Constellation task collaborative planning method, constellation task collaborative planning device and constellation task collaborative planning equipment based on space calculation

Abstract

The invention relates to the field of in-orbit data processing, and discloses a constellation task collaborative planning method, device and equipment based on space computing, and the method comprises the steps: obtaining task input data, the task input data comprises an observation satellite set and a regional grid set, and the observation satellite set comprises a plurality of prepared observation satellites; determining an observation plan of each preparatory observation satellite based on the task input data, and determining a target observation satellite of the regional grid set and observation information of the target observation satellite according to the observation plans of all the preparatory observation satellites; obtaining an inter-satellite link diagram with link time constraints, and determining a target transmission path corresponding to the target observation satellite based on the inter-satellite link diagram; and generating an action instruction list corresponding to the task input data according to the target transmission path and the observation information, wherein the action instruction list is used for controlling the task node to execute the collaborative task. According to the technical scheme provided by the invention, collaborative planning of various satellites can be realized, and the data on-orbit processing efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of on-orbit data processing, and in particular to a method, apparatus and equipment for collaborative planning of constellation missions based on space computing. Background Technology

[0002] In space missions for Earth observation, data collected by remote sensing and communication satellites must be transmitted back to ground stations for analysis. In practical applications, due to limited ground station resources and constrained satellite-to-ground transmission bandwidth, a large amount of observation data cannot be transmitted in a timely manner, and the ground processing process is time-consuming.

[0003] In related technologies, real-time data processing via on-orbit computing satellites, followed by the transmission of results back to ground stations, ensures timely data processing. However, during data transmission and computation between observation and computing satellite constellations, data conflicts and delays are prone to occur, resulting in prolonged on-orbit computation and transmission times and low efficiency. Therefore, improving on-orbit data processing efficiency has become a key research focus in the field of on-orbit data processing. Summary of the Invention

[0004] This application provides a method, apparatus, and equipment for collaborative planning of constellation missions based on space computing, which can realize collaborative planning of multiple types of satellites and improve the efficiency of on-orbit data processing.

[0005] This application provides a constellation mission collaborative planning method based on space computing. The method is applied to multiple mission nodes, including target observation satellites, computing satellites, and ground stations. The method includes: acquiring mission input data, which includes a set of observation satellites and a regional grid set, wherein the set of observation satellites includes multiple candidate observation satellites; determining observation plans for each candidate observation satellite based on the mission input data, and determining the target observation satellite and its observation information for the regional grid set based on the observation plans of all candidate observation satellites; acquiring an inter-satellite link diagram with link time constraints, and determining a target transmission path corresponding to the target observation satellite based on the inter-satellite link diagram, wherein the target transmission path includes multiple mission nodes; and generating an action instruction sheet corresponding to the mission input data based on the target transmission path and the observation information, wherein the action instruction sheet is used to control the mission nodes to execute collaborative tasks.

[0006] In one embodiment, the observation plan includes at least the observable area of ​​the prospective observation satellites, and the regional grid set includes multiple regional grids. Determining the target observation satellite and its observation information based on the observation plans of all prospective observation satellites includes: acquiring the observable areas of all prospective observation satellites; matching the observable areas with the regional grid set; and determining the target observation plan corresponding to each regional grid based on the matching results. Based on the target observation plan corresponding to any regional grid, determining the prospective observation satellite, target orbit number, target lateral tilt angle, and target observation period corresponding to the regional grid; and identifying the prospective observation satellite corresponding to the regional grid as the target observation satellite. The target orbit number, target lateral tilt angle, regional grid, and target observation period are then used as the observation information of the target observation satellite.

[0007] In one implementation, the mission input data further includes a set of orbit circle numbers and a set of observation time periods; determining the observation plan for each prospective observation satellite based on the mission input data includes: determining multiple opportunity tuples for any prospective observation satellite according to the mission input data, wherein the opportunity tuples represent the lateral tilt angle of the prospective observation satellite at a specified observation time under a specified orbit circle number; constructing an observation opportunity set for each prospective observation satellite based on the opportunity tuples of each prospective observation satellite, and determining the observation plan for each prospective observation satellite in the observation opportunity set based on a first optimization objective and a first constraint.

[0008] In one implementation, the first optimization objective includes regional grid coverage, orbital circle number threshold, and absolute value of side-slip angle, and the first constraint includes that the absolute value of the side-slip angle of each of the observed satellites is less than or equal to the side-slip angle threshold.

[0009] In one implementation, determining the target transmission path corresponding to the target observation satellite based on the inter-satellite link diagram includes: dynamically constructing a priority queue of the target observation satellite based on the inter-satellite link diagram, and determining the data transmission path of the target observation satellite based on a second optimization target and the priority queue, wherein the data transmission path includes multiple target nodes; verifying each of the target nodes based on a second constraint condition, and determining the target transmission path based on the data transmission path of the target observation satellite if the verification result indicates that the target node is valid.

[0010] In one implementation, the target node carries a node timing sequence; determining the data transmission path of the target observation satellite based on the priority queue and the second optimization objective includes: sorting the priority queue based on the second optimization objective, determining multiple target nodes of the target observation satellite based on the sorted priority queue; constructing the adjacency edges of each of the target nodes, and determining the departure timing sequence and transmission time of any of the adjacency edges, updating the node timing sequence of the target nodes based on the departure timing sequence and the transmission time, and constructing the data transmission path based on the updated target nodes.

