A method and system for optimizing multi-platform cooperative tasks at sea based on deep reinforcement learning

Abstract

The application discloses a kind of offshore multi-platform cooperative task optimization method and system based on deep reinforcement learning, it is related to resource optimization, artificial intelligence technology, including: generating the dynamic representation of task scene, and extracting task target and relevant features;Task target and relevant features are extracted based on using the processing of Transformer network, obtain the priority distribution of task;Pre-modeling multi-platform collaboration relationship as graph structure, and the collaboration efficiency between each platform is calculated using graph neural network;According to the priority distribution of task, the task allocation strategy is optimized using deep reinforcement learning, so that global optimization is achieved under multi-objective constraint;Based on the modeling result of graph neural network and the task allocation optimization result of deep reinforcement learning, generate multi-platform task allocation scheme.The method of the application can overcome the limitations of prior art, realize more efficient, more intelligent task allocation and optimization.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of resource optimization and artificial intelligence, and particularly relates to a method and system for optimizing multi-platform cooperative tasks at sea based on deep reinforcement learning. BACKGROUND

[0002] In modern maritime tasks, command and control systems need to coordinate multiple platforms (such as ships, drones and autonomous vessels) to complete complex task combinations such as reconnaissance, search and rescue, and strikes. This task allocation needs to make quick and dynamic decisions under limited resources to respond to complex and changing battlefield environments. However, existing cooperative task allocation techniques have the following shortcomings:

[0003] Poor dynamic adaptability in complex environments: Traditional task allocation is mostly based on pre-set rules or static optimization algorithms, lacking real-time response capability to dynamic environments such as target movement and weather changes.

[0004] Inadequate modeling of multi-platform collaboration: The communication and collaboration between multiple platforms are complex, and existing systems lack effective modeling mechanisms to fully utilize these relationships.

[0005] Not intelligent enough in task priority evaluation: The importance of tasks usually depends on artificial setting, lacking intelligent ability to dynamically balance battlefield needs and resource constraints.

[0006] Single optimization goal: Traditional algorithms usually only optimize a single goal (such as time or resources), making it difficult to balance multiple task demands.

[0007] The task allocation steps of existing rule-based and heuristic algorithm-based task allocation systems usually include:

[0008] Situation awareness and modeling: Collect environmental data from radars, drones and other sensors to form a situation map.

[0009] Task decomposition: Divide tasks into multiple subtasks according to task objectives.

[0010] Platform selection and allocation: Use rules or heuristic algorithms (such as genetic algorithms, ant colony algorithms) to allocate tasks to each platform.

[0011] Plan execution and adjustment: Adjust according to task completion and platform status.

[0012] System structure

[0013] Input module: Data collection, including environmental data and task requirements.

[0014] Task allocation module: Combine rule base with optimization algorithm to generate initial task allocation scheme.

[0015] Feedback and adjustment module: manual adjustment function based on real-time data.

[0016] Output module: generates task allocation results and instructs the execution platform.

[0017] There are the following shortcomings: Technical characteristics and limitations

[0018] Features: It realizes the preliminary automation of task allocation and has a certain degree of adaptability.

[0019] Lack of global optimization capabilities: Optimization algorithms are usually limited to local optimality and cannot balance multiple objectives.

[0020] Slow response speed: Lack of real-time response capabilities to dynamically changing environments.

[0021] Simple collaborative relationship modeling: Collaboration between platforms is usually based on static rules and is difficult to adapt to complex collaboration needs.

[0022] Task priority assessment relies on manual labor: the level of intelligence in the task allocation process is low. Summary of the Invention

[0023] The embodiment of the present application constructs a real-time, intelligent collaborative task allocation framework through the integration of deep reinforcement learning, graph neural network and Transformer model, which can overcome the limitations of existing technologies and achieve more efficient and intelligent task allocation and optimization.

[0024] The present application provides a method for optimizing maritime multi-platform collaborative tasks based on deep reinforcement learning, including:

[0025] Obtain input data from multiple sources;

[0026] Using deep learning models, a battlefield situation model is constructed based on the acquired multi-source input data to generate a dynamic representation of the mission scenario and extract mission objectives and related features;

[0027] The Transformer network is used to process the extracted task goals and related features, and the task priority is evaluated based on the context information to obtain the priority distribution of the tasks;

[0028] Pre-model the multi-platform collaboration relationship as a graph structure, and use a graph neural network to calculate the collaboration effectiveness between each platform, where the platforms are nodes and the collaboration relationships are edges in the graph structure;

[0029] Based on the priority distribution of tasks, deep reinforcement learning is used to optimize the task allocation strategy to achieve global optimization under multi-objective constraints;

[0030] Based on the modeling results of graph neural networks and the task allocation optimization results of deep reinforcement learning, a multi-platform task allocation solution is generated.

