Distributed intelligent air and space defense combat adaptive system architecture

Abstract

The invention discloses a distributed intelligent space defense combat adaptive system architecture, which relates to the technical field of command and control and comprises a distributed node deployment and networking system, a distributed situation intelligent sensing system, an adaptive decision system and a joint global command and control system. According to the distributed intelligent air and space defense combat adaptive system architecture provided by the invention, resources are effectively integrated through the distributed node deployment and networking system, the adaptive decision making system and the distributed situation awareness system, so that a highly integrated combat system is formed; through combination with the global command control system, cross-domain and cross-platform command control is realized, and the collaboration and consistency of combat actions are ensured.

Description

Technical Field

[0001] The present invention relates to the field of command and control technology, and in particular to a distributed intelligent air and space defense combat adaptive system architecture. Background Art

[0002] With the rapid evolution of modern warfare, the aerospace sector is becoming a key battlefield for military competition among countries. The traditional aerospace defense system faces an increasingly complex and changing threat environment, including the rapid development of new weaponry and equipment such as hypersonic weapons, stealth technology, electronic countermeasures, and unmanned combat platforms. The widespread application of these technologies has posed severe challenges to the existing defense system, while also exposing many deficiencies in the traditional aerospace defense system in terms of deployment mode, decision-making efficiency, and information processing capabilities.

[0003] Traditional aerospace defense systems are mostly designed based on a centralized architecture that is highly dependent on the command and control of the central node. Once the central node is attacked by the enemy, such as being hit by precision-guided weapons or subjected to high-intensity electronic interference, the entire defense system is very likely to be paralyzed. For example, in some local conflicts, when the enemy uses targeted electronic warfare means to interfere with the central command system, the air defense system in the relevant area will experience command confusion and information transmission interruption, which greatly reduces the defense effectiveness and makes it impossible to effectively intercept and defend against incoming targets.

[0004] Although there have been breakthroughs in the application of emerging technologies such as artificial intelligence (AI), big data, and 5G / 6G communications in the military field in recent years, there are still many challenges in effectively integrating these technologies into the aerospace defense system: Difficulty in integrating technologies: There is a lack of effective integration architecture between AI, sensor networks, communication technologies, and combat command systems. Increased system complexity: The introduction of multiple technologies increases the complexity of system design and maintenance, which may lead to compatibility and reliability issues. Insufficient adaptive capabilities: Although some systems have introduced intelligent functions, there are still obvious shortcomings in dynamic reorganization and adaptive adjustment.

[0005] To sum up, existing technologies have shown many limitations when facing modern aerospace threats. There is an urgent need for a distributed intelligent aerospace defense combat adaptive system architecture to improve defense effectiveness, flexibility and collaborative combat capabilities, and provide more efficient solutions for increasingly complex battlefield environments. Summary of the invention

[0006] The purpose of the present invention is to provide a distributed intelligent air and space defense combat adaptive system architecture to solve the problems existing in the prior art of lack of effective integrated architecture among sensor networks, communication technologies and combat command systems, increased system complexity and insufficient adaptive capabilities.

[0007] To achieve the above-mentioned purpose, the present invention provides a distributed intelligent air and space defense combat adaptive system architecture, including a distributed node deployment and networking system, a distributed situation intelligent perception system, an adaptive decision-making system and a joint global command and control system; The distributed node deployment and networking system includes an intelligent node network module, a node self-organizing module, and a data transmission module; The distributed situation intelligent perception system includes a situation prediction module, a situation information integration and processing module, and a situation information real-time update and distribution module; The adaptive decision-making system includes a threat assessment and classification module, a dynamic optimization resource allocation module, and a dynamic reconstruction execution module: The joint global command and control system integrates the resources of distributed node deployment and networking systems, distributed situational intelligent perception systems and adaptive decision-making systems to integrate the combat system; it adopts time-space calibration algorithms and service grid scheduling frameworks to ensure information synchronization and resource coordination, while combining quantum key distribution and blockchain technology to ensure communication security and identity authentication.

[0008] Preferably, the intelligent node network module is composed of multiple types of intelligent nodes, including but not limited to drones, unmanned vehicles, satellites, radars and missile systems. Each node is provided with different functions according to its physical characteristics and combat requirements. Drones and unmanned vehicles are responsible for ground reconnaissance and strike missions, satellites and radars are responsible for aerospace reconnaissance and early warning missions, and missile systems are responsible for intercepting and destroying enemy targets.

