Multi-satellite collaborative observation task distributed planning method for emergency response

Abstract

The invention relates to an emergency response-oriented multi-satellite collaborative observation task distributed planning method, which comprises the following steps of: modeling a plurality of satellites into an intelligent agent with an autonomous decision-making capability, and realizing task broadcasting, bidding and confirmation between a task manager and an executor by adopting an improved contract network protocol. By constructing a multi-factor dynamic evaluation function and comprehensively considering the task emergency degree, the observation feasibility, the resource state and the task conflict probability, intelligent bidding distribution of the observation sub-tasks is realized. A task conflict game diagram mechanism is introduced, the problem of multi-satellite bidding conflict is effectively solved, and the task scheduling fairness and the resource utilization rate are improved. The method has communication topology sensing and local task re-planning functions, supports adaptive recovery under the condition of node failure or communication interruption, and improves the system robustness and the task completion rate. The method has the advantages of being fast in task response, high in scheduling efficiency, high in distributed autonomy and the like, and is suitable for collaborative planning requirements of a multi-satellite system in emergency response.

Description

Technical Field

[0001] This application relates to the field of space mission planning, specifically to a distributed planning method for multi-satellite collaborative observation missions oriented towards emergency response. Background Technology

[0002] With the rapid development of low-Earth orbit small satellite constellations, traditional centralized mission planning and scheduling architectures suffer from resource bottlenecks, communication delays, and poor system robustness. Therefore, distributed mission planning has become a research hotspot. Contract net protocols, as a classic distributed mission allocation mechanism, have been extensively studied, but they suffer from high communication overhead, numerous mission conflicts, and low response efficiency in multi-satellite collaborative missions. Existing methods fail to effectively integrate information such as onboard resource status, mission priorities, and timing constraints, and lack adaptive optimization mechanisms for dynamically changing environments. Summary of the Invention

[0003] To overcome at least one deficiency in the prior art, this application provides a distributed planning method for multi-satellite collaborative observation missions oriented towards emergency response.

[0004] Firstly, a distributed planning method for multi-satellite collaborative observation missions oriented towards emergency response is provided, including: Step 1: Model each satellite as an intelligent agent, with each satellite acting as an independent agent; construct an adjacency communication topology graph, where each node is a satellite, and any two satellites with a communication relationship are connected by an edge. Step 2: When any satellite senses a new task, the satellite that senses the new task acts as the task manager and decomposes the task into multiple sub-tasks according to the task observation area, observation requirements, and response time limit. The satellite then broadcasts the task requests to neighboring satellites in the adjacency communication topology in the form of a contract network protocol. Each sub-task includes the target area, observation requirements, response time limit, and task priority. Step 3: Each satellite receiving the mission request performs a multi-factor comprehensive evaluation of the sub-mission based on its local status to obtain an evaluation value; based on the evaluation value, it determines the eligible sub-missions and sends a bidding request to the mission manager for the eligible sub-missions. Step 4: The task manager accepts the bidding requests and determines whether there are multiple satellites bidding for the same sub-task or one satellite bidding for multiple sub-tasks. If not, the sub-tasks are assigned to the corresponding satellites that sent the bidding requests. If so, a task conflict game graph is constructed and optimized using the maximum independent subgraph algorithm or graph game model to obtain the optimal task allocation scheme. Based on the optimal task allocation scheme, the sub-tasks are assigned to the corresponding satellites. Step 5: After the sub-tasks are assigned, each satellite conducts remote sensing observations and transmits the execution status back via inter-satellite communication.

[0005] In one embodiment, the task is decomposed into multiple sub-tasks based on the task location, observation requirements, and response time limit, including: Based on the task observation area, a regular grid is used to divide the task observation area into multiple sub-regions, each sub-region corresponding to a sub-task; each sub-task includes center coordinates, target area, task priority, observation requirements, and response time limit.