[0011] In one implementation, the second optimization objective includes cumulative transmission time, and the second constraints include transmission time constraints, link number constraints, non-cyclic path constraints, time window constraints, and computational constraints.

[0012] In one embodiment, the action instruction sheet includes action instructions for each of the task nodes, and the task nodes carry task timing information. Generating the action instruction sheet corresponding to the task input data based on the target transmission path and the observation information includes: acquiring multiple task nodes in the target transmission path and acquiring the task timing information of each task node; generating action instructions for the target observation satellite based on the observation information and task timing information of the target observation satellite for the task node representing the target observation satellite; and generating action instructions for the other task nodes based on the node type and task timing information of the task nodes.

[0013] A second aspect of this application provides a constellation mission collaborative planning device based on space computing. The device includes: a data preparation unit for acquiring mission input data, the mission input data including a set of observation satellites and a set of regional grids, the set of observation satellites including multiple candidate observation satellites; an observation planning unit for determining the observation plan of each candidate observation satellite based on the mission input data, and determining the target observation satellite and the observation information of the target observation satellite based on the observation plans of all candidate observation satellites; a path planning unit for acquiring an inter-satellite link diagram with link time constraints, and determining a target transmission path corresponding to the target observation satellite based on the inter-satellite link diagram, wherein the target transmission path includes multiple mission nodes; and an instruction generation unit for generating an action instruction sheet corresponding to the mission input data based on the target transmission path and the observation information, the action instruction sheet being used to control the multiple mission nodes to execute collaborative tasks.

[0014] A third aspect of this application provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the space-based constellation mission collaborative planning method as described in the first aspect above.

[0015] The technical solution provided in this embodiment improves on-orbit data processing efficiency through the collaborative planning of constellation tasks such as observation of the target observation area, image data calculation, and transmission. Specifically, by precisely planning the observation information and transmission paths of observation satellites to generate action command sheets that can be directly used to control task nodes, seamless temporal connection of actions such as observation, transmission, and calculation is ensured. This enables collaborative planning of multi-process constellation tasks, avoiding time and resource conflicts that may result from separate planning, and improving the collaborative planning efficiency of multiple types of satellites. Specifically, the transmission path planning based on a time-constrained inter-satellite link diagram ensures that links are available within the effective time window, while prioritizing the path with the highest data processing efficiency, shortening on-orbit data transmission latency, and achieving efficient observation, data processing, and transmission of the target observation area, thus improving on-orbit data processing efficiency. Furthermore, by dividing the target observation area into regional grids, the target observation satellites in each grid can be accurately matched, improving the utilization rate of observation resources.

[0016] As can be seen, the technical solution provided in this application can realize the collaborative planning of multiple types of satellites and improve the efficiency of on-orbit data processing. At the same time, it can also improve the utilization rate of observation resources. Attached Figure Description

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

[0018] Figure 1 A schematic diagram illustrating the steps of a constellation mission collaborative planning method based on space computing, provided for the implementation of this application; Figure 2 A schematic diagram illustrating the steps for determining a target observation satellite and observation information according to one embodiment of this application; Figure 3 A schematic diagram illustrating the steps for determining a target transmission path provided in one embodiment of this application; Figure 4 A schematic diagram of the structure of a constellation mission collaborative planning device based on space computing, provided as one embodiment of this application; Figure 5 This is a schematic diagram of the structure of a computer device provided in one embodiment of this application. Detailed Implementation

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

[0020] Furthermore, the use of terms such as "first," "second," etc., in this application is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of embodiments in this application, unless otherwise stated, "multiple" means two or more. Additionally, the use of "based on" or "according to" implies openness and inclusiveness, because processes, steps, calculations, or other actions "based on" or "according to" one or more of the stated conditions or values ​​may in practice be based on additional conditions or beyond the stated values.

[0021] In space missions conducting Earth observation, the current mainstream data processing method primarily involves collecting data from observation satellites and transmitting it back to ground stations for analysis. However, due to limited ground station resources and bandwidth constraints, a large amount of observational data cannot be transmitted in a timely manner. Statistics show that less than one-tenth of the valid data is successfully transmitted back to Earth, resulting in a significant waste of data resources. Furthermore, the data transmission and ground processing processes are time-consuming, significantly delaying the timeline from observation to result acquisition. This makes it difficult to meet the practical needs of scenarios with extremely high timeliness requirements, such as emergency rescue and target surveillance.

[0022] To overcome the aforementioned time constraints, a space computing model has emerged in recent years, which involves using computing satellites to process data in orbit. Specifically, one or more observation satellites collect data and transmit it to computing satellites for processing. The computing satellites then transmit the processed results directly back to the ground station, or via other relay satellites, ensuring the timeliness and accuracy of the data processing. However, different types of satellites are prone to transmission delays during data processing or transmission. Furthermore, with large constellations, data conflicts and link constraints can easily lead to data reception failures. The lack of coordinated scheduling during satellite observation, computation, and transmission results in low on-orbit data processing efficiency.

[0023] In view of this, one or more embodiments of this application provide a constellation mission collaborative planning method, apparatus and equipment based on space computing, which can solve the above problems, and perform collaborative planning of observation satellites, computing satellites and ground stations, so as to take into account path optimization and transmission constraints during data acquisition, transmission and computing, thereby improving the efficiency of on-orbit data processing.