[0031] Optionally, the acquired multi-source input data includes task information, platform status, resource information, and environmental data;

[0032] The battlefield situation model constructed based on the acquired multi-source input data includes: mission scenario modeling and platform status modeling.

[0033] Optionally, the extracted task objectives and related features include:

[0034] Task characteristics, including task type, task time limit, and required resources; Task characteristics, including current status and historical task information; Environmental characteristics: including external environmental influencing factors;

[0035] The Transformer network is used to process the extracted task objectives and related features, and the task priority is evaluated based on the context information to obtain the task priority distribution including:

[0036] Converting task features, mission characteristics, and environmental characteristics into feature vectors;

[0037] The input feature vector is mapped to a high-dimensional continuous space using an embedding layer and input into a Transformer network. The feature vectors of the task, platform, and environment are encoded in the input embedding layer of the Transformer network, and the time dimension information is added through position encoding.

[0038] Using the multi-head self-attention mechanism, we model the relationship between tasks and platforms, identify key tasks, and the degree of coupling between key tasks and platforms.

[0039] According to the key tasks and the coupling degree between the key tasks and the platform, a priority score is generated through a nonlinear activation function.

[0040] Optionally, processing the extracted task objectives and related features using a Transformer network and evaluating the task priorities based on contextual information to obtain the task priority distribution also includes:

[0041] Utilize the self-attention mechanism to calculate the global relationship between tasks, platforms, and environments.

[0042] Optionally, the multi-platform collaboration relationship is pre-modeled as a graph structure, and the graph neural network is used to calculate the collaboration efficiency between each platform, including:

[0043] Node features include the required features of the task, and edge features include the potential matching relationship between the task and the platform;

[0044] The graph structure is modeled as follows:

[0045] Each task and platform is represented as a node according to the task priority score;

[0046] The edges between nodes are determined based on the key tasks and the degree of coupling between the key tasks and the platform as the edge weights;

[0047] Through the message passing mechanism, task nodes and platform nodes receive feature information from neighboring nodes and update their own status to meet the following requirements:

[0048]

[0049] in, Represents the feature vector of node v in layer l, ReLU() represents the activation function, W represents the trainable parameter, Aggregate() represents the aggregation of feature information from neighboring nodes to update the comprehensive features of the target node, e uv represents the characteristics of the edge, and N(v) represents the set of neighbor nodes.

[0050] Optionally, pre-modeling the multi-platform collaboration relationship as a graph structure and using a graph neural network to calculate the collaboration efficiency between platforms also includes:

[0051] The following results are output based on the constructed graph structure:

[0052] A collaboration matrix that prioritizes the matching of each platform to the tasks;

[0053] The optimized task and platform matching relationship diagram is used as a collaboration diagram.

[0054] Optionally, based on the priority distribution of tasks, deep reinforcement learning is used to optimize the task allocation strategy to achieve global optimality under multi-objective constraints, including:

[0055] The collaboration matrix, the collaboration graph and the environment data are defined as a state space to form a state vector; and the task allocation is defined as an action space;

[0056] Set the reward function to satisfy:

[0057] R = w1·task completion rate + w2·resource utilization rate + w3·priority completion rate - w4

[0058] Delay penalty

[0059] Among them, w1, w2, w3, and w4 are the corresponding weights respectively;

[0060] Use deep reinforcement learning algorithms to optimize strategies;

[0061] Output the optimal task allocation plan, which includes the allocation relationship between tasks and platforms and the execution order.

[0062] Optionally, based on the priority distribution of tasks, deep reinforcement learning is used to optimize the task allocation strategy to achieve global optimality under multi-objective constraints. The following training process is also included:

[0063] Initialize the state space and randomly generate the initial task allocation plan;

[0064] The agent selects actions through the policy network and updates the task allocation;

[0065] Based on the execution results, calculate the immediate reward and update the status;

[0066] Using reward and state transition data, optimize the policy network and value network parameters through gradient descent;

[0067] Iterate sampling and learning until the policy converges or reaches the expected performance.