[0006] In one embodiment, the subtask is evaluated using a multi-factor comprehensive assessment based on the local state to obtain an evaluation value, using the following formula:

[0007] in, This is the evaluation value for the subtask. As a task priority, For target area visibility, For the current energy availability of the satellite, The degree of conflict between the subtask and the current task plan. All of these are weight parameters.

[0008] In one embodiment, the method further includes: If a satellite is unable to complete a sub-task while performing it, a local task replanning mechanism will be triggered. This mechanism includes: Dynamically sense changes in communication topology and reconstruct the adjacent communication topology graph; For unfinished subtasks, broadcast the task to the neighboring satellites in the reconstructed adjacency communication topology; execute steps 3 to 5.

[0009] Compared to existing technologies, this application offers the following advantages: The distributed planning method for multi-satellite collaborative observation missions in this application, geared towards emergency response, models multiple satellites as intelligent agents with autonomous decision-making capabilities. An improved contract network protocol is used to achieve mission broadcasting, bidding, and confirmation between mission managers and executors. By constructing a multi-factor dynamic evaluation function, considering mission urgency, observation feasibility, resource status, and mission conflict probability, intelligent bidding allocation of observation sub-tasks is achieved. The introduction of a mission conflict game graph mechanism effectively resolves multi-satellite bidding conflicts, improving mission scheduling fairness and resource utilization. This application possesses communication topology awareness and local mission replanning capabilities, supporting adaptive recovery in the event of node failure or communication interruption, thus enhancing system robustness and mission completion rate. This application boasts advantages such as fast mission response, high scheduling efficiency, and strong distributed autonomy, making it suitable for the collaborative planning needs of multi-satellite systems in emergency response. Attached Figure Description

[0010] This application can be better understood by referring to the description given below in conjunction with the accompanying drawings, which, together with the detailed description below, are incorporated in and form part of this specification. In the drawings: Figure 1 A flowchart of a distributed planning method for multi-satellite collaborative observation missions oriented towards emergency response is shown. Figure 2 A schematic diagram of a multi-satellite collaborative observation system is shown. Figure 3 The task conflict game diagram is shown. Detailed Implementation

[0011] Exemplary embodiments of the present application will be described below with reference to the accompanying drawings. For clarity and brevity, not all features of the actual embodiments are described in the specification. However, it should be understood that many embodiment-specific decisions can be made in the development of any such actual embodiment to achieve the developer’s specific objectives, and these decisions may vary as the embodiments differ.

[0012] It should also be noted that, in order to avoid obscuring this application with unnecessary details, only the device structure closely related to the solution of this application is shown in the accompanying drawings, while other details that are not closely related to this application are omitted.

[0013] It should be understood that this application is not limited to the described embodiments by virtue of the following description with reference to the accompanying drawings. In this document, embodiments may be combined with each other, features may be substituted or borrowed between different embodiments, and one or more features may be omitted in one embodiment, where feasible.

[0014] This application provides a distributed planning method for multi-satellite collaborative observation missions oriented towards emergency response. Figure 1 A flowchart illustrating a distributed planning method for multi-satellite collaborative observation missions geared towards emergency response is shown. (See [link]). Figure 1 The method mainly includes the following steps: Step 1: Model each satellite as an intelligent agent, with each satellite acting as an independent agent; construct an adjacency communication topology graph, where each node is a satellite, and any two satellites with a communication relationship are connected by an edge.

[0015] Figure 2A schematic diagram of a multi-satellite collaborative observation system is shown. Before the mission begins, the system performs intelligent agent modeling on each in-orbit satellite, endowing it with perception, communication, computing, and autonomous decision-making capabilities. Each satellite, as an independent agent, can dynamically assume the role of mission manager or mission executor within the system based on its resource status and communication conditions. Each agent can proactively acquire its own current attitude and orbit information, remaining energy, imaging payload status, mission buffer, and other local states, and obtain the status of neighboring satellites through inter-satellite communication links, achieving state sharing. The system constructs an adjacency communication topology graph, updates the candidate mission pool for each satellite agent, and forms a dynamic "virtual mission collaboration network," laying the structural foundation for subsequent mission collaboration.