[0024] Please see Figure 1 One embodiment of this application provides a constellation mission collaborative planning method based on space computing. The method is applied to multiple mission nodes, including target observation satellites, computing satellites, and ground stations, to achieve collaborative planning of the observation of the target observation area, the computation of image data, and the transmission process. The method may include the following steps: S1: Acquire task input data, which includes a set of observation satellites and a set of regional grids, wherein the set of observation satellites includes multiple pre-observation satellites; S3: Based on the task input data, determine the observation plan for each preparatory observation satellite, and based on the observation plans of all preparatory observation satellites, determine the target observation satellite and the observation information of the target observation satellite for the regional grid set; S5: Obtain an inter-satellite link diagram with link time constraints, and determine the target transmission path corresponding to the target observation satellite based on the inter-satellite link diagram, wherein the target transmission path includes multiple task nodes; S7: Generate an action instruction sheet corresponding to the task input data based on the target transmission path and the observation information. The action instruction sheet is used to control the task node to execute the collaborative task.

[0025] The aforementioned task nodes can be understood as the satellites and ground stations participating in a space computing mission. In collaborative missions, these nodes need to operate according to a specific sequence and path, such as observation satellites taking pictures, computing satellites processing data, inter-satellite link data transmission, and satellite-to-ground link data transmission. These task nodes are determined based on a space constellation, which typically includes multiple observation satellites and multiple computing satellites. This constellation effectively organizes different types of satellites and utilizes the varying capabilities of onboard payloads to perform collaborative observation, computation, and transmission of data over the target observation area.

[0026] First, before performing node collaborative planning, known task input data needs to be acquired. Based on this task input data, the target observation area and target transmission path are determined. The task input data includes at least a set of observation satellites and a set of regional grids. For example, the task input data may include an initial timestamp, a set of observation satellites, a set of computation satellites, a set of available ground stations, a set of orbital circle numbers, a set of observation time periods, and a set of regional grids. The set of observation satellites includes multiple candidate observation satellites to identify the target observation satellite. The target observation area is divided into multiple regional grids to form a set of regional grids. Each regional grid can be computed as a rectangle, with its vertices determined by the latitude and longitude vertices of its geographical location.

[0027] In this embodiment, the first step is to determine the target observation satellites required for the target observation area. Specifically, since the target observation area is divided into multiple regional grids and presented as a set of regional grids, it is first necessary to determine the reserve observation satellites capable of observing each regional grid. These reserve observation satellites are then designated as target observation satellites. A single reserve observation satellite may simultaneously observe multiple regional grids; therefore, for a set of regional grids, there may be one or more target observation satellites. Furthermore, each reserve observation satellite has its own observation plan. This observation plan represents the specific task arrangement specified for each reserve observation satellite, determining when, where, and in what attitude the satellite will conduct observations. Based on the above observation plan, the observation information for each target observation satellite can be determined. This observation information represents the specific parameters of the target observation satellite when performing its tasks, guiding how the satellite completes its observation mission. This typically includes the orbit number, side-swing angle, regional grid, and observation period. For example, this observation information clarifies which orbit number, how many degrees of side-swing, what time the satellite must start and what time it must shut down in order to capture images of the target area.

[0028] In this embodiment, the inter-satellite link diagram described above is used to describe the link relationships between various satellites. Data transmission links between satellites can only be established within a specific time window. The inter-satellite link diagram contains all possible inter-satellite links, i.e., the data transmission links, each carrying its own link time constraints. Specifically, the inter-satellite link diagram can be represented as a set of multiple inter-satellite links, for example, in the format: {node:[(adjacent node, edge availability start time, edge availability end time, bandwidth),...]}. Specifically, for mutually visible satellite pairs, the time window for establishing inter-satellite links between them is calculated, and the specific parameters for establishing links between satellites within the current time window are determined, including the available start time, available end time, and bandwidth of the link. The link establishment results are then organized into a graph, where nodes represent satellites and edges represent inter-satellite links. Furthermore, transmission paths are planned based on the time-constrained inter-satellite link diagram to ensure that links are available within the effective time window, while prioritizing the path with the highest data processing efficiency to shorten on-orbit data transmission latency. The aforementioned target transmission path includes multiple mission nodes to represent the observation satellites, computing satellites, and ground stations that the current transmission path needs to pass through.

[0029] In one embodiment, the required task nodes are determined from multiple nodes in the inter-satellite link graph to construct target transmission paths between the task nodes. Specifically, multiple first-segment transmission paths from the target observation satellite to the computing satellite and multiple second-segment transmission paths from the computing satellite to the ground station are determined respectively. Multiple overall transmission paths are determined based on the combination of the first-segment and second-segment transmission paths, and the target transmission path with the highest data processing efficiency while ensuring transmission performance is selected from the multiple overall transmission paths. For example, when there is only one target observation satellite corresponding to the regional grid set, multiple first-segment transmission paths from the target observation satellite to the computing satellite are determined. For example, when there are multiple target observation satellites corresponding to the regional grid set, multiple first-segment transmission paths from each target observation satellite to the computing satellite are determined respectively.

