaborative multi-satellite resource allocation method based on space discretization, comprising the following steps: Step S1, satellite coverage capacity calculation based on space discretization: discretize the earth's surface into a grid, and pre-calculate the coverage capacity of the satellite. Map the satellite coverage strip to the grid to realize the unified description of the coverage capacity.

[0030] In some embodiments, the satellite coverage capacity calculation based on space discretization specifically comprises: setting an update period, calculating the subsatellite point trajectory of the satellite using an orbit recursion model; wherein the update period T and the time resolution can be configured according to the satellite constellation scale and the task accuracy requirement, such as T being set to 24 hours and the time resolution being set to seconds to minutes; based on the satellite sensor parameters, calculating the instantaneous coverage strip of the satellite under the set time resolution; inputting the instantaneous coverage strip polygon corner longitude and latitude into a global subdivision grid system, mapping it into a grid set according to the grid calculation rules of the system, and outputting the satellite coverage capacity characterized by time-grid set.

[0031] Step S2, constructing a satellite coverage capacity large table: designing and storing the coverage capacity information in a large table, taking grid code as the primary key, for efficient retrieval of satellite coverage information.

[0032] In some embodiments, the structure of the satellite coverage capacity large table specifically includes: taking grid code as the primary key, and each grid is associated with at least one coverage capacity vector; the coverage capacity vector includes satellite identification, 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. The sensor state vector may contain, for example, side swing angle, pitch angle and corresponding maneuver energy consumption, etc.

[0033] In some embodiments, the satellite coverage capacity large table construction further includes the following storage method: traversing the coverage capacity information of each satellite, merging the coverage time windows of consecutive same grids; converting the coverage information with satellite as the primary key into the covered information with grid as the primary key, and updating and writing the satellite identification, time window and sensor state parameter vector.

[0034] In some embodiments, the satellite coverage capacity large table construction further includes the following update mechanism: a dual mechanism of periodic full update and event incremental update; the periodic full update periodically clears out-of-date information, recalculates the coverage capacity based on the latest orbit parameters and writes it to a temporary table, and switches the official table through a transaction; the event incremental update locates the satellite when the satellite parameters change, recalculates the coverage capacity grid representation, deletes the old record and writes the new result.

[0035] Step S3, dual-target multi-satellite resource allocation: based on the capacity large table, taking the optimal task completion time as the main target and the lowest energy consumption as the secondary target, performing a time optimization phase and an energy optimization phase to allocate satellite resources, and generating multiple optimization schemes, wherein the time optimization phase prioritizes compressing the overall task completion time, the energy optimization phase reduces the total system energy consumption under time constraints, and a dynamic relaxation mechanism is used to balance the conflict between the two targets.

[0036] In some embodiments, the time optimization phase of the dual-target multi-satellite resource allocation specifically includes: discretizing the target area of the observation task into a grid set, retrieving the candidate coverage records from the capacity large table; generating an initial allocation scheme, identifying the key grid with the latest end time; iteratively retrieving the capacity large table to assign satellites that can provide earlier coverage windows to the key grid; each iteration optimizes a predetermined proportion of the latest grid until the overall completion time cannot be compressed.

[0037] In some embodiments, the energy consumption optimization stage of the dual-target multi-satellite resource allocation specifically comprises: on the basis of the time optimization result, screening the grid whose observation end time is significantly earlier than the task completion time; retrieving the capability table, and attempting to replace the satellite with low energy consumption, while meeting the following conditions: the overall task completion time is not delayed after replacement, and the energy consumption reduction after single replacement reaches a significant threshold; and accumulating the reduction of the system total energy consumption through local replacement.

[0038] In some embodiments, the dynamic relaxation mechanism specifically comprises: when the time optimization stage fails to compress the completion time for a predetermined number of consecutive times, automatically relaxing the energy consumption reduction threshold of the energy consumption optimization stage; and when the average remaining energy of the satellite is lower than a safety threshold, suspending the time optimization stage and forcing the energy consumption protection mode.

