A method and system for constructing a double-layer dependent command and control network interlayer coupling strategy

Abstract

The application relates to a double-layer dependent command control network interlayer coupling strategy construction method and system, which comprises the following steps: defining a node walk betweenness, the node walk betweenness being quantified based on the accumulated information amount of network nodes in multiple information walk processes; calculating interlayer node dependence strength based on the node walk betweenness, the interlayer node dependence strength being positively correlated with the product of the walk betweenness of double-layer network nodes; establishing a node combat attribute matching degree model to evaluate the cross-layer node cooperation capability; the node combat attribute matching degree model being calculated based on the node self combat capability and the cooperation factor of the cooperative node; combining the interlayer node dependence strength and the combat attribute matching degree to construct an interlayer coupling probability function containing a hierarchical adjustment factor; and dynamically selecting the coupling nodes by using a roulette algorithm based on the interlayer coupling probability function to generate an interlayer coupling strategy. The application solves the problem that the coupling strategy ignores information multi-path transmission and dynamic combat attribute cooperation, and improves the network damage resistance performance.

Description

Technical Field

[0001] This invention relates to the intersection of complex networks and command and control systems, and in particular to a method and system for constructing interlayer dynamic coupling strategies for two-layer interdependent command and control networks. Background Technology

[0002] In today's highly information-driven and intelligent modern warfare environment, command and control networks have become the core infrastructure for achieving integrated battlefield situational awareness, decision-making, resource allocation, and firepower strikes, playing a crucial and even decisive role in the military field. Command and control networks are responsible for integrating, transmitting, and processing information, providing decision-makers with real-time and accurate intelligence and instructions. As operational concepts evolve towards "systematization and network centralization," command and control networks are no longer isolated systems composed of single homogeneous nodes, but have gradually evolved into interdependent networks composed of multiple functional subnets such as perception, command and control, and firepower, through deep interaction and dependencies. In these interdependent networks, inter-layer coupling strategies—that is, how nodes in different subnets establish dependencies—have become key factors affecting the robustness, survivability, and operational effectiveness of the entire system. Therefore, scholars both domestically and internationally have conducted extensive research on the types and selection of coupling strategies for interdependent networks. Although existing research has made some progress, the inventors have found that these traditional coupling strategies still have significant limitations and are difficult to meet the high survivability requirements of modern command and control networks. Most existing strategies rely on the static topology attributes of nodes, neglecting the diversity and randomness of information transmission path selection, leading to biases in the assessment of the true influence of nodes. Existing coupling strategies generally ignore the operational attributes of nodes, merely connecting two nodes based on topology, which may lead to functional mismatch, accelerate cascading failures, and reduce the operational efficiency of the entire network.

[0003] Based on this, this invention proposes a definition of node walking betweenness, similar to node betweenness centrality; based on the multipath propagation characteristics of information, the network is dynamically quantified through information walking processes to calculate the global influence of nodes; combining an improved operational attribute quantification method, walking betweenness, and hierarchical adjustment factor, a new interlayer dependency strength is defined; based on node functional attributes and command hierarchy, a node operational attribute matching degree is defined; based on the dependency strength and attribute matching degree of this invention, an interlayer coupling strategy for a two-layer dependent command and control network that combines information transmission capabilities with operational function synergy is designed. This invention fully considers the multipath characteristics of node information transmission, the synergistic requirements of functional attributes, and command hierarchy constraints in the command and control network, and designs an interlayer coupling strategy that combines dynamism and functionality. This invention effectively reduces the risk of cascading failures in the two-layer command and control network, significantly improves the network's resilience and operational effectiveness, and provides new theoretical basis and practical reference for the optimized design of command and control networks.

[0004] Furthermore, on the one hand, there are differences in understanding among those skilled in the art; on the other hand, the applicant studied a large number of documents and patents when making this invention, but due to space limitations, not all details and contents were listed in detail. However, this does not mean that the present invention does not possess the features of these prior art. On the contrary, the present invention already possesses all the features of the prior art, and the applicant reserves the right to add relevant prior art to the background art. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for constructing interlayer coupling strategies in a two-layer interdependent command and control network. Addressing the shortcomings of existing coupling strategies in the background art—namely, their inability to simultaneously consider the dynamic information transmission capabilities of nodes and the synergy of operational attributes, as well as their failure to effectively integrate network hierarchical characteristics—this method aims to dynamically quantify the global influence of nodes in a multi-path information environment by introducing node betweenness factors, accurately assess the functional synergy potential between cross-layer nodes by establishing a node operational attribute matching degree model, and design a probabilistic coupling function that integrates dependency strength, functional matching, and hierarchical preferences. Ultimately, this achieves an interlayer dynamic coupling strategy that significantly improves network survivability and operational effectiveness.

[0006] To address the shortcomings of existing technologies, this invention provides a method for constructing inter-layer coupling strategies in a two-layer interdependent command and control network. This method includes: defining node walking betweenness factors, which are quantified based on the amount of information accumulated by network nodes during multiple information walks; calculating the inter-layer node dependency strength based on the node walking betweenness factors, wherein the inter-layer node dependency strength is positively correlated with the product of the walking betweenness factors of the two-layer network nodes; establishing a node combat attribute matching degree model to evaluate the cross-layer node collaboration capability; constructing an inter-layer coupling probability function including a hierarchical adjustment factor by combining the inter-layer node dependency strength and the combat attribute matching degree; and dynamically selecting coupling nodes using a roulette wheel algorithm based on the inter-layer coupling probability function to generate an inter-layer coupling strategy.

[0007] This invention effectively solves the problem that traditional coupling strategies neglect information multipath transmission and dynamic coordination of combat attributes, significantly improves network resilience, and can provide a reference for the optimized design of command and control networks.

[0008] This invention fully considers the multi-path characteristics of node information transmission, the collaborative requirements of functional attributes, and the constraints of command hierarchy in command and control networks, and designs an inter-layer coupling strategy that combines dynamism and functionality. This invention effectively reduces the risk of cascading failures in two-layer command and control networks, significantly improves the network's resilience and operational effectiveness, and provides new theoretical basis and practical reference for the optimized design of command and control networks.

[0009] According to a preferred embodiment, the steps of defining the node walk betweenness include: defining a set G to represent the set of all nodes directly connected to node i, considering the multi-path transmission characteristics of node information, and after n information walks, traversing the set G to obtain the amount of information owned by node , and defining the amount of information as the node walk betweenness; the calculation formula of the node walk betweenness is: ; where, is the amount of information owned by node after the nth information walk; is the degree of node j; is the initial amount of information; is the connection relationship between node i and node j; ; D is the maximum number of network levels; , is the node directly connected to node i.

[0010] This method can more comprehensively reflect the importance of nodes in the network by defining the node walk betweenness. Specifically, by defining the set G of nodes directly connected to node , and considering the propagation paths of information during multiple walks, the role of nodes in information flow can be more accurately evaluated. This process not only reflects the multi-path characteristics of information transmission, but also reveals the key position of nodes in the entire network by analyzing the amount of information on different paths. In this way, it is possible to more scientifically identify which nodes have higher strategic value in the network, providing reliable data support for the subsequent design of inter-layer coupling strategies and enhancing the overall coordination ability and anti-destruction performance of the network.