[0030] In this embodiment, an action instruction sheet is generated based on the target transmission path and observation information to inform each satellite of a specified action at a certain time. Specifically, the execution time of each specified action can be determined based on the node status of each task node along the target transmission path, and this execution time is written into the corresponding action instruction as a timestamp. The node status represents the departure time and transmission time of that node, and is determined based on time window constraints with the next node. For example, the specified actions may include satellite power-on, attitude adjustment, camera on / off control, inter-satellite link creation / deletion, satellite-to-ground link creation / deletion, computation satellite initiating data processing, and downloading computation results to the ground station. The generated action instruction sheet ensures that computation tasks only begin after all data has been transmitted to the computation satellite, while avoiding time and resource conflicts that might result from separate planning. This ensures seamless temporal coordination of observation, transmission, and computation actions, enabling collaborative planning among multiple types of satellites and improving on-orbit data processing efficiency.

[0031] Based on the above ideas, the technical solution provided in this embodiment of the application improves the efficiency of on-orbit data processing by coordinating constellation tasks such as observation of the target observation area, image data calculation, and transmission. Specifically, by precisely planning the observation information and transmission paths of the observation satellites to generate action command sheets that can be directly used to control task nodes, seamless connection of observation, transmission, and calculation actions in time is ensured, thereby realizing multi-process constellation task coordinating planning. This avoids time and resource conflicts that may be caused by separate planning and improves the efficiency of coordinating planning for multiple types of satellites. Among them, the transmission path is planned based on the time-constrained inter-satellite link diagram to ensure that the link is available within the effective time window, while prioritizing the path with the highest data processing efficiency to shorten the on-orbit data transmission delay, thus achieving efficient observation, data processing, and transmission of the target observation area. In addition, by dividing the target observation area into regional grids, the target observation satellites in each grid can be accurately matched, improving the utilization rate of observation resources.

[0032] Please see Figure 2 In one implementation, the aforementioned observation plan includes at least the observable area that the prospective observation satellites can observe, and the aforementioned area grid set includes multiple area grids; the target observation satellite and the corresponding observation information are determined by matching the observable areas of all prospective observation satellites with the area grid set. Specifically, this is performed according to the following steps: S21: Obtain the observable areas of all the satellites to be observed, match the observable areas with the set of regional grids, and determine the target observation plan corresponding to each regional grid based on the matching results; S23: Based on the target observation plan corresponding to any of the regional grids, determine the pre-observation satellite, target orbit number, target side swing angle and target observation time period corresponding to the regional grid, and determine the pre-observation satellite corresponding to the regional grid as the target observation satellite; S25: The target orbit number, the target side swing angle, the regional grid, and the target observation period are used as the observation information of the target observation satellite.

[0033] In this embodiment, each prospective observation satellite has its own observation plan. The observable area of ​​the prospective observation satellite is determined from the observation plan, thereby identifying one or more target observation satellites capable of observing the regional grid set. For example, the above observation plan can be quantified in the following form: ,in, In preparation for satellite observation, For the orbital circle number, In preparation for satellite observation In orbit number Side swing angle, The set of observable regions To determine the set of observation periods for these regions, by analyzing... The calculations can determine the camera's power-on and power-off times. This is the start time for taking photos. This is the end time of the photo capture. If a matching area grid exists in the above set of observable areas, the prospective observation satellite is designated as the target observation satellite, and the orbital circle number, lateral tilt angle, and observation period corresponding to that area grid are extracted from the prospective observation satellite's observation plan as observation information. Here, the orbital circle number can be understood as the orbital number of the satellite's orbit around the Earth, and the lateral tilt angle can be understood as the angle at which the satellite or camera tilts laterally.

[0034] The technical solution provided in this embodiment refines the specific steps for selecting target observation satellites and determining observation information, ensuring a high degree of compatibility between target observation satellites and the observation requirements of the grid. Specifically, a matching mechanism between the observable area and the regional grid is used to determine the required target observation satellites, improving the accuracy of resource allocation. Furthermore, core parameters such as orbital circle number and lateral tilt angle in the observation information are clearly defined and correspond to the quantitative indicators of the observation plan, enabling rapid and accurate acquisition of the corresponding observation information and providing data support for the subsequent generation of action command sheets. Simultaneously, locking the observation period in the observation plan avoids temporal ambiguity in observation actions, facilitates timing coordination in subsequent transmission and calculation stages, improves the overall efficiency of multi-satellite collaborative planning, and enhances data processing efficiency during the data observation process.

[0035] In one implementation, the aforementioned task input data further includes a set of orbit circle numbers and a set of observation time periods. The observation plan for each candidate observation satellite is determined based on the set of orbit circle numbers, the set of observation time periods, and the set of regional grids. Specifically, multiple opportunity tuples are determined for any candidate observation satellite based on the task input data, wherein the opportunity tuples represent the side-slip angle of the candidate observation satellite at a specified observation time under a specified orbit circle number within a specified regional grid. Based on the opportunity tuples of each candidate observation satellite, an observation opportunity set for the candidate observation satellite is constructed, and based on a first optimization objective and a first constraint, the observation plan for each candidate observation satellite is determined from the observation opportunity set.