[0039] The predetermined proportion, the energy consumption significant threshold, and the like can be set according to the calculation resources and optimization benefits, and the safety threshold can be configured in combination with the remaining energy of the satellite and the like.

[0040] In some embodiments, the dual-target multi-satellite resource allocation further comprises: generating three sets of scheduling schemes: a time-optimal scheme aiming to minimize the completion time, an energy-optimal scheme aiming to minimize the total energy consumption, and a balanced scheme aiming to maximize the weighted comprehensive score of time and energy consumption; updating the state markers of the capability table, and outputting the user-selected scheme.

[0041] The multi-satellite resource allocation method of the application effectively solves the resource coordination difficulty problem caused by the inconsistent description specifications of the satellite coverage capability in the prior art by discretizing the earth's surface into spatial grids of uniform specifications and precomputing and storing the satellite coverage capability, innovatively constructing a satellite coverage capability table taking grid coding as the primary key; the hierarchical decoupling dual-target optimization mechanism designed based on the capability table compresses the overall task completion time as the priority stage, reduces the system total energy consumption through the energy consumption optimization stage under the time constraint, and introduces a dynamic relaxation mechanism to adaptively balance the dual-target conflict, thereby eliminating the complex target polygon decomposition and time window calculation links in the traditional method, significantly reducing the calculation complexity of large-scale satellite resource allocation; at the same time, the method generates three sets of schemes of time-optimal, energy-optimal, and balanced schemes to flexibly adapt to different task requirements, and the capability table structure supports parameter expansion without significantly increasing the query burden, and finally realizes the efficient coordination and scheduling of a satellite cluster of more than 100 satellites, while ensuring full coverage, and simultaneously improving the timeliness and energy utilization rate of the task.

[0042] The specific embodiments of the application are further described below.

[0043] A space-discrete-based multi-satellite resource allocation method, by discretizing the earth's surface into a grid and precomputing and storing satellite coverage capabilities, converts the complex satellite resource allocation problem into an efficient grid-based query problem, significantly reducing the computational complexity. In the implementation of the scheme, in view of the inconsistent description of the current space-time coverage capability, the remote sensing satellite capability is described based on space discretization; in order to realize efficient processing of tasks, a capability table is designed; in order to meet the needs of different types of observation tasks, a multi-objective optimization method is designed, and satellite resources are allocated based on the capability table. The implementation method mainly includes three core parts: (I) Satellite coverage capability calculation based on space discretization This part realizes the accurate mapping from satellite parameters to coverage capability represented by grid, including three-step calculation process.

[0044] (1) Satellite orbit recursion: set the update period T, and calculate the orbits of all satellites. Input the orbit parameters of the satellite, use the existing orbit recursion model to recursively calculate the position of the satellite in the future update period, calculate the subsatellite point trajectory of the satellite in this period of time, and output in the form of position coordinates + time stamp sequence; (2) Sensor coverage strip calculation: set the time resolution , based on the satellite sensor parameters (field of view, width), calculate the instantaneous coverage strip of the satellite at each time: (3) Mapping calculation from coverage strip to space grid: use the GeoSOT global subdivision grid system, which has perfect multi-scale grid calculation rules, set the grid scale, take the longitude and latitude of the instantaneous coverage strip polygon corner at each time as the input, calculate the grid set corresponding to the instantaneous strip according to the longitude and latitude position and grid code calculation rules in the GeoSOT grid system, and calculate the same for each coverage strip at each time until all coverage strips in the update period are calculated. The grid set corresponding to the position. Thus, the satellite coverage strip represented by the space grid in the form of "time-grid set" is obtained, the description specification is unified, and the process is shown in Figure 1 . Those skilled in the art can understand that, in addition to GeoSOT, S2, H3, etc. Global subdivision grid system can also be used to realize coverage strip mapping.

[0045] (II) Design of satellite coverage capability table The function of the coverage capability table is to store the satellite coverage capability for resource allocation calculation and calling. To realize efficient resource allocation calculation, replace time with space. The specific implementation includes capability table structure design, capability table storage and query method, and capability table update mechanism.