[0016] Step 2: When any satellite senses a new task, the satellite that senses the new task acts as the task manager. Based on the task's observation area, observation requirements, and response time limit, it decomposes the task into multiple sub-tasks. These sub-tasks are then broadcast to neighboring satellites in the adjacency communication topology using the Contract Net Protocol (CNP) format. Each sub-task includes the target area, observation requirements, response time limit, and task priority. Here, observation requirements include the required observation resolution and band, and the response time limit refers to the response deadline.

[0017] Here, a new task could be, for example, a new forest fire observation task. The task is broken down into multiple sub-tasks, including: dividing the observation area into multiple sub-regions using a regular grid, with each sub-region corresponding to a sub-task; each sub-task includes center coordinates, target area, task priority, observation requirements, and response time limit.

[0018] Step 3: Each satellite receiving the mission request performs a multi-factor comprehensive evaluation of the sub-mission based on its local status to obtain an evaluation value; based on the evaluation value, it determines the eligible sub-missions and sends bidding requests to the mission manager for the eligible sub-missions.

[0019] Specifically, the sub-tasks are evaluated using a multi-factor comprehensive assessment to obtain the evaluation value, which is calculated using the following formula:

[0020] in, This is the evaluation value for the subtask. As a task priority, For target area visibility, For the current energy availability of the satellite, The degree of conflict between the subtask and the current task plan. All of these are weight parameters.

[0021] Here, target area visibility refers to the overlap ratio between the visible range of the satellite and the target area of ​​the sub-task. For example, if the overlap ratio of the overlapping area is 80%, the target area visibility is 0.8.

[0022] The current energy availability of a satellite depends primarily on the design and operating status of its power system, such as the proportion of energy that the satellite can currently use.

[0023] The degree of conflict between subtasks and the current task plan is mainly reflected in resource allocation and time scheduling. The assessment methods for the degree of conflict may include resource assessment and monitoring, time swapping and scheduling optimization, etc., which are determined according to the actual situation and are not specifically limited here.

[0024] Each factor is a normalized index, and the weight parameters are... It can be adaptively adjusted according to task attributes.

[0025] Step 4: The task manager accepts the bidding requests and determines whether there are multiple satellites bidding for the same sub-task or one satellite bidding for multiple sub-tasks. If not, the sub-tasks are assigned to the corresponding satellites that sent the bidding requests. If so, a task conflict game graph is constructed, and the maximum independent subgraph algorithm or graph game model is used to optimize and solve the problem to obtain the optimal task allocation scheme. Based on the optimal task allocation scheme, the sub-tasks are assigned to the corresponding satellites. Figure 3 The task conflict game diagram is shown.

[0026] The task conflict game graph uses all bidding subtasks as vertices. If the time windows of two subtasks overlap or their required satellite resources (such as energy, storage, and observation load) conflict, an undirected edge is established between the corresponding nodes. Based on this graph structure, the maximum independent subgraph algorithm or graph game model is used for optimization. The goal is to find a subset of tasks such that any two subtasks within the subset do not conflict and have the optimal overall evaluation value, thereby maximizing resource utilization and minimizing the conflict rate. Finally, the most suitable satellite combination scheme for executing the task is selected and enters the task confirmation stage. The task manager completes the task allocation based on the evaluation results and the graph game output, and issues confirmation instructions to the selected satellites.

[0027] Step 5: After the sub-tasks are assigned, each satellite conducts remote sensing observations and transmits the execution status back via inter-satellite communication.