[0036] In this embodiment, the first optimization objective includes regional grid coverage, orbit number threshold, and the sum of absolute values ​​of side-swing angles. The first constraint includes that the absolute value of the side-swing angle of each observation satellite is less than or equal to the side-swing angle threshold. Specifically, the first optimization objective is to maximize regional grid coverage, minimize the maximum orbit number, and minimize the sum of side-swing angles, where the sum of side-swing angles is the sum of the minimum and maximum side-swing angles. The first constraint is specifically expressed as follows: , (Condition: when) In the set, there exists and ),in, This is the satellite's theoretical maximum side yaw angle. The chance tuple represents the side angle of the grid corresponding to a specified region at a specified observation time under a specified orbital circle number of the satellite to be observed. In addition, the influence of illumination on the photography can be added to the first constraint condition.

[0037] The aforementioned regional grid coverage can be understood as the proportion of successfully observed grid cells to the total number of grid cells. Minimizing the maximum orbital circle number prioritizes satellites with smaller orbital circle numbers, allowing missions to be executed and completed earlier. Minimizing the sum of side-slip angles saves energy required for satellite attitude maneuvers. This indicates a rigid physical constraint: the side-swing angle allocated to each satellite cannot exceed the physical limits of its camera mechanism; otherwise, the planned commands cannot be executed. The above... This indicates logical coupling constraints, defining the conditions for mesh coverage, including: at least one satellite. One lap Selected ( ), and this satellite Actual yaw angle performed in this lap When you land on it, you can see the grid. The required feasible lateral swing angle range Inside.

[0038] In one embodiment, multiple opportunity tuples for any prospective observation satellite can be represented as a set of observation opportunities: Each opportunity tuple is represented as ,in, In order to observe satellites, The orbital circle number (the initial orbital circle numbers are all 0). The grid number is assigned to the observable area. The observation time (the corresponding time of each satellite position at the time of detection). The minimum lateral swing angle, The maximum side-swing angle is given. Further, an objective function is constructed based on the first optimization objective and the first constraint conditions. One or more target opportunity tuples are determined for each candidate observation satellite based on the output of the objective function, representing the relevant observation information of the regional grid that can be observed relatively well. Then, the observation plan for the observed satellite is determined based on these target opportunity tuples. The objective function can be expressed as: ,in, Indicates satellite In orbit number Side swing angle, ,express Whether the grid is observable; if observable, the value is 1. This indicates whether or not to use satellite. orbit number If used, it will be 1.

[0039] The technical solution provided in this embodiment determines the globally optimal observation plan for each prospective observation satellite under multiple constraints. Specifically, it generates opportunity tuples by combining key parameters such as orbit number and observation period to ensure that the plan conforms to the satellite's operational patterns. Each opportunity tuple is examined under optimization objectives and constraints to determine the observation plan for each prospective observation satellite. Minimizing the maximum orbit number prioritizes early arrival orbits, maximizing grid coverage to improve observation efficiency, and minimizing the absolute value of the side-slip angle reduces energy consumption for satellite attitude maneuvers. Furthermore, constraints such as the physical limit of the side-slip angle and grid coverage logic ensure that the observation plan meets the satellite's hardware capabilities and mission requirements, thereby improving execution reliability.

[0040] Please see Figure 3 In one implementation, based on the inter-satellite link diagram, the target transmission path corresponding to the target observation satellite is determined according to the following steps: S51: Based on the inter-satellite link diagram, dynamically construct a priority queue for the target observation satellite, and based on the priority queue and the second optimization target, determine the data transmission path of the target observation satellite, wherein the data transmission path includes multiple target nodes; S53: Verify each of the target nodes based on the second constraint. If the verification result indicates that the target node is valid, determine the target transmission path based on the data transmission path of the target observation satellite.

[0041] In this embodiment, the data transmission path is verified according to the second constraint to determine whether it meets the transmission performance requirements. The transmission performance is characterized by the validity of nodes. If it meets the requirements, the current data transmission path is retained; otherwise, the target node is re-explored through a priority queue to determine a new data transmission path for the target observation satellite. The data transmission paths of all target observation satellites corresponding to the aforementioned regional grid set are summarized to form the target transmission path for the target observation satellite. It should be noted that the aforementioned regional grid set corresponds to only one computing satellite, ensuring that each data transmission path passes through a specific computing satellite. The aforementioned priority queue can be understood as a data structure used during path exploration to determine which node to explore next.

[0042] In this embodiment, a priority queue for the target observation satellite is dynamically constructed based on the inter-satellite link graph and the second optimization objective. Specifically, a priority queue is established and initialized based on the nodes and edges represented by the inter-satellite link graph, i.e., the state of the starting node is added to the priority queue. The priority queue explores nodes with the target observation satellite as the starting node to find the shortest data transmission path from the target observation satellite to the ground station. By continuously obtaining the neighboring nodes of the current node from the inter-satellite link graph and determining the node state of each neighboring node, the neighboring nodes carrying the node state are added to the priority queue. It should be noted that the priority queue is not constructed as a fixed queue based on the entire inter-satellite link graph all at once, but is constructed dynamically. The initial queue only contains the starting node. By continuously obtaining the neighboring nodes of the current node from the graph, obtaining the state of the neighboring nodes and adding them to the queue, the priority queue is dynamically updated.

[0043] In this embodiment, the target nodes carry node timing information. Based on a second optimization objective, a priority queue is used to determine the data transmission path of the target observation satellite. Specifically, the nodes in the priority queue are dynamically sorted based on the second optimization objective. Multiple target nodes of the target observation satellite are determined based on the sorted priority queue. Preferably, the cumulative transmission time is used as the second optimization objective, and the adjacent nodes of the current node in the priority queue are sorted in ascending order according to their cumulative transmission time. Further, the adjacent edges of all target nodes are constructed, and the departure timing and transmission time of any adjacent edge are determined. The node timing of the target nodes is updated based on the departure timing and transmission time, and a data transmission path is constructed based on the updated target nodes.