[0009] Preferably, the node self-organizing module adopts the improved AODV (On-Demand Distance Vector) protocol, which enables nodes to achieve low-latency and high-stability communication under different network topologies; at the same time, spectrum hopping and adaptive channel allocation technology are introduced to improve the reliability of the network in complex electromagnetic environments; in addition, each intelligent node is equipped with a low-power processing unit and a self-organizing network protocol stack, which can dynamically reorganize the network topology when the node fails or is interfered with, thereby ensuring network connectivity.

[0010] Preferably, the data transmission module includes an intelligent spectrum resource utilization submodule, an efficient data transmission protocol submodule, and a data compression and preprocessing submodule; Intelligent spectrum resource utilization submodule: Using cognitive radio technology, it can perceive the surrounding electromagnetic environment in real time and intelligently select idle spectrum resources for communication, effectively improving spectrum utilization and anti-interference capabilities. At the same time, by equipping with spectrum analysis modules and spectrum prediction algorithms, combined with deep reinforcement learning models, intelligent nodes can predict spectrum usage trends in real time and avoid interference bands in advance. Efficient data transmission protocol submodule: Based on a low-latency multi-hop communication solution, combined with 6G millimeter wave communication technology, it realizes high-bandwidth, low-power point-to-point and multi-node transmission; Data compression and preprocessing submodule: Utilizes a data compression algorithm based on a variational autoencoder (VAE) to retain target features to the greatest extent possible and reduce the computational pressure of the central node.

[0011] Preferably, the situation prediction module uses historical combat data and real-time perception data to train the GAN model to predict the target trajectory and plan the deployment of defense resources in advance; by designing a multi-source data enhancement strategy, the multi-source data and historical trajectories are fused and input into the GAN model to improve the generalization ability of the model in complex scenarios.

[0012] Preferably, the situation information integration and processing module adopts a Transformer network based on feature-level fusion to integrate multi-source information according to temporal correlation and spatial constraints; at the same time, a hybrid model based on the analytic hierarchy process (AHP) and the Bayesian network is introduced to perform a situation assessment algorithm to quantitatively analyze and evaluate the battlefield situation and provide support for decision-making.

[0013] Preferably, the situation information real-time update and distribution module is based on the state estimation model of Kalman filtering and multi-sensor fusion, which dynamically corrects the errors in the situation map to ensure the accuracy of the situation information; at the same time, an information distribution protocol is designed to distribute key situation information with low latency through a high-priority queue management mechanism, and combined with network coding technology, it ensures that the situation information can be efficiently and reliably transmitted to each combat unit.

[0014] Preferably, the threat assessment and classification module includes a threat level classification standard submodule and a deep learning model assisted decision-making submodule; Threat level classification standard submodule: By building a dynamic weight adjustment mechanism, the weight of threat assessment indicators is dynamically adjusted according to the real-time situation changes of target type, speed, heading and distance. At the same time, combined with the spatiotemporal prediction model, enemy targets that pose a threat to core targets are evaluated first; Deep learning model assisted decision-making sub-module: Through the target feature extraction model based on the Transformer architecture, it evaluates threats under unknown or extreme conditions, improves classification capabilities in complex battlefield environments, and increases small sample learning capabilities.

[0015] Preferably, the dynamic optimization resource allocation module includes an intelligent game algorithm submodule and a real-time adjustment strategy submodule; Intelligent game algorithm submodule: Based on deep reinforcement learning, the Monte Carlo Tree Search algorithm (MCTS) is introduced to simulate various action plans taken by the enemy and generate the optimal solution for resource allocation; Real-time adjustment strategy submodule: Comprehensively optimize the multi-objective problems of interception point selection and firepower coverage range through joint optimization algorithms (such as NSGA-II).

[0016] Preferably, the dynamic reconfiguration execution module includes a modular task scheduling submodule and a real-time feedback and optimization submodule; Modular task orchestration submodule: Through the modular task system, combined with the service mesh architecture (ServiceMesh), seamless coordination between task modules in different combat modes (air defense, anti-missile, electronic countermeasures) is achieved; Real-time feedback and optimization submodule: Based on real-time feedback during the combat process, a dynamic feedback system based on fuzzy control is deployed at key nodes to dynamically adjust the firepower coverage and interception strategy to ensure the agility of combat parameter adjustment.