[0068] An embodiment of the present application also proposes a marine multi-platform collaborative task optimization system based on deep reinforcement learning, including a processor and a memory, wherein a computer program is stored on the memory, and when the computer program is executed by the processor, the steps of the marine multi-platform collaborative task optimization method based on deep reinforcement learning as described above are implemented.

[0069] The embodiment of the present application constructs a real-time, intelligent collaborative task allocation framework through the integration of deep reinforcement learning, graph neural network and Transformer model, which can overcome the limitations of existing technologies.

[0070] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present application. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0072] Figure 1 This is a flowchart of a method for optimizing a multi-platform maritime collaborative task based on deep reinforcement learning according to an embodiment of the present application;

[0073] Figure 2This is a schematic diagram of the task priority evaluation process of the marine multi-platform collaborative task optimization method based on deep reinforcement learning in an embodiment of the present application;

[0074] Figure 3 This is a diagram illustrating the construction of a collaborative relationship diagram between optimization tasks and platforms in an offshore multi-platform collaborative task optimization method based on deep reinforcement learning according to an embodiment of the present application;

[0075] Figure 4 This is an illustration of task allocation using deep reinforcement learning in an offshore multi-platform collaborative task optimization method based on deep reinforcement learning in an embodiment of the present application. DETAILED DESCRIPTION

[0076] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0077] Existing technologies usually rely on preset rules or static optimization algorithms, which are difficult to handle emergencies in dynamic environments, such as changes in target locations and rapid changes in environmental conditions (such as sea conditions and weather). Collaboration between multiple platforms is usually based on fixed static models or simple logical rules, which makes it difficult to adapt to complex task collaboration requirements (such as dynamic division of labor and resource sharing). Traditional optimization methods usually only focus on a single goal (such as task completion time or resource consumption) and find it difficult to find a balance in multi-objective scenarios (such as task completion rate, response time and resource utilization). The setting of priorities in task allocation usually relies on manual evaluation, lacks dynamic adjustment capabilities, and cannot scientifically respond to rapid changes in the battlefield environment.

[0078] Based on this, the embodiment of the present application provides a method for optimizing maritime multi-platform collaborative tasks based on deep reinforcement learning, such as Figure 1 As shown, the following steps are included:

[0079] In step S101 , multi-source input data is acquired. In some embodiments, the acquired multi-source input data includes task information, platform status, resource information, and environment data.

[0080] In step S102, a deep learning model (such as a convolutional neural network, CNN) is used to build a battlefield situation model based on the acquired multi-source input data to generate a dynamic representation of the mission scenario and extract mission objectives and related features, including target location, platform capabilities, and resource status.

[0081] In step S103, the extracted task objectives and related features are processed using a Transformer network, and the task priorities are evaluated based on the context information to obtain the priority distribution of the tasks.

[0082] In step S104, the multi-platform collaborative relationship is modeled as a graph structure in advance, and the collaborative efficiency between each platform is calculated using the graph neural network GNN, where the platform is a node and the collaborative relationship is an edge in the graph structure.

[0083] In step S105, based on the priority distribution of tasks, Deep Reinforcement Learning (DRL) is used to optimize the task allocation strategy so as to achieve global optimization under multi-objective constraints;

[0084] In step S106, a multi-platform task allocation solution is generated based on the modeling results of the graph neural network and the task allocation optimization results of deep reinforcement learning.

[0085] In some embodiments, the battlefield situation model constructed based on the acquired multi-source input data includes: mission scenario modeling (such as target location, mission type, etc.) and platform status modeling (such as available resources, current mission load, etc.).

[0086] In some embodiments, as Figure 2 As shown in Figure 2, the extracted task objectives and related features include:

[0087] Task characteristics, including task type, task time limit, and required resources; task characteristics, including current status and historical task information; environmental characteristics: including external environmental influencing factors.

[0088] The Transformer network is used to process the extracted task objectives and related features, and the task priority is evaluated based on the context information to obtain the task priority distribution including:

[0089] Converting task features, mission characteristics, and environmental characteristics into feature vectors;

[0090] The input feature vector is mapped to a high-dimensional continuous space using an embedding layer and input into the Transformer network. The feature vectors of the task, platform, and environment are encoded in the input embedding layer of the Transformer network, and the time dimension information is added through positional encoding.