[0044] In one embodiment, a node state dictionary is pre-constructed to store the reachability state of each node. This node state can be identified in the format ('Arrival Time', 'Cumulative Transmission Time', 'Survivor Node'), where the predecessor node corresponds to the arrival time and cumulative transmission time. Each node may have multiple predecessor nodes, i.e., multiple adjacent edges. For each adjacent edge, the node carries the arrival time and cumulative transmission time corresponding to the predecessor node of that adjacent edge. The cumulative transmission time represents the total transmission time from the starting point to the current node. Through this node state dictionary, the priority queue can obtain more detailed node states to perform path exploration and state updates based on the node states.

[0045] In one embodiment, path exploration is performed on each adjacent node of the priority queue according to its cumulative transmission time to achieve ascending order sorting of the priority queue. Specifically, for any proposed transmission path, the starting state is initialized to (start_time, 0, None), indicating that the starting point is reached at start_time, signifying that the photo taking is completed and data preparation is ready at that time. The cumulative transmission time is 0, and there is no predecessor node. Multiple adjacent nodes of the current node are obtained based on the inter-satellite link graph, and these adjacent nodes are sorted in ascending order according to their cumulative transmission time to determine the adjacent node with the shortest cumulative transmission time.

[0046] Furthermore, the node state (current_d, current_time, u, parent) with the shortest cumulative transmission time is popped from the priority queue, where current_d represents the cumulative transmission time to the current node, current_time represents the arrival time of the current node, u represents the ID of the current node, and parent represents the ID of the predecessor node to the current node. If the current node is the destination, the path is the shortest path, the final state is recorded, and the exploration is terminated. The node state of the current node is then validated, i.e., it is checked whether the current state is in the node state dictionary, and invalid states that have been optimized are excluded. If the current node is an intermediate node, the next target node of the current node is determined through the priority queue.

[0047] The current node needs a certain processing time to process the data. For each adjacent edge, its earliest departure time is the sum of its arrival time and the processing time interval, denoted as: ,in, Indicates the earliest departure time. Indicates arrival time. This indicates the node processing time. The adjacent edges mentioned above carry adjacent link information (v, a, b, bandwidth), where v is the adjacent node ID, a is the start time of link availability, b is the end time of link availability, and bandwidth is the link bandwidth. However, due to the time window constraint of inter-satellite links, the actual departure time is the later of the earliest departure time and the window opening time; that is, the departure sequence of the adjacent edges, and the actual departure time. Constraints.

[0048] Furthermore, the data transmission time from the current node to its neighboring nodes is calculated as follows: ,in, Data transmission time, expressed based on package size. and road speed limits The calculation is performed, with a minimum time limit of 1000ms (simulating the overhead of establishing a stable connection). Furthermore, the node state of the target node is updated, i.e., the node timing of the target node is updated, and the new node state is represented as... ,in, The data transmission path is constructed based on the updated target nodes, that is, the data transmission path is traced backward according to the status of the final destination node, so that the arrival and departure times of each node are accurately determined.

[0049] In this embodiment, the second optimization objective includes cumulative transmission time, and the second constraint includes transmission time constraint, link number constraint, no-loop path constraint, time window constraint, and computation constraint. The second optimization objective can be understood as minimizing the total time consumed by all nodes, i.e., the total time consumed in the entire process of "end of image capture → completion of computation → ground transmission back". The transmission time constraint can be understood as prohibiting satellite transmission during image capture / attitude adjustment. The link number constraint can be understood as requiring that the satellite / ground station only supports two links for transmission at a time. The no-loop path constraint can be understood as ensuring that the same transmission path should not contain duplicate nodes to avoid loops. The time window constraint can be understood as requiring that link establishment and data transmission must be completed within a single time window. The computation constraint can be understood as requiring that the computation start time be greater than or equal to the latest end time of all data to be computed being transmitted to the computation satellite.

[0050] The technical solution provided in this embodiment uses dynamic priority queue exploration and multi-constraint verification based on inter-satellite link graphs to determine the target transmission path corresponding to the target observation satellite. With minimizing cumulative transmission time as the optimization objective, the priority queue is explored in ascending order of cumulative time, which can quickly identify the path with the shortest overall time, significantly shortening the data cycle from observation to ground transmission and improving the timeliness of on-orbit data processing. Simultaneously, through link quantity constraints and time window constraints, the efficient utilization of link resources between the satellite and ground station is ensured, avoiding resource waste. Furthermore, through non-cyclic path constraints and computational constraints, the reliability of the transmission path and the timely execution of computational tasks are ensured, achieving efficient transmission path planning for the target observation area and improving the efficiency of on-orbit data processing.