[0017] Therefore, the present invention adopts the above-mentioned distributed intelligent air and space defense combat adaptive system architecture, which has the following beneficial effects: (1) Combining distributed deployment with intelligent decision-making has achieved optimal allocation and efficient utilization of combat resources; distributed node deployment and distributed deployment of networking systems have improved the flexibility and survivability of the combat system; and the intelligent decision-making of the adaptive decision-making system has ensured the accuracy and effectiveness of the combat plan; (2) The system architecture proposed in the present invention has the ability of adaptive adjustment and dynamic reorganization; the distributed node deployment and networking system can be dynamically reorganized and optimized according to the battlefield situation and mission requirements; the adaptive decision-making system can make real-time adjustments based on feedback information during the combat process; this adaptive adjustment and dynamic reorganization capability enables the combat system to flexibly respond to various complex and changing battlefield environments; (3) The system architecture proposed in this invention realizes cross-domain coordination and joint command and control. The resources of the distributed node deployment and networking system, the adaptive decision-making system and the distributed situational awareness system are effectively integrated to form a highly integrated combat system. The joint global command and control system realizes cross-domain and cross-platform command and control, ensuring the coordination and consistency of combat operations.

[0018] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is an overall block diagram of a distributed intelligent air and space defense combat adaptive system architecture of the present invention. DETAILED DESCRIPTION

[0020] The following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] See also Figure 1 , a distributed intelligent air and space defense combat adaptive system architecture, including distributed node deployment and networking system, distributed situational intelligent perception system, adaptive decision-making system and joint global command and control system; The distributed node deployment and networking system includes an intelligent node network module, a node self-organizing module, and a data transmission module; the intelligent node network module is composed of various types of intelligent nodes, including but not limited to drones, unmanned vehicles, satellites, radars, and missile systems. Each node is set with different functions according to its physical characteristics and combat requirements. Drones and unmanned vehicles are responsible for ground reconnaissance and strike missions, satellites and radars are responsible for air and space reconnaissance and early warning missions, and missile systems are responsible for intercepting and destroying enemy targets. The node self-organizing module adopts an improved AODV (on-demand distance vector) protocol to enable nodes to achieve low-latency and high-stability communication under different network topologies; at the same time, spectrum hopping and adaptive channel allocation technology are introduced to improve the reliability of the network in complex electromagnetic environments; in addition, each intelligent node is equipped with a low-power processing unit and a self-organizing network protocol stack, which can dynamically reorganize the network topology and ensure network connectivity when the node fails or is interfered. The data transmission module includes an intelligent spectrum resource utilization submodule, an efficient data transmission protocol submodule, and a data compression and preprocessing submodule; the intelligent spectrum resource utilization submodule: using cognitive radio technology to perceive the surrounding electromagnetic environment in real time, intelligently select idle spectrum resources for communication, and effectively improve spectrum utilization and anti-interference capabilities; at the same time, by equipping with a spectrum analysis module and a spectrum prediction algorithm, and combining with a deep reinforcement learning model, the intelligent node can predict the spectrum usage trend in real time and avoid the interference frequency band in advance; the efficient data transmission protocol submodule: based on a low-latency multi-hop communication solution, combined with 6G millimeter wave communication technology, high-bandwidth, low-power point-to-point and multi-node transmission is achieved; the data compression and preprocessing submodule: using a data compression algorithm based on a variational autoencoder (VAE) to retain the target features to the greatest extent and reduce the computing pressure of the central node.

[0022] The distributed situation intelligent perception system includes situation prediction module, situation information integration and processing module and situation information real-time update and distribution module. The situation prediction module uses historical combat data and real-time perception data to train the GAN model to predict the target trajectory and plan the deployment of defense resources in advance. By designing a multi-source data enhancement strategy, the multi-source data and historical trajectory are integrated into the GAN model to improve the generalization ability of the model in complex scenarios. The situation information integration and processing module adopts the Transformer network based on feature-level fusion to integrate multi-source information according to temporal correlation and spatial constraints. At the same time, a hybrid model based on the analytic hierarchy process (AHP) and the Bayesian network is introduced to perform situation assessment algorithms, quantitatively analyze and evaluate the battlefield situation, and provide support for decision-making. The situation information real-time update and distribution module is based on the state estimation model of Kalman filtering and multi-sensor fusion, which dynamically corrects the errors in the situation map to ensure the accuracy of the situation information. At the same time, an information distribution protocol is designed to distribute key situation information with low latency through a high-priority queue management mechanism, and combined with network coding technology to ensure that the situation information can be efficiently and reliably transmitted to each combat unit.