[0091] The multi-head self-attention mechanism is used to model the relationship between tasks and platforms, identify key tasks and the degree of coupling between key tasks and platforms.

[0092] Feedforward Neural Network (FFN): Generates priority scores through nonlinear activation functions based on key tasks and their coupling with the platform.

[0093] In some embodiments, processing is performed using a Transformer network based on the extracted task objectives and related features, and task priorities are evaluated based on contextual information to obtain a priority distribution of tasks, which also includes: using a self-attention mechanism to calculate the global relationship between tasks, platforms, and environments.

[0094] This application captures the global relationship between tasks, platforms, and environments through the self-attention mechanism. The parallel computing capability of Transformer ensures the efficiency of priority evaluation. It is suitable for dynamic scenarios and can be fine-tuned for different task scenarios to improve the accuracy of evaluation.

[0095] In some embodiments, as Figure 3 As shown in the figure, the multi-platform collaboration relationship is pre-modeled as a graph structure, and the graph neural network is used to calculate the collaboration efficiency between each platform, including:

[0096] Node features include the required features of the task (such as resource type, priority, completion time limit, etc.), and edge features include the potential matching relationship between the task and the platform (such as location, current resource amount, adaptability).

[0097] The graph structure is modeled as follows:

[0098] Each task and platform is represented as a node according to the task priority score.

[0099] The edges between nodes are determined based on the key tasks and the coupling degree between the key tasks and the platform as the edge weights.

[0100] Through the message passing mechanism, task nodes and platform nodes receive feature information from neighboring nodes and update their own status to meet the following requirements:

[0101]

[0102] in, Represents the feature vector of node v in layer l, ReLU() represents the activation function, W represents the trainable parameter, Aggregate() represents the aggregation of feature information from neighboring nodes to update the comprehensive features of the target node, e uv represents the characteristics of the edge, and N(v) represents the set of neighbor nodes.

[0103] In some embodiments, pre-modeling the multi-platform collaboration relationship as a graph structure and using a graph neural network to calculate the collaboration efficiency between the platforms also includes outputting the following results based on the constructed graph structure:

[0104] A collaboration matrix that prioritizes the matching of each platform to the tasks;

[0105] The optimized task and platform matching relationship diagram is used as a collaboration diagram.

[0106] Through dynamic updates of nodes and edges, it supports real-time changes in task and platform status. The GNN aggregation mechanism can capture the complex relationship between tasks and platforms, optimize collaboration efficiency from a global perspective, and the collaboration graph can intuitively display the matching relationship between tasks and platforms, supporting transparent system decision-making.

[0107] This application further utilizes Deep Reinforcement Learning (DRL) technology to dynamically optimize the task allocation scheme to ensure that the system performance is maximized under multi-task and multi-platform constraints. In some embodiments, Figure 4 As shown in Figure 1, based on the priority distribution of tasks, deep reinforcement learning is used to optimize the task allocation strategy to achieve the global optimum under multi-objective constraints, including:

[0108] The collaboration matrix, collaboration graph, and environmental data are used to define a state space, forming a state vector. Furthermore, the task assignments are defined as an action space. Specifically, the collaboration effectiveness matrix serves as the initial data for the state space, reflecting the degree of match between tasks and platforms. The collaboration graph provides constraints for the action space, for example, only allowing task-platform assignment combinations with high-weight edges.

[0109] Dynamic environment data: real-time task status (priority, remaining workload), platform status (remaining resources, current load), and environmental changes (such as sudden task changes, weather changes, etc.).

[0110] State Space Definition: Combine the output of collaborative modeling with task, platform, and environment data to form a complete state description. The state vector includes: task priority and remaining workload, platform remaining resources and availability, and the current task-platform match (derived from the collaborative effectiveness matrix).

[0111] Action Space Definition: Actions are defined as task assignments, such as "Task A is assigned to Platform 1." The collaboration graph is used to constrain the possible combinations in the action space, for example, allowing only highly compatible task-platform pairs as candidate actions. The priority of actions is weighted based on the scores in the collaboration effectiveness matrix.