[0046] (1) Capability table structure design 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: CV = { Satellite ID, Coverage Time Window {[Start Time 1, End Time 1], [Start Time 2, End Time 2], ...}, Sensor Type, Sensor Status} 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.

[0047] (2) Capability table storage method 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 2 As 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.

[0048] (3) Capability table update mechanism 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.

[0049] 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.

[0050] (III) A dual-target, multi-satellite resource allocation method based on a large satellite capability table The dual-target multi-satellite resource allocation method solves the dual-target collaborative optimization problem of time efficiency and energy consumption in large-scale satellite cluster observation tasks. The core idea is to compress the overall task completion time and reduce the total energy consumption of the satellite system by using a hierarchical progressive optimization strategy while ensuring full coverage of all target grids. The specific implementation is as follows: When 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, and the energy consumption is calculated based on the sensor type and sensor state. Energy consumption includes two aspects: normal working energy consumption and maneuvering energy consumption. The normal working energy consumption is only related to the on-time, which is described as a linear relationship in this method. The linear energy consumption model can be replaced by a nonlinear model or a lookup table according to the actual energy consumption characteristics of the satellite, without affecting the overall optimization framework. The maneuvering energy consumption is determined by the sensor state, and different side swing angles correspond to different maneuvering energy consumption. The calculated energy consumption replaces the sensor type and sensor state in the original CV to form an initial data set. Based on this data, a two-stage optimization is performed: First stage: time optimization. Identify the grid with the latest end time in the current solution (referred to as "critical grid") and find an earlier coverage opportunity for it by searching the capability table. If a satellite can provide an earlier coverage window (e.g., the original end time is 9:30, and the new satellite can be advanced to 9:00), immediately assign the satellite to the grid. This process is iteratively executed, optimizing the current 10% of the latest grids each time, until the overall completion time cannot be further advanced. This stage ensures that the task completion time approaches the theoretical minimum under satellite resource constraints.

[0051] Second stage: energy consumption optimization. Based on the results of the time optimization, the system selects grids with time margins (i.e., grids whose observation end time is significantly earlier than the task completion time) and attempts to replace them with satellites with lower energy consumption. Replacement must satisfy two constraints: first, the coverage time window of the new satellite must not delay the overall task completion time; second, the maneuvering energy consumption must be significantly reduced (threshold set to save energy ≥ 5% per replacement). For example, if a grid is originally covered by satellite A (energy consumption of 12 units), and satellite B can cover the same time period with an energy consumption of only 9 units, then the replacement is performed. This stage accumulates the reduction in system total energy consumption by local adjustment while maintaining the time optimization results of the first stage.

[0052] To balance the conflict between the two objectives, a dynamic relaxation mechanism is introduced: when the time optimization phase fails to compress the completion time for 3 consecutive iterations, the energy saving threshold of the energy optimization phase is automatically relaxed (from 5% to 3%), allowing more grids to participate in replacement; conversely, if the average remaining energy of the satellite is lower than the safety threshold (25%), the time optimization is suspended and the energy conservation mode is forced. Finally, a multi-solution set is generated for user decision: 1) Time optimal solution: only target is to minimize the completion time; 2) Energy optimal solution: only target is to minimize the total energy consumption; 3) Balanced solution: take the one with the highest weighted comprehensive score (weights are preset according to task type). All solutions guarantee 100% grid coverage, and the state flag of the atomic transaction update capability table is updated.

[0053] The main process of the multi-satellite resource allocation method is summarized as follows: Step one: analyze the observation task and convert the target area into a set of grid IDs; Step two: retrieve the satellite coverage capability table to obtain candidate coverage records that meet the deadline; Step three: execute the time optimization phase to iteratively compress the completion time of critical grids; Step four: execute the energy optimization phase to replace high-energy satellites under time constraints; Step five: generate three sets of scheduling schemes: time optimal, energy optimal, and balanced; Step six: update the capability table state and output the user-selected scheme.

[0054] Figure 3 The specific process of the dual-target multi-satellite resource allocation calculation based on the satellite capability table is shown.