[0011] According to a preferred embodiment, the steps of calculating the inter-layer node dependence strength based on the node walk betweenness include: considering all paths of information walks rather than just the shortest paths, and the calculation formula of the inter-layer node dependence strength is: ; where, is the node walk betweenness of node i in subnet A, is the node walk betweenness of node j in subnet B; a(0 < a < 1) is an adjustment parameter to avoid the situation where the node walk betweenness of some edge nodes is 0.

[0012] The step of calculating inter-layer node dependency strength based on node walk betweenness factors, by considering the propagation of information across all paths rather than relying solely on the shortest path, can more comprehensively reflect the dependencies between nodes. This method not only avoids the impact of redundant paths, which may be overlooked in traditional methods, on network stability, but also enhances the understanding of the global role of nodes in complex information flows. Introducing an adjustment parameter 'a' helps balance the dependency strength between different nodes, especially when the walk betweenness factors of some edge nodes may be zero, preventing them from causing excessive deviations from the overall calculation results, thereby improving the robustness and applicability of the model. In this way, the calculation of inter-layer node dependency strength is more reasonable, providing a more accurate basis for subsequently constructing dynamic and efficient inter-layer coupling strategies, which helps optimize the structure of command and control networks and improve their resilience and collaborative efficiency in the face of failures or attacks.

[0013] According to a preferred implementation, the steps of establishing a node combat attribute matching degree model and evaluating cross-layer node collaborative capabilities include: considering the needs of node collaborative operations, calculating the combat capabilities of upper-layer network nodes; calculating the combat capabilities of lower-layer network command and control nodes; and calculating the inter-layer node combat attribute matching degree between the combat capabilities of upper-layer network nodes and the combat capabilities of lower-layer network command and control nodes.

[0014] This invention establishes a node combat attribute matching model and evaluates the steps of cross-layer node collaboration. By comprehensively considering the combat capabilities of upper-layer network nodes and lower-layer network command and control nodes, it can more accurately measure the degree of functional compatibility between the two. This method not only focuses on the combat attributes of the nodes themselves but also emphasizes the collaborative relationships between nodes at different levels, helping to identify which nodes are more suitable for inter-layer connections, thereby optimizing the overall network's collaborative efficiency. By using combat capability as the evaluation criterion, it ensures that inter-layer coupling strategies meet both information transmission requirements and the execution requirements of actual combat missions, improving the command and control network's responsiveness and combat effectiveness in complex environments. Furthermore, this model provides a theoretical basis for dynamically adjusting inter-layer coupling relationships, enabling the network to flexibly optimize according to mission changes and node states, enhancing the system's adaptability and stability.

[0015] According to a preferred implementation, in the step of establishing a node combat attribute matching degree model and evaluating cross-layer node collaborative capabilities, the calculation formula for the combat capability of upper-layer network nodes is as follows: ; in, The weighted sum of the combat attributes of node i quantifies the inherent combat potential or effectiveness of node i as an independent combat unit, representing the node's own combat capability. Let be the cooperation factor between node i and its cooperating nodes, representing the difference in attribute vectors between node i and its neighboring nodes. This factor compensates for the blind spots in the capabilities of a single node and represents the collaborative combat capability of the nodes. Let i be the set of cooperating / adjacent collaborative nodes. Let i be any node in the set of cooperating / adjacent collaborative nodes of node i.

[0016] Through this step, the present invention can more systematically measure the comprehensive performance of nodes in combat missions. The formula consists of two parts: first, a weighted sum of the combat attributes of node i, used to quantify its inherent combat potential or effectiveness as an independent combat unit, reflecting the node's own combat capabilities; second, a cooperation factor between node i and its cooperating nodes, reflecting the degree of matching between node i and its neighboring nodes in terms of attributes, compensating for the limitations of single-node capability assessment, and thus more comprehensively reflecting the role of nodes in collaborative operations. This calculation method not only helps identify the key roles of nodes in combat missions but also enhances the cooperation efficiency between nodes at different layers, enabling the command and control network to have stronger adaptability and stability when facing complex tasks. By combining the node's own capabilities with its collaborative capabilities, this model provides a scientific basis for constructing a more rational and efficient inter-layer coupling strategy, further improving the overall combat effectiveness and resilience of the network.

[0017] According to a preferred implementation, in the step of establishing a node combat attribute matching degree model and evaluating cross-layer node collaborative capabilities, the combat capability of the lower-layer network command and control node is calculated; the combat capability of the lower-layer network command and control node is the normalized product of communication physical capability and hierarchical effect, and its calculation formula is as follows: ; in, Let H(i) be the Shannon channel capacity of node i, representing its information throughput capacity at the physical layer; H(i) represents the layer where node i is located; CP max D represents the maximum Shannon channel capacity among all nodes; D is the maximum number of network levels.

[0018] This step, by combining Shannon channel capacity and hierarchical effects, provides a more comprehensive assessment of the operational capabilities of lower-level nodes, reflecting both their physical communication performance and hierarchical strategic value. Normalization ensures the objectivity and comparability of the assessment, helps to accurately identify nodes with high collaborative potential, improves the scientific rigor and effectiveness of inter-layer coupling strategies, and enhances the overall network resilience and operational effectiveness.

[0019] According to a preferred embodiment, in the step of establishing a node combat attribute matching degree model and evaluating cross-layer node collaborative capabilities, the formula for calculating the inter-layer node combat attribute matching degree is as follows: ; in, Let be the matching degree index between node i and node j. This is the normalized value of the path length between node i and node j.

[0020] This step quantifies the collaborative potential of inter-layer nodes by combining the matching degree index with the normalized path length, taking into account both functional matching and communication efficiency. Normalization enhances the fairness of the assessment, making the matching degree closer to actual collaborative needs, thereby improving the rationality and adaptability of inter-layer coupling strategies and enhancing the overall network resilience and operational effectiveness.

[0021] According to a preferred embodiment, the calculation formula for the inter-layer coupling probability function, which incorporates a layer adjustment factor, is constructed by combining the inter-layer node dependency strength and the combat attribute matching degree: ; Where i is the upper-layer network node, j is the lower-layer network node, and N is the lower-layer network node. c For the set of accusation nodes, The maximum number of levels in the network. Let be the basic inter-layer correlation degree between upper-layer network node i and lower-layer node j. H(j) represents the matching degree of combat attributes between nodes at different levels, and H(j) represents the level adjustment factor of the level where command and control node j is located. s and T f These are sets of sensing nodes and firepower nodes, respectively.

[0022] This step combines dependency strength with operational attribute matching degree and introduces hierarchical effects to make the inter-layer coupling probability more closely match actual combat needs. The formula design takes into account information transmission efficiency and functional synergy, enhances the dynamic adaptability of the strategy, effectively improves network resilience and combat effectiveness, and achieves a more scientific and reasonable inter-layer connection.

[0023] According to a preferred embodiment, the step of dynamically selecting coupling nodes using a roulette wheel algorithm to generate an inter-layer coupling strategy based on an inter-layer coupling probability function includes: Initialize the network A Middle nodes form a queue Q A ; Initialize the network B The nodes in the queue form a queue. Q B ; Calculate the node traversal betweenness of each node; From the queue in sequence Q A Select the node that has not yet formed a coupled edge, and denote it as node i; if the queue Q A Traversal complete, end; Compute node i and queue Q B The interdependence strength of each node and the matching degree of node combat attributes; Determine the type of node i, and calculate the relationship between node i and queue Q. B The probability of dependency between nodes is used to select dependent node j using the roulette wheel method; Select the neighboring nodes of node i and its dependent node j, and denote them as Q. i and Q j ; Remove node j from queue Q B Delete; if queue Q B If the current state is empty, reinitialize the nodes in network B to form queue Q. B ; Queue Q i and Q j As a local world, the interdependence strength and combat attribute matching degree of each node are calculated, and then the relationship between node i and Q is calculated. B The probability of dependency between nodes is used to select dependent node j using the roulette wheel method; this continues until Q. i Once the traversal is complete, continue from the queue. Q A Select the node that has not yet formed a coupled edge, and denote it as node i; if the queue Q A Traversal complete, end.