[0023] The adaptive decision-making system includes a threat assessment and classification module, a dynamic optimization resource allocation module, and a dynamic reconstruction execution module: Among them, the threat assessment and classification module includes a threat level classification standard submodule and a deep learning model auxiliary decision submodule; the threat level classification standard submodule: by building a dynamic weight adjustment mechanism, the weight of the threat assessment index is dynamically adjusted according to the real-time situation changes of the target type, speed, heading and distance, and at the same time, combined with the spatiotemporal prediction model, the enemy targets that threaten the core targets are evaluated first; the deep learning model auxiliary decision submodule: through the target feature extraction model based on the Transformer architecture, the threats under unknown or extreme conditions are evaluated, the classification ability in complex battlefield environments is improved, and the small sample learning ability is increased. The dynamic optimization resource allocation module includes an intelligent game algorithm submodule and a real-time adjustment strategy submodule; the intelligent game algorithm submodule: based on deep reinforcement learning, the Monte Carlo tree search algorithm (MCTS) is introduced to simulate the various action plans taken by the enemy and generate the optimal solution for resource allocation; the real-time adjustment strategy submodule: through the joint optimization algorithm (such as NSGA-II), the multi-objective problems of interception point selection and firepower coverage are comprehensively optimized. The dynamic reconstruction execution module includes a modular task scheduling submodule and a real-time feedback and optimization submodule; the modular task scheduling submodule: through the modular task system, combined with the service mesh architecture (Service Mesh), it realizes seamless coordination between task modules of different combat modes (air defense, anti-missile, electronic countermeasures); the real-time feedback and optimization submodule: based on real-time feedback during the combat process, a dynamic feedback system based on fuzzy control is deployed at key nodes to dynamically adjust the firepower coverage and interception strategy to ensure the agility of combat parameter adjustment.

[0024] The joint global command and control system integrates the resources of distributed node deployment and networking systems, distributed situational intelligent perception systems and adaptive decision-making systems to integrate the combat system; it adopts time-space calibration algorithms and service grid scheduling frameworks to ensure information synchronization and resource coordination, while combining quantum key distribution and blockchain technology to ensure communication security and identity authentication.

[0025] Therefore, the present invention adopts the above-mentioned distributed intelligent aerospace defense combat adaptive system architecture, and effectively integrates resources through distributed node deployment and networking system, adaptive decision-making system and distributed situational awareness system, thereby forming a highly integrated combat system; through the joint global command and control system, cross-domain and cross-platform command and control is realized, ensuring the coordination and consistency of combat operations.

[0026] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A distributed intelligent air and space defense combat adaptive system architecture, characterized by: Including distributed node deployment and networking system, distributed situational intelligence perception system, adaptive decision-making system and joint global command and control system; The distributed node deployment and networking system includes an intelligent node network module, a node self-organizing module, and a data transmission module; The distributed situation intelligent perception system includes a situation prediction module, a situation information integration and processing module, and a situation information real-time update and distribution module; The adaptive decision-making system includes a threat assessment and classification module, a dynamic optimization resource allocation module, and a dynamic reconstruction execution module: The joint global command and control system integrates the resources of distributed node deployment and networking systems, distributed situational intelligent perception systems and adaptive decision-making systems to integrate the combat system; it adopts time-space calibration algorithms and service grid scheduling frameworks to ensure information synchronization and resource coordination, while combining quantum key distribution and blockchain technology to ensure communication security and identity authentication.

2. A distributed intelligent air and space defense combat adaptive system architecture according to claim 1, characterized in that: The intelligent node network module is composed of various types of intelligent nodes, including drones, unmanned vehicles, satellites, radars and missile systems. Each node is equipped with different functions according to its physical characteristics and combat requirements. Drones and unmanned vehicles are responsible for ground reconnaissance and strike missions, satellites and radars are responsible for aerospace reconnaissance and early warning missions, and missile systems are responsible for intercepting and destroying enemy targets.