[0112] Set the reward function to satisfy:

[0113] R = w1·task completion rate + w2·resource utilization rate + w3·priority completion rate - w4

[0114] Delay penalty

[0115] Where w1, w2, w3, and w4 are the corresponding weights. In this specific example, 1. Task Completion Rate: The more tasks completed, the higher the reward. Resource Utilization: The efficiency of platform resource utilization (avoiding resource waste or overload). Priority Matching: The higher the reward for completing high-priority tasks. Time Efficiency: The faster the task is completed, the higher the reward.

[0116] Use deep reinforcement learning algorithms (such as Proximal Policy Optimization, PPO, or Deep Q-Network, DQN) to perform policy optimization.

[0117] Policy Network: Builds a policy function π(a|s) based on a neural network, outputting the probability of taking action a in the current state s. Value Network: Estimates the state value V(s) or action value Q(s,a).

[0118] Output the optimal task allocation plan, which includes the assignment relationship between tasks and platforms, and the execution order. In this specific example, the output includes: The optimal task allocation plan includes the assignment relationship between tasks and platforms, and the execution order. System performance indicators include task completion rate, resource utilization, and allocation efficiency.

[0119] In some embodiments, based on the priority distribution of tasks, deep reinforcement learning is used to optimize the task allocation strategy so as to achieve global optimization under multi-objective constraints, which further includes the following training process:

[0120] Initialize the state space and randomly generate the initial task allocation plan;

[0121] The agent selects actions through the policy network and updates the task allocation;

[0122] Based on the execution results, calculate the immediate reward and update the status;

[0123] Using reward and state transition data, optimize the policy network and value network parameters through gradient descent;

[0124] Iterate sampling and learning until the policy converges or reaches the expected performance.

[0125] The embodiment of the present application is based on a reinforcement learning algorithm that can dynamically adapt to changes in tasks and environment without the need for preset rules. Through long-term reward optimization, it obtains an allocation strategy close to the global optimality. Combined with parallel computing, it adapts to complex task scenarios and quickly generates allocation plans.

[0126] The application utilizes deep reinforcement learning (DRL) and a Transformer model to realize real-time optimization of task allocation strategies and dynamic intelligent evaluation of task priorities, overcoming the problems of slow response and unscientific priority evaluation in the prior art in a dynamic environment. Through real-time collection and processing of multi-source data, the system can quickly respond to changes in the battlefield environment, greatly improving the efficiency and flexibility of task allocation.

[0127] The multi-platform cooperation relationship is modeled by using a graph neural network (GNN), fully tapping the cooperation potential between platforms, and optimizing resource allocation, solving the technical problems of low multi-platform cooperation efficiency and insufficient resource utilization in the prior art. At the same time, the multi-objective optimization capability of the system balances the task completion rate, resource consumption and response time, improving the global optimization effect of the allocation strategy.

[0128] The embodiment of the application also provides a multi-platform cooperative task optimization system based on deep reinforcement learning at sea, including a processor and a memory, the memory storing a computer program, and the computer program being executed by the processor to realize the steps of the multi-platform cooperative task optimization method based on deep reinforcement learning at sea as described above.

[0129] It should be noted that in the embodiments of the application, the terms "comprise", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of another identical element in the process, method, article or device including the element.

[0130] The above-mentioned serial numbers of the embodiments of the application are only for description, and do not represent the advantages or disadvantages of the embodiments.

[0131] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be realized by means of software and necessary general hardware platform, of course, they can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the application can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes a plurality of instructions for making a terminal (which can be a mobile phone, computer, server, air conditioner or network device) execute the methods described in the embodiments of the application.

[0132] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms without departing from the purpose of this application and the scope of protection of the claims, all of which are protected by this application.

Claims

1. A method for optimizing maritime multi-platform collaborative tasks based on deep reinforcement learning, characterized in that: include: Obtain input data from multiple sources; Using deep learning models, a battlefield situation model is constructed based on the acquired multi-source input data to generate a dynamic representation of the mission scenario and extract mission objectives and related features; The Transformer network is used to process the extracted task goals and related features, and the task priority is evaluated based on the context information to obtain the priority distribution of the tasks; Pre-model the multi-platform collaboration relationship as a graph structure, and use a graph neural network to calculate the collaboration effectiveness between each platform, where the platforms are nodes and the collaboration relationships are edges in the graph structure; Based on the priority distribution of tasks, deep reinforcement learning is used to optimize the task allocation strategy to achieve global optimization under multi-objective constraints; Generate a multi-platform task allocation solution based on the modeling results of graph neural networks and the task allocation optimization results of deep reinforcement learning; The multi-source input data obtained include task information, platform status, resource information, and environmental data; The battlefield situation model constructed based on the acquired multi-source input data includes: mission scenario modeling and platform status modeling; The extracted task objectives and related features include: Task characteristics, including task type, task time limit, and required resources; task characteristics, including current status and historical task information; environmental characteristics: including external environmental influencing factors.