[0024] This step dynamically selects coupling nodes using a roulette wheel algorithm, combining dependency strength and operational attribute matching to achieve efficient and rational inter-layer connections. The queue traversal mechanism ensures coverage of all nodes, enhancing the comprehensiveness and flexibility of the strategy, improving the network's resilience and collaborative efficiency in complex environments, and optimizing the overall performance of the command and control system.

[0025] This invention provides, from a second aspect, a system for constructing inter-layer coupling strategies in a two-layer interdependent command and control network. The system includes a processor comprising a first computing module, a second computing module, a third computing module, a fourth computing module, and a fifth computing module. The first computing module defines node walk betweenness factors, which are quantified based on the amount of information accumulated by network nodes during multiple information walks. The second computing module calculates the inter-layer node dependency strength based on the node walk betweenness factors, wherein the inter-layer node dependency strength is positively correlated with the product of the walk betweenness factors of the two-layer network nodes. The third computing module establishes a node combat attribute matching degree model to evaluate the cross-layer node collaboration capability; wherein the node combat attribute matching degree model is calculated based on the node's own combat capability and the cooperation factor of collaborating nodes. The fourth computing module combines the inter-layer node dependency strength and combat attribute matching degree to construct an inter-layer coupling probability function containing a hierarchical adjustment factor. The fifth computing module uses a roulette wheel algorithm to dynamically select coupled nodes based on the inter-layer coupling probability function to generate an inter-layer coupling strategy. A memory stores node attribute data, calculation results, and strategy output information, providing data support for the processor. The input / output interface is responsible for inputting network data and outputting policy results, enabling information exchange between the system and the external environment.

[0026] This system employs a modular architecture, enabling each component of the algorithm to operate independently yet collaboratively, thereby improving overall execution efficiency and system scalability. The first module quantifies the global influence of nodes in multi-path transmission; the second module calculates the dependency strength between nodes; the third module evaluates cross-layer collaborative capabilities; the fourth module integrates multi-dimensional indicators to design coupling probabilities; and the fifth module dynamically generates strategies. This structured design enhances the algorithm's interpretability and execution efficiency, ensures the strategy can adapt to complex operational environments, strengthens network robustness and collaboration, and provides systematic and intelligent support for the optimization of command and control systems. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of node survival rate under different adjustment parameters 'a' during a deliberate attack, provided by the present invention. Figure 2 This is a schematic diagram of node survival rate under different adjustment parameters 'a' during random attacks provided by the present invention; Figure 3 This is a schematic diagram illustrating the survival rate of nodes with different dependency strengths under deliberate attacks provided by the present invention; Figure 4 This is a schematic diagram illustrating the survival rate of nodes with different dependency strengths under random attacks provided by the present invention. Figure 5 This is a schematic diagram illustrating the survival rate of nodes with different matching degrees under deliberate attacks provided by the present invention. Figure 6This is a schematic diagram illustrating the survival rate of nodes with different matching degrees under random attacks provided by the present invention. Figure 7 This is a schematic diagram illustrating the node survival rate under different inter-layer coupling strategies during a deliberate attack, as provided by the present invention. Figure 8 This is a schematic diagram illustrating the node survival rate under different inter-layer coupling strategies during random attacks provided by the present invention. Figure 9 This is a schematic diagram illustrating the efficiency reduction rate of the combat link under different coupling strategies during a deliberate attack, provided by the present invention. Figure 10 This is a schematic diagram illustrating the efficiency reduction rate of the combat link under different coupling strategies under random attacks provided by the present invention. Figure 11 This is a schematic diagram illustrating the node survival rates of different network models under deliberate attacks provided by the present invention. Figure 12 This is a schematic diagram illustrating the node survival rates of different network models under random attacks provided by the present invention; Figure 13 This is a schematic diagram illustrating the operational link efficiency of different network models under deliberate attacks provided by the present invention; Figure 14 This is a schematic diagram illustrating the operational link efficiency of different network models under random attacks provided by the present invention; Figure 15 This is a schematic diagram of the framework of the interlayer coupling strategy construction system for the two-layer interdependent command and control network provided by the present invention.

[0028] List of reference numerals in the attached diagram: 100: Processor; 110: First computing module; 120: Second computing module; 130: Third computing module; 140: Fourth computing module; 150: Fifth computing module; 200: Memory; 300: Input / output interface. Detailed Implementation

[0029] The following is a detailed explanation with reference to the accompanying drawings.

[0030] Example 1 To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments. Those skilled in the art will understand the specific meanings of the terms used in this invention based on the specific circumstances.

[0031] This invention provides a system for constructing interlayer coupling strategies in a two-layer dependent command and control network, used to execute the method for constructing interlayer coupling strategies in a two-layer dependent command and control network according to this invention.

[0032] like Figure 15As shown, the system of the present invention includes a processor 100. The physical hardware of the processor 100 is a CPU. The processor 100 includes a first computing module 110, a second computing module 120, a third computing module 130, a fourth computing module 140, and a fifth computing module 150. The processor 100 is physically connected to the memory 200 and the input / output interface (I / O) 300 via a system bus.

[0033] like Figure 15 As shown, the first calculation module 110 is a node walking betweenness calculation circuit, used to define the node walking betweenness. This node walking betweenness is quantified based on the amount of information accumulated by network nodes during multiple information walks. The second calculation module 120 is a node dependency strength calculation circuit, used to calculate the inter-layer node dependency strength based on the node walking betweenness. This inter-layer node dependency strength is positively correlated with the product of the walking betweennesses of the two-layer network nodes. The third calculation module 130 is an attribute matching degree calculation circuit, used to establish a node combat attribute matching degree model to evaluate the cross-layer node cooperation capability. This node combat attribute matching degree model is calculated based on the node's own combat capability and the cooperation factor of cooperating nodes. The fourth calculation module 140 is a local world construction circuit, used to combine the inter-layer node dependency strength and combat attribute matching degree to construct an inter-layer coupling probability function containing a hierarchical adjustment factor. The fifth calculation module 150 is an inter-layer coupling strategy generation circuit, used to dynamically select coupling nodes based on the inter-layer coupling probability function using a roulette wheel algorithm to generate an inter-layer coupling strategy. The memory stores node attribute data, calculation results, and policy output information, providing data support for the processor 100. The input / output interfaces handle network data input and policy result output, enabling information exchange between the system and the external environment.

[0034] like Figure 15 As shown, the node walk betweenness calculation circuit and the node dependency strength calculation circuit are physically connected via internal wiring. The node dependency strength calculation circuit and the local world construction circuit are physically connected via internal wiring. The attribute matching degree calculation circuit and the local world construction circuit are physically connected via internal wiring. The local world construction circuit and the inter-layer coupling strategy generation circuit are physically connected via internal wiring.