3. A distributed intelligent air and space defense combat adaptive system architecture according to claim 2, characterized in that: The node self-organizing module adopts the improved AODV protocol; at the same time, spectrum hopping and adaptive channel allocation technology are introduced; in addition, each intelligent node is equipped with a low-power processing unit and a self-organizing network protocol stack.

4. A distributed intelligent air and space defense combat adaptive system architecture according to claim 3, characterized in that: The data transmission module includes an intelligent spectrum resource utilization submodule, an efficient data transmission protocol submodule, and a data compression and preprocessing submodule; Intelligent spectrum resource utilization submodule: Using cognitive radio technology, it can perceive the surrounding electromagnetic environment in real time and intelligently select idle spectrum resources for communication. At the same time, by equipping with spectrum analysis modules and spectrum prediction algorithms, combined with deep reinforcement learning models, intelligent nodes can predict spectrum usage trends in real time. Efficient data transmission protocol submodule: Based on a low-latency multi-hop communication solution, combined with 6G millimeter wave communication technology, it realizes high-bandwidth, low-power point-to-point and multi-node transmission; Data compression and preprocessing submodule: Utilizes a data compression algorithm based on a variational autoencoder to retain target features and reduce the computational pressure of the central node.

5. A distributed intelligent air and space defense combat adaptive system architecture according to claim 4, characterized in that: The situation prediction module uses historical combat data and real-time perception data to train the GAN model to predict target trajectories and plan defense resource deployment in advance; by designing a multi-source data enhancement strategy, the multi-source data and historical trajectories are fused and input into the GAN model.

6. A distributed intelligent air and space defense combat adaptive system architecture according to claim 5, characterized in that: The situation information integration and processing module adopts the Transformer network based on feature-level fusion to integrate multi-source information according to temporal correlation and spatial constraints. At the same time, a hybrid model based on hierarchical analysis method and Bayesian network is introduced to perform situation assessment algorithm to quantitatively analyze and evaluate the battlefield situation.

7. A distributed intelligent air and space defense combat adaptive system architecture according to claim 6, characterized in that: The real-time update and distribution module of situation information is based on the state estimation model of Kalman filtering and multi-sensor fusion, which dynamically corrects the errors in the situation map. At the same time, an information distribution protocol is designed to distribute key situation information with low latency through a high-priority queue management mechanism, and combine network coding technology to ensure that the situation information is transmitted to each combat unit.

8. The distributed intelligent air and space defense combat adaptive system architecture according to claim 7 is characterized by: The threat assessment and classification module includes the threat level classification standard submodule and the deep learning model assisted decision-making submodule; Threat level classification standard submodule: By building a dynamic weight adjustment mechanism, the weight of threat assessment indicators is dynamically adjusted according to the real-time situation changes of target type, speed, heading and distance. At the same time, combined with the spatiotemporal prediction model, enemy targets that pose a threat to core targets are evaluated first; Deep learning model-assisted decision-making sub-module: Evaluates threats under unknown or extreme conditions through a target feature extraction model based on the Transformer architecture.

9. A distributed intelligent air and space defense combat adaptive system architecture according to claim 8, characterized in that: The dynamic optimization resource allocation module includes an intelligent game algorithm submodule and a real-time adjustment strategy submodule; Intelligent game algorithm submodule: Based on deep reinforcement learning, the Monte Carlo tree search algorithm is introduced to simulate various action plans taken by the enemy and generate the optimal solution for resource allocation; Real-time adjustment strategy submodule: Comprehensively optimize the multi-objective problems of interception point selection and firepower coverage range through a joint optimization algorithm.

10. A distributed intelligent air and space defense combat adaptive system architecture according to claim 9, characterized in that: The dynamic reconfiguration execution module includes a modular task scheduling submodule and a real-time feedback and optimization submodule; Modular task orchestration submodule: Through the modular task system, combined with the service grid architecture, seamless coordination between task modules in different combat modes can be achieved; Real-time feedback and optimization submodule: Based on real-time feedback during the combat process, a dynamic feedback system based on fuzzy control is deployed to dynamically adjust the firepower coverage and interception strategy.

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