[0055] In summary, the present application proposes a dual-target collaborative multi-satellite resource allocation method based on spatial discretization. The important innovations and contributions of the present application include: 1. Unified modeling method of satellite coverage capability based on spatial discretization; 2. Storage, query and update mechanism of satellite coverage capability table; 3. Dual-target hierarchical decoupling multi-satellite resource allocation method based on satellite capability table.

[0056] Compared with other typical remote sensing satellite resource allocation technologies, the technical scheme of the application is proposed for large-scale satellite resource allocation application scenarios, the unified capability description specification, the pre-storage, and the on-demand calling idea of the technical scheme of the application exchanges space for time, saves the complicated pre-computation steps of the traditional method for regional target polygon decomposition and time window calculation, improves the resource allocation calculation efficiency, and through the double-target hierarchical decoupling and dynamic relaxation mechanism, can meet the task completion time and energy consumption double-target optimization demand of different types of observation tasks, and quickly generate three schemes of time optimization, energy consumption optimization and on-demand balance. And the method has good scalability, and adding additional parameters according to the actual situation only affects the large table column number, and has little effect on the query calculation complexity.

[0057] The embodiment of the application also provides a storage medium for storing a computer program, which is executed to perform at least the method described above.

[0058] The embodiment of the application also provides a control device, which comprises a processor and a storage medium for storing a computer program; wherein the processor is used to execute the computer program to perform at least the method described above.

[0059] The embodiment of the application also provides a processor, which executes a computer program to perform at least the method described above.

[0060] The storage medium can be realized by any type of non-volatile storage device, or a combination thereof. The non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a ferromagnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc or a compact disc read-only memory (CD-ROM). The magnetic surface memory can be a disk memory or a tape memory. The storage medium described in the embodiment of the application is intended to include but not limited to these and any other suitable types of memory.

[0061] In several embodiments provided by the present application, it should be understood that the disclosed system and method can be implemented in other manners. The described device embodiments are merely illustrative. For example, the division of the units is merely logical function division, and there can be other division manners in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling or direct coupling or communication connection between the components can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0062] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, can be located in one place or distributed on a plurality of network units; and some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0063] In addition, each functional unit in each embodiment of the present application 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; and the integrated unit can be implemented in the form of hardware or hardware plus software functional unit.

[0064] Those skilled in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by program instruction related hardware, and the foregoing program can be stored in a computer readable storage medium, and the program is executed to perform the steps of the above-mentioned method embodiments; and the foregoing storage medium includes mobile storage equipment, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk and various storage medium capable of storing program codes.

[0065] Alternatively, the integrated unit of the present application, if implemented in the form of a software function module and sold or used as an independent product, can also be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present application. The foregoing storage medium includes: mobile storage equipment, ROM, RAM, magnetic disk or optical disk, and various storage media capable of storing program codes.

[0066] The methods disclosed in the several method embodiments of the present application can be combined arbitrarily without conflict to obtain new method embodiments.

[0067] The features disclosed in the several product embodiments of the present application can be combined arbitrarily without conflict to obtain new product embodiments.

[0068] The features disclosed in the several method or device embodiments of the present application can be combined arbitrarily without conflict to obtain new method embodiments or device embodiments.

[0069] The above is a further detailed description of the present application in combination with specific preferred embodiments, and the specific implementation of the present application should not be limited to these descriptions. For those skilled in the art to which the present application belongs, without departing from the concept of the present application, a number of equivalent substitutions or obvious modifications can be made, and the performance or use is the same, which should be regarded as belonging to the protection scope of the present application.

Claims

1. A method for dual-objective collaborative multi-satellite resource allocation based on spatial discretization, characterized in that, The method comprises the following steps: S1, calculating the satellite coverage capacity based on spatial discretization: discretizing the earth's surface into a grid, and pre-calculating the coverage capacity of the satellite, mapping the satellite coverage strip to the grid to realize a unified description of the coverage capacity; S2, constructing a large table of satellite coverage capacity: designing and storing the coverage capacity information in a large table, taking the grid code as the primary key for efficient retrieval of satellite coverage information; S3, dual-target multi-satellite resource allocation: based on the capacity table, taking the optimal task completion time as the main target and the lowest energy consumption as the secondary target, performing a time optimization phase and an energy optimization phase to allocate satellite resources and generate multiple optimization schemes, wherein the time optimization phase prioritizes compressing the overall task completion time, the energy optimization phase reduces the total energy consumption of the system under time constraints, and a dynamic relaxation mechanism is used to balance the conflict between the two targets.