[0035] like Figure 15 As shown, the memory internally includes a data buffer and an instruction storage area. The data buffer stores historical calculation results and policy results. The input / output interfaces include a network data input interface and a coupled policy output interface.

[0036] This embodiment provides a method for constructing an interlayer coupling strategy for a two-layer interdependent command and control network, including the following steps S100~S500.

[0037] S100: Define the node traversal betweenness, which is quantified based on the amount of information accumulated by a network node during multiple information traversals.

[0038] Let set G represent the set of all nodes directly connected to node i. Considering the multipath transmission characteristics of node information, the amount of information possessed by node i after traversing set G through n information walks is the node traversal betweenness. The formula for calculating the node traversal betweenness is as follows: ; in, For the node after the nth information walk The amount of information possessed; For nodes The degree; This represents the initial amount of information. This represents the connection relationship between node i and node j; D represents the maximum number of levels in the network. , The node is directly connected to node i.

[0039] S200: Calculate the inter-layer node dependency strength based on node walking betweenness.

[0040] Considering all paths of information travel rather than just the shortest path, the formula for calculating the inter-layer node dependency strength is as follows: .

[0041] in, Let be the node traversal betweenness of node i in subnet A. Let be the node traversal betweenness of node j in subnet B. (0< <1) is an adjustment parameter to avoid the situation where the node betweenness of some edge nodes is 0.

[0042] S300: Establish a node combat attribute matching degree model to evaluate cross-layer node collaboration capabilities. This node combat attribute matching degree model is calculated based on the node's own combat capabilities and the cooperation factor of collaborating nodes. Considering the need for node collaborative operations, the formula for calculating the combat capability of upper-layer network nodes is as follows: .

[0043] in, The weighted sum of the combat attributes of node i quantifies the inherent combat potential or effectiveness of node i as an independent combat unit, representing the node's own combat capability. Let be the cooperation factor between node i and its cooperating nodes, representing the difference in attribute vectors between node i and its neighboring nodes. This factor compensates for the blind spots in the capabilities of a single node and represents the collaborative combat capability of the nodes. Let i be the set of cooperating / adjacent collaborative nodes. Let i be any node in the set of cooperating / adjacent collaborative nodes of node i.

[0044] Meanwhile, the combat performance of command and control nodes is mainly reflected in the speed at which they issue commands, i.e. the communication capability of nodes. Considering that the communication capability of network nodes depends on their own hardware characteristics on the one hand, and is also positively correlated with their level on the other hand, the combat capability of lower-level network command and control nodes is defined as the normalized product of communication physical capability and hierarchical effect.

[0045] The formula for calculating the combat capability of lower-layer network command and control nodes is as follows: .

[0046] in Let H(i) be the Shannon channel capacity of node i, representing its information throughput capacity at the physical layer; H(i) represents the layer where node i is located; CP max D represents the maximum Shannon channel capacity among all nodes; D is the maximum number of network levels.

[0047] Based on the definitions of combat capabilities of upper-layer and lower-layer network nodes, the matching degree of combat attributes between inter-layer nodes is obtained. The formula for calculating the matching degree of combat attributes between inter-layer nodes is as follows: .

[0048] in, Let be the matching degree index between node i and node j. This is the normalized value of the path length between node i and node j.

[0049] S400: Combining the inter-layer node dependency strength and the combat attribute matching degree, construct an inter-layer coupling probability function containing a layer adjustment factor.

[0050] The formula for calculating the interlayer coupling probability function is as follows: .

[0051] Where i represents the upper-layer network node, j represents the lower-layer network node, and N c For the set of accusation nodes, The maximum number of levels in the network. Let be the basic inter-layer correlation degree between upper-layer network node i and lower-layer node j. H(j) represents the matching degree of combat attributes between nodes at different levels, and H(j) represents the level adjustment factor of the level where command and control node j is located. s and T f These are sets of sensing nodes and firepower nodes, respectively.

[0052] S500: Based on the inter-layer coupling probability function, the roulette wheel algorithm is used to dynamically select coupling nodes to generate inter-layer coupling strategies.

[0053] The inter-layer coupling strategy is dynamically generated using the roulette wheel algorithm, and the process of calculating the edge connection probability is described below.

[0054] The algorithm for determining the edge connection probability is a coupling strategy.

[0055] Inputs to the inter-layer coupling strategy: Network A; Network B, betweenness centrality of Network A, betweenness centrality of Network B. Output of the inter-layer coupling strategy: Coupled network (A, B).

[0056] S501: Initialize the nodes in network A and form queue Q. A .

[0057] S502: Initialize the nodes in network B and form queue Q. B .

[0058] S503: Calculate the node traversal betweenness of each node according to the aforementioned formula.

[0059] S504: Sequentially from queue Q A Select the node that has not yet formed a coupled edge, and denote it as node i; when queue Q A After traversal is complete, proceed to step S510.

[0060] S505: Calculate the relationship between node i and queue Q using the formulas for calculating the inter-node dependency strength and the inter-node combat attribute matching degree. B The interdependence strength of each node and the matching degree of node combat attributes.

[0061] S506: Determine the type of node i, and calculate the relationship between node i and queue Q. B The probability of dependency between nodes is used to select dependent node j using the roulette wheel method.

[0062] S507: Select the neighboring nodes of nodes i and j and denote them as queues Q respectively. i and queue Q j .

[0063] S508: Remove node j from queue Q B Delete it; if QB is empty, proceed to step S502.

[0064] S509: Move queue Q i and queue Q j As a local world, execute Steps 505-506. Continue until queue Q... i After traversal is complete, proceed to step S504.

[0065] S510: End.

[0066] The time complexity of the interlayer coupling strategy is O(m·n) (where m and n represent the total number of nodes in the upper and lower layers of the two-layer structure, respectively).

[0067] To verify the feasibility, effectiveness, and applicability of this invention, simulation analysis was designed from five aspects.

[0068] The initial parameters are set as follows: the total number of nodes is 580, of which n are command and control nodes. c =160, Sensing Node n s =150, Firepower Node n f =270; Command span k=3, Command level h=4; Assume that the initial combat attribute weights of the nodes are the same; Two attack strategies are adopted: deliberate and random; When attacking randomly, 20 experiments are conducted to calculate the average value.

[0069] (1) Simulation analysis of adjusting parameter a To obtain a reasonable dependency strength adjustment parameter 'a', a network performance simulation experiment was designed to vary with 'a'. The experimental results are as follows: Figure 1 and Figure 2 As shown. Figure 1 This diagram illustrates the node survival rate under different parameter 'a' adjustments during a deliberate attack. Figure 1 and Figure 2 In the diagram, the X-axis represents the adjustment parameter a, the Y-axis represents the number of attacked nodes (DeleteNodeNum), and the Z-axis represents the node survival rate. Different colors are used to observe the node survival rate; the higher the survival rate, the closer the color is to bright yellow. Figure 1 and Figure 2 The red curve in the graph represents the characteristic curve for a=0.3. From... Figure 1 and Figure 2 It can be seen that the node survival rate is the highest when a=0.3, indicating that the network resilience of the present invention is optimal under this parameter.

[0070] Figure 1 This shows how the node survival rate changes with 'a' after deleting 100-150 nodes. Figure 2 This diagram illustrates the node survival rate under different adjustment parameters 'a' during a random attack. Figure 2 This shows how the node survival rate changes with the adjustment parameter 'a' after deleting 300-350 nodes. (Comparison) Figure 1 and Figure 2 It can be seen that the node survival rate is maximized when a=0.3, and this value was selected for all subsequent experiments. Under random attacks, the node survival rate changes more smoothly, but the influence of adjusting parameter a on network stability follows the same pattern, verifying the applicability and robustness of adjusting parameter a=0.3.