2. The method for optimizing maritime multi-platform collaborative tasks based on deep reinforcement learning according to claim 1, characterized in that: The extracted task objectives and related features also include: The Transformer network is used to process the extracted task objectives and related features, and the task priority is evaluated based on the context information to obtain the task priority distribution including: Converting task features, mission characteristics, and environmental characteristics into feature vectors; The input feature vector is mapped to a high-dimensional continuous space using an embedding layer and input into a Transformer network. The feature vectors of the task, platform, and environment are encoded in the input embedding layer of the Transformer network, and the time dimension information is added through position encoding. Using the multi-head self-attention mechanism, we model the relationship between tasks and platforms, identify key tasks, and the degree of coupling between key tasks and platforms. According to the key tasks and their coupling degree with the platform, priority scores are generated through a nonlinear activation function.

3. The method for optimizing maritime multi-platform collaborative tasks based on deep reinforcement learning according to claim 2, characterized in that: The extracted task objectives and related features are processed using the Transformer network, and the task priorities are evaluated based on the context information to obtain the task priority distribution. Utilize the self-attention mechanism to calculate the global relationship between tasks, platforms, and environments.

4. The method for optimizing maritime multi-platform collaborative tasks based on deep reinforcement learning according to claim 2, wherein: Model the multi-platform collaboration relationship as a graph structure in advance, and use graph neural networks to calculate the collaboration efficiency between platforms include: Node features include the demand features of the task, and edge features include the potential matching relationship between the task and the platform; The graph structure is modeled as follows: Each task and platform is represented as a node according to the task priority score; The edges between nodes are determined based on the key tasks and the degree of coupling between the key tasks and the platform as the edge weights; Through the message passing mechanism, task nodes and platform nodes receive feature information from neighboring nodes and update their own status to meet the following requirements: in, Representation node In the The feature vector of the layer, represents the activation function, represents the trainable parameters, Indicates that feature information is aggregated from neighboring nodes to update the comprehensive features of the target node. Represents the characteristics of the edge, Represents a set of neighbor nodes.

5. The method for optimizing maritime multi-platform collaborative tasks based on deep reinforcement learning according to claim 4, characterized in that: Modeling multi-platform collaboration relationships as a graph structure in advance and using graph neural networks to calculate the collaboration efficiency between platforms also includes: The following results are output based on the constructed graph structure: A collaboration matrix that prioritizes the matching of each platform to the tasks; The optimized task and platform matching relationship diagram is used as a collaboration diagram.

6. The method for optimizing maritime multi-platform collaborative tasks based on deep reinforcement learning according to claim 5, characterized in that: Based on the priority distribution of tasks, deep reinforcement learning is used to optimize the task allocation strategy to achieve global optimization under multi-objective constraints, including: The collaboration matrix, the collaboration graph and the environment data are defined as a state space to form a state vector; and the task allocation is defined as an action space; Set the reward function to satisfy: in, 、 、 、 are the corresponding weights respectively; Use deep reinforcement learning algorithms to optimize strategies; Output the optimal task allocation plan, which includes the allocation relationship between tasks and platforms and the execution order.

7. The method for optimizing maritime multi-platform collaborative tasks based on deep reinforcement learning according to claim 6, characterized in that: Based on the priority distribution of tasks, deep reinforcement learning is used to optimize the task allocation strategy to achieve global optimization under multi-objective constraints. The training process also includes the following: Initialize the state space and randomly generate the initial task allocation plan; The agent selects actions through the policy network and updates the task allocation; Based on the execution results, calculate the immediate reward and update the status; Using reward and state transition data, optimize the policy network and value network parameters through gradient descent; Iterate sampling and learning until the policy converges or reaches the expected performance.

8. A marine multi-platform collaborative task optimization system based on deep reinforcement learning, characterized in that: The method comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the steps of the method for optimizing marine multi-platform collaborative tasks based on deep reinforcement learning are implemented as described in any one of claims 1 to 7.

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