2. The method according to claim 1, wherein, In step S1, the satellite coverage capacity calculation based on spatial discretization specifically comprises: Setting an update period, using an orbit recursion model to calculate the subsatellite point trajectory of the satellite; Based on the satellite sensor parameters, calculate the instantaneous coverage strip of the satellite at a given time resolution; Input the longitude and latitude of the instantaneous coverage strip polygon corner into the global subdivision grid system, and map it to a grid set according to the grid calculation rules of the system, and output the satellite coverage capacity represented by time-grid set.

3. The method according to claim 1 or 2, wherein, In step S2, the structure of the large table includes: Taking the grid code as the primary key, each grid is associated with at least one coverage capacity vector; The coverage capacity vector includes satellite identification, coverage time window set, sensor type and sensor state vector, wherein the sensor state vector is used to mark the attitude adjustment information required by the satellite to cover the grid.

4. The method according to claim 3, wherein, In step S2, the satellite coverage capacity table construction further includes the following storage method: Iterate through the coverage capacity information of each satellite, merge the coverage time windows of consecutive identical grids; Convert the coverage information with satellite as the primary key to the covered information with grid as the primary key, update and write the satellite identification, time window and sensor state parameter vector.

5. The method according to claim 1 or 2, wherein, In step S2, the satellite coverage capacity table construction further includes the following update mechanism: Using a dual mechanism of periodic full update and event incremental update; The periodic full update periodically clears outdated information, recalculates the coverage capacity based on the latest orbit parameters and writes it to a temporary table, and switches the primary table through a transaction; The event incremental update locates the satellite when the satellite parameters change, recalculates the coverage capacity grid representation, deletes the old record and writes the new result.

6. The method according to claim 1 or 2, wherein, In step S3, the time optimization phase of the dual-target multi-satellite resource allocation specifically comprises: Discretize the target area of the observation task into a grid set, retrieve the capacity table to obtain the candidate coverage record; Generate an initial allocation scheme and identify the key grid with the latest end time; Iteratively retrieve the capacity table to assign satellites that can provide earlier coverage windows to the key grid; Optimize a predetermined proportion of the latest grid in each iteration until the overall completion time cannot be compressed.

7. The method according to claim 6, wherein, In step S3, the energy optimization phase of the dual-target multi-satellite resource allocation specifically comprises: On the basis of time optimization results, the grid whose observation end time is significantly earlier than the task completion time is screened out; The ability table is retrieved to attempt to replace low-energy satellites while meeting the following conditions: the overall task completion time is not delayed after replacement, and the energy consumption reduction of a single replacement reaches a significant threshold; The system total energy consumption is cumulatively reduced through local replacement.

8. The method according to claim 1 or 2, wherein, In step S3, the dynamic relaxation mechanism includes: When the time optimization phase fails to compress the completion time for a predetermined number of consecutive times, the energy consumption reduction threshold of the energy consumption optimization phase is automatically relaxed; When the average remaining energy of the satellites is lower than a safety threshold, the time optimization phase is suspended and the energy consumption protection mode is forced to enter.

9. The method according to any one of claims 1 to 2, wherein, In step S3, the dual-target multi-satellite resource allocation further includes: Three sets of scheduling schemes are generated: a time-optimal scheme aiming to minimize the completion time, an energy-optimal scheme aiming to minimize the total energy consumption, and a balanced scheme aiming to maximize the weighted comprehensive score of time and energy consumption; The state markers of the ability table are updated, and a user-selected scheme is output.

10. A computer program product comprising a computer program, characterized in that, The computer program, when executed by a processor, implements the dual-target collaborative multi-satellite resource allocation method based on spatial discretization according to any one of claims 1 to 9.

Images