[0071] (2) Comparative analysis of different interdependence intensities The three dependency strengths proposed in this invention—wandering betweenness (WB), betweenness (B), and proximity centrality (C)—were compared and analyzed. Figure 3 and Figure 4 These represent the network survival rates under three different dependency strengths, categorized as intentional and random attacks. Figure 3 and Figure 4 In the diagram, the X-axis represents the number of deleted nodes, and the Y-axis represents the network survival rate. Figure 3 In the diagram, the red curve represents the change in node survival rate under deliberate attack based on the Walking Betweenness (WB) method of this invention, the dark blue curve represents the change in node survival rate under deliberate attack based on the Betweenness Centrality (B) method, and the black curve represents the change in node survival rate under deliberate attack based on the Proximity Centrality (C) method. Figure 4 In the diagram, the red curve represents the change in node survival rate under random attacks based on the walking betweenness (WB) method, the dark blue curve represents the change in node survival rate under random attacks based on the betweenness centrality (B) method, and the black curve represents the change in node survival rate under random attacks based on the proximity centrality (C) method.

[0072] like Figure 3 As shown, under deliberate attack, the node survival rates of the three methods are roughly the same at the beginning, but after deleting 100 nodes, the survival rate reaches an inflection point, which means that the network begins to experience large-scale cascading failures. Among them, the node survival rate of the network built based on betweenness and proximity centrality drops rapidly to 0. The dependency strength (red curve) of the method proposed in this invention delays the cascading failures, and the node survival rate only drops to 0 after deleting 150 nodes.

[0073] Depend on Figure 4 It can be seen that under random attacks, the black curve (C) decreases the fastest, followed by the dark blue curve (B), and the red curve (WB) decreases the slowest. After deleting approximately 100 nodes, the three curves begin to separate. The black curve (C) and the dark blue curve (B) have already decreased rapidly before deleting 200 nodes, while the red curve (WB) only decreases slowly after deleting 300 nodes. The method of this invention is significantly superior to the other two methods. This is because this invention uses the walk betweenness method, considering multi-path propagation of data and dynamic changes in the network, thus delaying sudden changes in network performance. Figure 3 and Figure 4 As can be seen from the curves, the overall survival rate of the method of the present invention is higher than that of other methods, demonstrating better stability and resilience.

[0074] (3) Simulation analysis of matching degree of different nodes The effectiveness of the combat attribute matching degree proposed in this invention is verified by comparing its performance differences with hierarchical matching degree and random matching degree. Figure 5 and Figure 6 These represent node survival rates under different node matching degrees, specifically for deliberate and random attacks. Figure 5 and Figure 6 In the diagram, the X-axis represents the number of deleted nodes, the Y-axis represents the network survival rate, the red curve represents the change in the node survival rate of the AM (combat attribute matching degree) model of this invention, the dark blue curve represents the change in the node survival rate of the LM (hierarchical matching degree) model, and the black curve represents the change in the node survival rate of the RM (random matching degree) model.

[0075] like Figure 5 As shown, under a deliberate attack, the black curve (RM) and the blue curve (LM) first show an inflection point and then decline rapidly after deleting approximately 100 nodes, while the red curve (AM) subsequently shows an inflection point and declines slowly. This indicates that under a deliberate attack, the AM (operational attribute matching degree) model of this invention can effectively avoid weak matching dependency edges and improve system stability.

[0076] like Figure 6 As shown, under random attacks, the black curve (RM) decreases the fastest, followed by the blue curve (LM), and the red curve (AM) decreases the slowest, with the red curve consistently maintaining the highest node survival rate. This indicates that under random attacks, the AM (Operational Attribute Matching) model of this invention still maintains the highest node survival rate, demonstrating that the AM (Operational Attribute Matching) model has good versatility.

[0077] Depend on Figure 5 and Figure 6 It can be seen that, under the two attack strategies, the node survival rate of the node matching degree proposed in this invention is the highest and the network model constructed is more resistant to destruction. This is because considering the node matching degree can effectively avoid the unbalanced dependency edge formed by two nodes with large differences in combat performance, thereby suppressing cascading failures.

[0078] (4) Simulation analysis of different interlayer coupling strategies To observe the impact of different coupling strategies on the model's resilience, a comparative analysis was conducted on the changes in survival rate and operational link efficiency after attacks on the network using the present invention and several other coupling strategies. Figure 7-10 As shown. In Figure 7 and Figure 8In the diagram, the X-axis represents the number of attacking nodes (Number of delete nodes), the Y-axis represents the network survival rate (Network Survival Rate), the red curve represents PRO (PRO strategy of this invention), the dark blue curve represents DB (load balancing strategy), the black curve represents RL (random strategy), the green curve represents NPC (neighbor node priority strategy), the orange curve represents BAL (degree matching), the light blue curve represents BDL (degree dissimilar matching), the pink curve represents DAL (degree matching), and the purple curve represents DDL (degree dissimilar matching).

[0079] like Figure 7 As shown, under a deliberate attack, the red curve representing the PRO strategy is consistently higher than the curves representing other strategies, indicating that the PRO strategy has the best resilience under a deliberate attack. Figure 8 As shown, under random attacks, the curve comparison results show that the red curve of the PRO strategy is also at the highest position among all curves, indicating that its resilience in random attack environments is also better than other strategies.

[0080] Depend on Figure 7 and Figure 8 It is known that, regardless of whether it is a deliberate attack or a random attack, the node survival rate curve of the PRO strategy of this invention is consistently higher than that of all other strategies, fully demonstrating its comprehensive superiority in survivability. This is because the combination of walk betweenness and combat attribute matching effectively balances information flow and functional flow, constructing a more resistant network structure.

[0081] exist Figure 9 and Figure 10 In the diagram, the X-axis represents the number of delete nodes, the Y-axis represents the network combatlink efficiency, the red curve represents PRO (the PRO strategy of this invention), the dark blue curve represents DB (load balancing strategy), the black curve represents RL (random strategy), the green curve represents NPC (neighbor node priority strategy), the orange curve represents BAL (degree matching), the light blue curve represents BDL (degree dissimilar matching), the pink curve represents DAL (degree matching), and the purple curve represents DDL (degree dissimilar matching).

[0082] like Figure 9As shown, the light blue curve rises the fastest in the early stages, reaching the high-efficiency range relatively early; the black, green, orange, pink, and purple curves then gradually rise, with small differences between them; the red curve (the strategy of this invention) and the dark blue curve rise relatively slowly, and the red curve remains consistently higher than the other curves in the later stages, maintaining its advantage in operational link efficiency. This indicates that the link efficiency of the red curve in the PRO strategy of this invention decreases the slowest, always maintaining a high level, ensuring the continuous effectiveness of the operational link.

[0083] like Figure 10 As shown, in the initial stage, multiple curves (such as red, dark blue, and black) are relatively dense, with similar efficiency improvement rates. As the number of deleted nodes increases, the red curve gradually shows its advantage, with a stable rate of increase that eventually reaches the highest level. Other curves, such as green, orange, pink, and purple, also continue to rise, but their final efficiency is lower than that of the red curve. The black and dark blue curves are at a moderate level, while the sky blue curve has a period of stability before rising. This also shows that the red curve, representing the PRO strategy of this invention, experiences the slowest decline in link efficiency, and its efficiency advantage becomes increasingly apparent as the number of deleted nodes increases, indicating that it exhibits high robustness under different attack modes.