[0051] In one implementation, the aforementioned action instruction sheet includes action instructions for each task node, with each task node carrying a task timing sequence. An action instruction sheet corresponding to the task input data is generated based on the target transmission path and observation information. Specifically, multiple task nodes in the target transmission path and the task timing sequence of each task node are obtained. For the task node representing the target observation satellite, action instructions for the target observation satellite are generated based on the observation information and task timing sequence. These action instructions typically include power-on instructions, attitude adjustment instructions, camera switch control instructions, and inter-satellite link creation / deletion instructions. For other task nodes, action instructions are generated based on the node type and task timing sequence. For action instructions representing the computational satellite, these typically include inter-satellite link creation / deletion instructions, satellite-to-ground link creation / deletion instructions, computational satellite data processing instructions, and computational satellite data transmission instructions. For action instructions representing the ground station, these typically include computation result download instructions. A corresponding timestamp is added to each action instruction based on the task timing sequence of each task node to ensure that the action instruction is executed at a specific time.

[0052] In one embodiment, the above action instruction is constructed based on an action instruction field table, which is shown in Table 1 below: Table 1 Action Instruction Fields For example, the action command '{"instructionCode":"power-on","sat_id":"SCS-01-12","triggerTime": 1756262488000,"equipments": ["ZS-ZJNX1-CPLD-01","LY-ZLR1-CPLD"]}' is a satellite power-on command, indicating that at the absolute timestamp of 1756262488000 (corresponding to UTC time), a power-on command is sent to the satellite SCS-01-12 to power on the specific devices ZS-ZJNX1-CPLD-01 and LY-ZLR1-CPLD on it.

[0053] For example, the action command '{"action":"isl-create","sat_id":"SCS-01-12","sat_id2":"SCS-01-11","uCommunicator":"Communicator#Laser#-X","vCommunicator":"Communicator#Laser#+X","triggerTime": 1756262567000}' is an inter-satellite link creation command, indicating that at the absolute timestamp of 1756262567000 (corresponding to UTC time), satellite SCS-01-12 is instructed to establish an inter-satellite link with satellite SCS-01-11. SCS-01-12 uses its laser communicator in the negative X-axis direction, and SCS-01-11 uses its laser communicator in the positive X-axis direction for docking.

[0054] The technical solution provided in this embodiment achieves precise control and optimization of multi-node collaborative tasks by generating detailed action instruction sheets, thereby improving the automation level, efficiency, and reliability of space computing tasks. Specifically, by generating action instruction sheets containing action instructions for all task nodes, collaborative optimization between task nodes is achieved, improving the efficiency and effectiveness of task execution. Furthermore, by adding timestamps to each action instruction, precise control of task node actions is achieved, ensuring timely task execution. This enables precise control and collaborative planning of multiple types of satellites, improves on-orbit data processing efficiency, and enhances the automation level and reliability of space computing tasks.

[0055] Please see Figure 4 This application also provides a space computing-based constellation mission collaborative planning device, the device comprising: The data preparation unit 100 is used to acquire task input data, which includes a set of observation satellites and a set of regional grids, and the set of observation satellites includes multiple pre-observation satellites. The observation planning unit 200 is used to determine the observation plan for each preparatory observation satellite based on the mission input data, and to determine the target observation satellite and the observation information of the target observation satellite for the regional grid set according to the observation plans of all preparatory observation satellites. The path planning unit 300 is used to acquire an inter-satellite link diagram with link time constraints, and determine the target transmission path corresponding to the target observation satellite based on the inter-satellite link diagram, wherein the target transmission path includes multiple task nodes. The instruction generation unit 400 is used to generate an action instruction sheet corresponding to the task input data based on the target transmission path and the observation information. The action instruction sheet is used to control the task node to execute a collaborative task.

[0056] in, The observation planning unit 200 is specifically used to acquire the observable areas of all prospective observation satellites, match the observable areas with the set of regional grids, determine the target observation plan corresponding to each regional grid based on the matching results, determine the prospective observation satellite, target orbit number, target lateral tilt angle, and target observation period corresponding to any regional grid according to the target observation plan corresponding to any regional grid, and determine the prospective observation satellite corresponding to the regional grid as the target observation satellite; and use the target orbit number, target lateral tilt angle, regional grid, and target observation period as the observation information of the target observation satellite.

[0057] The path planning unit 300 is specifically used to dynamically construct a priority queue of target observation satellites based on the inter-satellite link diagram, and determine the data transmission path of the target observation satellites based on the priority queues and the second optimization objective. The data transmission path includes multiple target nodes. Each target node is verified based on the second constraint condition. If the verification result indicates that the target node is valid, the target transmission path is determined based on the data transmission path of the target observation satellites.

[0058] The instruction generation unit 400 is specifically used to acquire multiple task nodes in the target transmission path and acquire the task timing of each task node. For the task node representing the target observation satellite, it generates action instructions for the target observation satellite based on the observation information and task timing of the target observation satellite. For other task nodes, it generates action instructions for the task nodes based on the node type and task timing of the task nodes.

[0059] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0060] In this application, a constellation mission collaborative planning device based on space computing is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit), a processor and memory that execute one or more software or fixed programs, or other devices that can provide the above functions.

[0061] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application, such as... Figure 5 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 5 Take a processor 10 as an example.

[0062] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0063] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0064] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0065] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0066] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0067] This application also provides a computer-readable storage medium. The methods described in this application can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code downloaded over a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the methods shown in the above embodiments are implemented.

[0068] The apparatus, module, or unit described in the above embodiments can be implemented by a computer chip or entity, or by a product having a certain function. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0069] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0070] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, or computer devices. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0071] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer devices according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0072] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0073] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0074] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0075] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0076] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of this application should be included within the scope of the claims of this application.