[0028] Furthermore, when a satellite is executing a sub-task, if it is unable to complete the sub-task due to insufficient energy, communication interruption, or execution failure, a local task replanning mechanism will be triggered. This local task replanning mechanism includes: Dynamically sense changes in communication topology and reconstruct the adjacent communication topology graph; For unfinished subtasks, broadcast task requests to neighboring satellites in the reconstructed adjacency communication topology, limited to the local constellation range, i.e., without introducing new satellites; execute steps 3 to 5, thereby ensuring that the system has fault tolerance, self-recovery, and autonomous collaborative capabilities, significantly improving overall robustness and reliability.

[0029] In summary, this application has the following technical effects: This application models multiple satellites as intelligent agents with autonomous decision-making capabilities, employing an improved contract network protocol to achieve task broadcasting, bidding, and confirmation between task managers and executors. By constructing a multi-factor dynamic evaluation function, comprehensively considering task urgency, observation feasibility, resource status, and task conflict probability, intelligent bidding allocation of observation subtasks is achieved. A task conflict game graph mechanism is introduced to effectively resolve multi-satellite bidding conflicts, improving task scheduling fairness and resource utilization. This application possesses communication topology awareness and local task replanning capabilities, supporting adaptive recovery in the event of node failure or communication interruption, enhancing system robustness and task completion rate. This application features fast task response, high scheduling efficiency, and strong distributed autonomy, making it suitable for collaborative planning needs in multi-satellite systems during emergency response.

[0030] The above descriptions are merely various embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A distributed planning method for multi-satellite collaborative observation missions oriented towards emergency response, characterized in that, include: Step 1: Model each satellite as an intelligent agent, with each satellite acting as an independent agent; construct an adjacency communication topology graph, where each node in the adjacency communication topology graph is a satellite, and any two satellites with a communication relationship are connected by an edge. Step 2: When any satellite senses a new task, the satellite that senses the new task acts as the task manager and decomposes the task into multiple sub-tasks according to the task observation area, observation requirements, and response time limit. The satellite then broadcasts the task requests to neighboring satellites in the adjacency communication topology in the form of a contract network protocol. Each sub-task includes the target area, observation requirements, response time limit, and task priority. Step 3: Each satellite receiving the mission request performs a multi-factor comprehensive evaluation of the sub-mission based on its local status to obtain an evaluation value; based on the evaluation value, it determines the eligible sub-missions and sends bidding requests to the mission manager for the eligible sub-missions. Step 4: The task manager accepts the bidding request and determines whether there are multiple satellites bidding for the same sub-task at the same time or one satellite bidding for multiple sub-tasks. If not, the sub-tasks are assigned to the corresponding satellites that sent the bidding requests. If so, a task conflict game graph is constructed and optimized using the maximum independent subgraph algorithm or graph game model to obtain the optimal task allocation scheme. Based on the optimal task allocation scheme, the sub-tasks are assigned to the corresponding satellites. Step 5: After the sub-tasks are assigned, each satellite conducts remote sensing observations and transmits the execution status back via inter-satellite communication.

2. The method as described in claim 1, characterized in that, in, Based on the task location, observation requirements, and response timeframe, the task is broken down into multiple sub-tasks, including: Based on the task observation area, a regular grid is used to divide the task observation area into multiple sub-regions, each sub-region corresponding to a sub-task; each sub-task includes center coordinates, target area, task priority, observation requirements, and response time limit.

3. The method as described in claim 1, characterized in that, in, Based on the local status, a multi-factor comprehensive evaluation of the subtasks is performed to obtain the evaluation value, using the following formula: in, This is the evaluation value for the sub-task. As a task priority, For target area visibility, For the current energy availability of the satellite, The degree of conflict between the subtask and the current task plan. All of these are weight parameters.

4. The method as described in claim 1, characterized in that, The method further includes: If a satellite is unable to complete a sub-task while performing it, a local task replanning mechanism will be triggered; the local task replanning mechanism includes: Dynamically sense changes in communication topology and reconstruct the adjacent communication topology graph; For unfinished subtasks, broadcast the task to the neighboring satellites in the reconstructed adjacency communication topology; execute steps 3 to 5.

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