[0084] Combination Figure 9 and Figure 10 It is evident that the PRO strategy of this invention exhibits the slowest rate of decline in operational link efficiency, and its efficiency is significantly lower than other strategies in the later stages of an attack (i.e., the decline is less). This demonstrates that this invention not only ensures the survival of physical nodes, but more importantly, guarantees the effectiveness of operational functions, achieving a balance between topology resilience and functional effectiveness.

[0085] (5) Simulation analysis of different network models To verify the universality and stability of the proposed method under different subnet construction methods, two figures compare the network performance of different subnet construction models using the PRO and DB strategies under the same attack mode. The comparison figures of survival rate and operational link efficiency of several different network models are shown below. Figures 11 to 14 As shown.

[0086] The network model is constructed as shown in Table 1 below.

[0087] Table 1: Network Model Construction Strategies

[0088] exist Figure 11 and Figure 12 In the diagram, the X-axis represents the number of deleted nodes, and the Y-axis represents the network survival rate. Figure 11In the diagram, the red curve represents the node survival rate of network model M1 (of this invention) under a deliberate attack. The dark blue curve represents the node survival rate of network model M2 under a deliberate attack. The black curve represents the node survival rate of network model M3 under a deliberate attack. The green curve represents the node survival rate of network model M4 under a deliberate attack. The light blue curve represents the node survival rate of network model M5 under a deliberate attack. The pink curve represents the node survival rate of network model M6 under a deliberate attack.

[0089] Figure 12 In the diagram, the red curve represents the node survival rate of network model M1 (of this invention) under random attacks. The dark blue curve represents the node survival rate of network model M2 under random attacks. The black curve represents the node survival rate of network model M3 under random attacks. The green curve represents the node survival rate of network model M4 under random attacks. The light blue curve represents the node survival rate of network model M5 under random attacks. The pink curve represents the node survival rate of network model M6 under random attacks.

[0090] Figure 11 The curve results show that the network model M1 of this invention has the highest survival rate under deliberate attacks; Figure 12 The curve results show that the network model M1 of this invention also has the highest survival rate under random attacks, demonstrating the universality and robustness of the model design.

[0091] Depend on Figure 11 and Figure 12 It can be seen that, regardless of the intra-layer subnet construction method, the network models (M1, M2, M3) using the PRO strategy are more robust than the models (M4, M5, M6) using the DB strategy. In particular, the advantages of the PRO strategy are most obvious in the network model M1 of this invention, which confirms its good universality and synergistic gain effect with the reasonable subnet construction method.

[0092] exist Figure 13 and Figure 14 In the diagram, the X-axis represents the number of delete nodes, and the Y-axis represents network combatlink efficiency. Figure 13 In the diagram, the red curve represents the operational link efficiency of network model M1 under a deliberate attack. The dark blue curve represents the operational link efficiency of network model M2 under a deliberate attack. The black curve represents the operational link efficiency of network model M3 under a deliberate attack. The green curve represents the operational link efficiency of network model M4 under a deliberate attack. The light blue curve represents the operational link efficiency of network model M5 under a deliberate attack. The pink curve represents the operational link efficiency of network model M6 under a deliberate attack.

[0093] Figure 14 In the diagram, the red curve represents the operational link efficiency of network model M1 (of this invention) under random attacks. The dark blue curve represents the operational link efficiency of network model M2 under random attacks. The black curve represents the operational link efficiency of network model M3 under random attacks. The green curve represents the operational link efficiency of network model M4 under random attacks. The light blue curve represents the operational link efficiency of network model M5 under random attacks. The pink curve represents the operational link efficiency of network model M6 under random attacks.

[0094] Figure 13 The curve results show that the network model M1 of this invention has the highest link efficiency, indicating that it can effectively guarantee combat functions while maintaining topological stability. Figure 14 The curve results show that the network model M1 of this invention consistently achieves the highest link efficiency under random attacks, further demonstrating its advantages in resilience and functional synergy.

[0095] Depend on Figure 13 and Figure 14 It can be seen that the network model M1 of this invention consistently achieves the highest operational link efficiency. This indicates that the coupling strategy of this invention, combined with a reasonable intra-layer network optimization design, can maximize the synergistic effect and ensure that it can maintain high operational capability even when attacked.

[0096] In summary, the interlayer coupling strategy for a two-layer dependent command and control network proposed in this invention demonstrates higher node survivability and operational link efficiency compared to other methods under different attack strategies, different dependency strength adjustment parameters, different coupling strategies, and different network models. While meeting the multi-path information transmission characteristics and functional coordination requirements of command and control networks, it significantly enhances the network's resilience. This indicates that the method of this invention has significant advantages in reducing the risk of cascading failures and improving network robustness, providing important theoretical basis and practical reference for the optimized design and resilience research of command and control networks.

[0097] Example 2 This invention discloses a method for constructing an interlayer coupling strategy for a two-layer interdependent command and control network, the method comprising: The node traversal betweenness is defined, which quantifies the amount of information accumulated by a network node during multiple information traversals, and is based on a set. Represents all nodes The set of directly connected nodes, after passing through After the second information walk, traverse the set. Get Node The amount of information possessed is defined as the node traversal betweenness. Its calculation satisfies: .

[0098] in, For nodes The degree, As the initial amount of information, For nodes With nodes The connection between them , The maximum number of levels in the network; and the maximum number of iterations is reached within a preset limit. Or the maximum difference between two adjacent iterations is not greater than a preset threshold. Stop iteration when the time is right; Calculate the inter-layer node dependency strength based on node traversal betweenness, and construct the upper-layer subnet by considering all information traversal paths rather than just the shortest path. node With lower subnet node Inter-layer node dependence strength Its calculation satisfies: .

[0099] in, For subnet Middle node The node betweenness, For subnet Middle node The node traversal betweenness, and To adjust the parameters, the node betweenness to avoid edge nodes is set to... The situation; A node combat attribute matching degree model is established to evaluate cross-layer node collaboration capabilities. The node combat attribute matching degree model is calculated based on the node's own combat capabilities and the cooperation factor of collaborating nodes, where the calculation of the combat capabilities of upper-layer network nodes satisfies: .

[0100] in, For nodes Combat attribute weighted sum, used to quantify nodes The inherent combat potential or effectiveness of an independent combat unit, representing the node's own combat capability. For nodes A set of cooperative / adjacent collaborative nodes. For set Any node in the array.

[0101] The cooperation factor of the collaborative nodes satisfies: .

[0102] Collaborative node cooperation factor is used to characterize nodes With neighboring nodes The operational attribute vector difference degree is used to compensate for the capability blind spots of single nodes and represent the node collaborative combat capability; and the combat capability of the lower-layer network command and control node is calculated as the normalized product of communication physical capability and hierarchical effect. The calculation of the combat capability of the lower-layer network command and control node satisfies: .

[0103] in, For nodes The Shannon channel capacity is used to characterize its information throughput capability at the physical layer. Represents a node The level at which it is located This represents the maximum Shannon channel capacity across all nodes. This represents the maximum number of levels in the network.