[0077] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A constellation mission collaborative planning method based on space computing, characterized in that, The method is applied to multiple mission nodes, including target observation satellites, computing satellites, and ground stations. The method includes: Acquire task input data, which includes a set of observation satellites and a set of regional grids, wherein the set of observation satellites includes multiple pre-observation satellites; Based on the task input data, the observation plan for each preparatory observation satellite is determined, and based on the observation plans of all preparatory observation satellites, the target observation satellites of the regional grid set and the observation information of the target observation satellites are determined. Obtain an inter-satellite link diagram with link time constraints, and determine the target transmission path corresponding to the target observation satellite based on the inter-satellite link diagram, wherein the target transmission path includes multiple task nodes; Based on the target transmission path and the observation information, an action instruction sheet corresponding to the task input data is generated. The action instruction sheet is used to control the task node to execute the collaborative task.

2. The method according to claim 1, characterized in that, The observation plan includes at least the observable area of ​​the proposed observation satellite, and the set of regional grids includes multiple regional grids; Based on the observation plans of all prospective observation satellites, the target observation satellites for the regional grid set and their observation information include: Obtain the observable regions of all the satellites to be observed, match the observable regions with the set of regional grids, and determine the target observation plan corresponding to each regional grid based on the matching results; Based on the target observation plan corresponding to any of the aforementioned regional grids, determine the pre-observation satellite, target orbit number, target lateral angle, and target observation period corresponding to the regional grid, and then determine the pre-observation satellite corresponding to the regional grid as the target observation satellite; The target orbit number, the target lateral tilt angle, the regional grid, and the target observation period are used as the observation information of the target observation satellite.

3. The method according to claim 1, characterized in that, The mission input data also includes a set of orbit numbers and a set of observation periods; the observation plan for each pre-observation satellite is determined based on the mission input data, including: Based on the task input data, multiple opportunity tuples are determined for any of the proposed observation satellites, wherein the opportunity tuples represent the side swing angle of the proposed observation satellite at a specified observation time under a specified orbit number in a specified region grid. Based on the opportunity tuples of each of the proposed observation satellites, an observation opportunity set for the proposed observation satellites is constructed, and based on the first optimization objective and the first constraint, the observation plan for each proposed observation satellite is determined in the observation opportunity set.

4. The method according to claim 3, characterized in that, The first optimization objective includes regional grid coverage, orbit number threshold, and absolute value of side-swing angle. The first constraint includes that the absolute value of the side-swing angle of each of the observed satellites is less than or equal to the side-swing angle threshold.

5. The method according to claim 1, characterized in that, The target transmission path corresponding to the target observation satellite is determined based on the inter-satellite link diagram, including: Based on the inter-satellite link diagram, a priority queue of the target observation satellite is dynamically constructed, and based on the priority queue and the second optimization target, the data transmission path of the target observation satellite is determined, wherein the data transmission path includes multiple target nodes; The target nodes are verified based on the second constraint. If the verification result indicates that the target node is valid, the target transmission path is determined based on the data transmission path of the target observation satellite.

6. The method according to claim 5, characterized in that, The target node carries node timing information; based on the priority queue and the second optimization target, the data transmission path of the target observation satellite is determined as follows: The priority queue is sorted based on the second optimization objective, and multiple target nodes of the target observation satellite are determined based on the sorted priority queue. Construct the adjacent edges for each of the target nodes, determine the departure time and transmission time of any of the adjacent edges, update the node timing of the target nodes based on the departure time and transmission time, and construct the data transmission path based on the updated target nodes.

7. The method according to claim 5 or 6, characterized in that, The second optimization objective includes cumulative transmission time, and the second constraint includes transmission time constraint, number of links constraint, no-loop path constraint, time window constraint, and computation constraint.

8. The method according to claim 1, characterized in that, The action instruction sheet includes action instructions for each of the task nodes, and the task nodes carry the task sequence. The action instruction sheet generated based on the target transmission path and the observation information corresponding to the task input data includes: Obtain multiple task nodes in the target transmission path, and obtain the task timing of each task node; For each mission node representing the target observation satellite, action commands for the target observation satellite are generated based on the observation information and mission sequence of the target observation satellite. And for the other task nodes, action instructions for the task nodes are generated according to the node type and task sequence of the task nodes.

9. A constellation mission collaborative planning device based on space computing, characterized in that, The device includes: A data preparation unit is used to acquire task input data, which includes a set of observation satellites and a set of regional grids, and the set of observation satellites includes multiple pre-observation satellites. The observation planning unit is used to determine the observation plan for each preparatory observation satellite based on the mission input data, and to determine the target observation satellite and the observation information of the target observation satellite for the regional grid set according to the observation plans of all preparatory observation satellites. The path planning unit is used to obtain an inter-satellite link diagram with link time constraints, and determine the target transmission path corresponding to the target observation satellite based on the inter-satellite link diagram, wherein the target transmission path includes multiple task nodes; The instruction generation unit is used to generate an action instruction sheet corresponding to the task input data based on the target transmission path and the observation information. The action instruction sheet is used to control multiple task nodes to perform collaborative tasks.

10. A computer device, characterized in that, include: A memory and a processor are communicatively connected, the memory stores computer instructions, and the processor executes the computer instructions to perform the constellation mission collaborative planning method based on space computing as described in any one of claims 1 to 8.

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