[0104] Based on this, the inter-layer node combat attribute matching degree between the combat capabilities of upper-layer network nodes and the combat capabilities of lower-layer network command and control nodes is calculated. Its calculation satisfies: .

[0105] Among them, the matching index satisfy: .

[0106] For nodes With nodes The normalized value of the path length between them and satisfying: .

[0107] For nodes With nodes The path length between them.

[0108] By combining the inter-layer node dependency strength and combat attribute matching degree, an inter-layer coupling probability function including a layer adjustment factor is constructed. And based on the type of the upper-level node In the set of perception nodes With firepower node set Distinguish between them. Interlayer coupling probability function. Three-dimensional calculations satisfy: .

[0109] Where i represents the upper-layer network node, j represents the lower-layer network node, and N c For the set of accusation nodes, The maximum number of levels in the network. For the matching degree of combat attributes between inter-layer nodes, T is the hierarchical adjustment factor of the level where the command node j is located. s and T f These are sets of perception nodes and fire support nodes, respectively. The upper-layer network nodes... With lower-level network nodes The basic interlayer correlation between them is denoted as And satisfy .

[0110] Based on interlayer coupling probability function The roulette wheel algorithm is used to dynamically select coupling nodes to generate inter-layer coupling strategies. This involves initializing the network. Middle nodes form a queue And initialize the network Middle nodes form a queue From the queue in sequence Select the node that has not had a coupled edge constructed as the node. compute nodes With queue Each node and And based on this, we can obtain Based on Generate cumulative probability and combine it with random numbers Select the option that satisfies the condition that the cumulative probability is not less than 1 for the first time. Dependent nodes This generates an inter-layer coupling strategy and outputs a set of inter-layer coupling edges.

[0111] This invention also discloses a system for constructing interlayer coupling strategies in a two-layer interdependent command and control network. The system includes a processor 100, a non-transitory computer-readable storage medium 200, and a communication interface (input / output interface 300). The non-transitory computer-readable storage medium 200 stores a computer program. When executed by the processor 100, the computer program causes the processor 100 to implement the following first calculation modules 110 to fifth calculation modules 150 to generate interlayer coupling strategies: The first calculation module 110 is used to define the node traversal betweenness. The node traversal betweenness is quantified based on the amount of information accumulated by a network node during multiple information traversals, and is based on... Calculate the node traversal betweenness.

[0112] The second calculation module 120 is used to calculate the inter-layer node dependency strength based on the node walk betweenness, and the inter-layer node dependency strength is based on... Calculated and used to characterize the upper subnet. Nodes and lower-level subnets The basic interlayer correlation between nodes. The third calculation module 130 is used to establish a node combat attribute matching degree model to evaluate cross-layer node collaboration capabilities. The node combat attribute matching degree model is calculated based on the node's own combat capabilities and the cooperation factors of collaborating nodes, and also includes the calculation of the combat capabilities of upper-layer network nodes. Calculate the combat capabilities of lower-layer network command and control nodes. And calculate the matching degree of combat attributes between inter-layer nodes .

[0113] The fourth calculation module 140 is used to construct an inter-layer coupling probability function that includes a hierarchical adjustment factor by combining the inter-layer node dependency strength and the combat attribute matching degree. and according to or Choose whether to introduce a hierarchical adjustment factor. Perform weighted normalization.

[0114] The fifth calculation module 150 is used for calculations based on the inter-layer coupling probability function. The roulette wheel algorithm is used to dynamically select coupling nodes to generate inter-layer coupling strategies and output the set of inter-layer coupling edges.

[0115] In some embodiments, the two-layer interdependent command and control network inter-layer coupling strategy construction system is implemented as a network control device for command and control network orchestration and coupling strategy distribution, which is physically connected to the upper-layer subnet through input / output interfaces 300. Network equipment group and lower-level subnet Network device group connection, used to collect topology connection relationships Node degree ,bandwidth Signal-to-noise ratio and node level The system calculates and executes parameters such as the layer coupling edge set, and outputs the resulting set of inter-layer coupling edges as binding relationships or routing / forwarding configurations that can be executed by network devices. This makes the inter-layer coupling strategy a real change to the physical network control link and control message forwarding behavior, avoiding being understood as a purely abstract rule or mathematical method.

[0116] The network control device includes a processor 100, a non-transitory computer-readable storage medium 200, and an input / output interface 300. Optionally, it may include a hardware acceleration unit for accelerating matrix summation and cumulative probability comparison, and random number generation hardware for generating random numbers for roulette. The processor 100 can be a multi-core CPU, a network processor, or a SoC. The input / output interface 300 can be a physical transceiver interface such as Ethernet / optical port and supports network management / control protocols. The storage medium 200 is used to store executable instructions and parameters. , , and wait.

[0117] When the processor 100 executes instructions stored in the storage medium 200, it logically forms a functional division of labor between the first computing module 110 and the fifth computing module 150. These modules can be implemented by software instructions or by hardware acceleration units implementing some computing cores.

[0118] In the first computing module 110, the network control device calculates the collected connection relationships. With node degree Construct a set of nodes adjacent to each other in storage medium 200. The corresponding index structure, and the node traversal betweenness of each node calculated according to the following formula. Perform iterative calculations.

[0119] .

[0120] At the implementation level, the hardware acceleration unit can... The items are accumulated in parallel and written back to storage medium 200. Processor 100 monitors the maximum difference between two adjacent iterations. When the difference is not greater than a threshold, Or the number of iterations reaches the maximum number of layers in the network. The iteration is terminated at a certain time, thus quantifying the "accumulated information during multiple information walks" into an executable and measurable hardware decision process.

[0121] In the second computing module (120), the network control device will connect the upper subnet With lower subnet The node traversal betweennesses are respectively mapped to and The inter-layer node dependency strength is calculated according to the following formula. .

[0122] .

[0123] The inter-layer node dependency strength is used as the "inter-layer basic correlation" within the network control device. The weight input of "" participates in the construction of the subsequent coupling probability function, so its calculation result will affect the selection of the final set of coupling edges, thereby affecting the binding relationship and control link bearer distribution in the physical network.

[0124] In the third computing module (130), the network control device establishes a node combat attribute matching degree model to evaluate cross-layer node collaboration capabilities. For upper-layer nodes... Network control equipment is based on combat attribute vector components and their weights The combat capabilities of individual computing nodes are combined with the cooperative factors of collaborating nodes to form the combat capabilities of upper-layer network nodes: .

[0125] .

[0126] Lower-level command nodes The network control device directly collects or estimates data from the input / output interface 300. and And based on Shannon channel capacity and hierarchical effects Calculate the combat capabilities of lower-layer network command and control nodes: .

[0127] The network control device then calculates the matching index: .

[0128] Network control devices use path length normalized values Construct inter-layer node combat attribute matching degree: .

[0129] Among them, path length It can be generated by the lower layer network The routing table, link cost, or topology ranging calculations are used to ensure... It is related to actual communication reachability / cost, rather than an abstract metric detached from the physical network.

[0130] In the fourth computing module 140, the network control device is combined with and Constructing a hierarchical adjustment factor Interlayer coupling probability function And according to node type Belongs to the set of perception nodes or firepower node set Choose different weighted normalization forms: .

[0131] in, For the set of accusation nodes.

[0132] Through this branch design, the selection of fire node coupling will affect the hierarchical adjustment factor. More sensitive, thus tending to select command and control nodes at higher levels on the physical network to undertake key control / coordination tasks.

[0133] In the fifth computing module 150, the network control device is based on the inter-layer coupling probability function. The roulette wheel algorithm is executed to dynamically select coupled nodes to generate inter-layer coupling strategies. To conform to the original terminology and for feasibility, the network control device maintains queues in storage medium 200. and and during the traversal Select node At that time, compute nodes and Each candidate node Interlayer coupling probability function And form a cumulative probability sequence; random number hardware or pseudo-random number module generates it. Processor 100 or acceleration unit performs a comparison to select the one that satisfies the cumulative probability of not being less than 1 for the first time. Dependent nodes This generates interlayer coupling edges and updates the queue state.

[0134] Finally, the network control device outputs the interlayer coupling edge set and sends it to the network device through the input / output interface 300, so that the upper-layer node and the lower-layer command and control node can establish a binding relationship or control message forwarding strategy, and realize the implementation of the interlayer coupling strategy.

[0135] It should be noted that the specific embodiments described above are exemplary. Those skilled in the art can devise various solutions inspired by the disclosure of this invention, and these solutions all fall within the scope of this invention and its protection. Those skilled in the art should understand that this specification and its accompanying drawings are illustrative and not intended to limit the scope of the claims. The scope of protection of this invention is defined by the claims and their equivalents. This specification contains multiple inventive concepts; terms such as "preferredly," "according to a preferred embodiment," or "optionally" indicate that the corresponding paragraph discloses an independent concept. The applicant reserves the right to file divisional applications based on each inventive concept.

Claims

1. A method for constructing an interlayer coupling strategy for a two-layer interdependent command and control network, characterized in that, The method includes: Define a node traversal betweenness, which is quantified based on the amount of information accumulated by a network node during multiple information traversals; The inter-layer node dependency strength is calculated based on the node betweenness, wherein the inter-layer node dependency strength is positively correlated with the product of the betweennesses of the nodes in the two-layer network; Establish a node combat attribute matching degree model to evaluate cross-layer node collaboration capabilities; wherein, the node combat attribute matching degree model is calculated based on the node's own combat capabilities and the cooperation factors of collaborating nodes. By combining the inter-layer node dependency strength and the combat attribute matching degree, an inter-layer coupling probability function containing a layer adjustment factor is constructed. Based on the inter-layer coupling probability function, the roulette wheel algorithm is used to dynamically select coupling nodes to generate inter-layer coupling strategies.

2. The method according to claim 1, characterized in that, The steps for defining the node traversal betweenness include: Let set G represent the set of all nodes directly connected to node i. Considering the multipath transmission characteristics of node information, after n information walks, traverse set G to obtain the amount of information possessed by node i. The amount of information is defined as the node walk betweenness. The formula for calculating the node's wander betweenness is: ； in, For the node after the nth information walk The amount of information possessed; For nodes The degree; This represents the initial amount of information. This represents the connection relationship between node i and node j; D represents the maximum number of levels in the network. , The node is directly connected to node i.

3. The method according to claim 1 or 2, characterized in that, The steps for calculating the inter-node dependency strength based on the node betweenness number include: Considering all paths of information travel rather than just the shortest path, the formula for calculating the inter-layer node dependency strength is: ； Among them, is the node betweenness centrality of node i in subnet A, is the node betweenness centrality of node j in subnet B; a(0 < a < 1) is a regulation parameter to avoid the situation where the node betweenness centrality of some edge nodes is 0.

4. The method according to any one of claims 1 to 3, characterized in that, The steps for establishing a node combat attribute matching degree model and evaluating cross-layer node collaboration capabilities include: Considering the need for node-based collaborative operations, the combat capabilities of upper-layer network nodes are calculated. Calculate the combat capabilities of lower-layer network command and control nodes; Calculate the inter-layer node combat attribute matching degree between the combat capabilities of upper-layer network nodes and the combat capabilities of lower-layer network command and control nodes.

5. The method according to any one of claims 1 to 4, characterized in that, In the step of establishing a node combat attribute matching degree model and evaluating cross-layer node collaborative capabilities, the calculation formula for the combat capability of upper-layer network nodes is as follows: ； in, The weighted sum of the combat attributes of node i quantifies the inherent combat potential or effectiveness of node i as an independent combat unit, representing the node's own combat capability. Let be the cooperation factor between node i and its cooperating nodes, representing the difference in attribute vectors between node i and its neighboring nodes. This factor compensates for the blind spots in the capabilities of a single node and represents the collaborative combat capability of the nodes. Let i be the set of cooperating / adjacent collaborative nodes. Let i be any node in the set of cooperating / adjacent collaborative nodes of node i.

6. The method according to any one of claims 1 to 5, characterized in that, In the step of establishing a node combat attribute matching degree model and evaluating cross-layer node collaboration capability, the combat capability of the lower-layer network command and control node is calculated. The combat capability of lower-layer network command and control nodes is the normalized product of communication physical capabilities and hierarchical effects, and its calculation formula is as follows: ； in Let H(i) be the Shannon channel capacity of node i, representing its information throughput capability at the physical layer; H(i) represents the layer where node i is located. CP max D represents the maximum Shannon channel capacity among all nodes; D is the maximum number of network levels.

7. The method according to any one of claims 1 to 6, characterized in that, In the step of establishing a node combat attribute matching degree model and evaluating cross-layer node collaboration capabilities, the formula for calculating the inter-layer node combat attribute matching degree is as follows: ； in, Let be the matching degree index between node i and node j. This is the normalized value of the path length between node i and node j.

8. The method according to any one of claims 1 to 7, characterized in that, The calculation formula for constructing the interlayer coupling probability function, which combines the interlayer node dependency strength and the combat attribute matching degree, and includes a hierarchical adjustment factor, includes: ； Where i is the upper-layer network node, j is the lower-layer network node, and N is the lower-layer network node. c For the set of accusation nodes, The maximum number of levels in the network. Let be the basic inter-layer correlation degree between upper-layer network node i and lower-layer node j. H(j) represents the matching degree of combat attributes between nodes at different levels, H(j) represents the level adjustment factor of the level where command and control node j is located, and T represents the matching degree of combat attributes between nodes at different levels. s and T f These are sets of sensing nodes and firepower nodes, respectively.

9. The method according to any one of claims 1 to 8, characterized in that, The step of dynamically selecting coupling nodes based on the inter-layer coupling probability function and using the roulette wheel algorithm to generate the inter-layer coupling strategy includes: Initialize the nodes in network A and form a queue Q. A ; Initialize the nodes in network B to form queue Q. B ; Calculate the node traversal betweenness of each node; From queue Q in sequence A Select the node that has not formed a coupled edge, and denote it as node i; if queue Q A Traversal complete, end; Compute node i and queue Q B The interdependence strength of each node and the matching degree of node combat attributes; Determine the type of node i, and calculate the relationship between node i and queue Q. B The probability of dependency between nodes is used to select dependent node j using the roulette wheel method; Select the neighboring nodes of node i and its dependent node j, and denote them as Q. i and Q j ; Remove node j from queue Q B Delete; if queue Q B If the current state is empty, reinitialize the nodes in network B to form queue Q. B ; Queue Q i and Q j As a local world, the interdependence strength and combat attribute matching degree of each node are calculated, and then the relationship between node i and Q is calculated. B The probability of dependency between nodes is used to select dependent node j using the roulette wheel method; this continues until queue Q is reached. i After traversal is complete, continue from queue Q. A Select the node that has not formed a coupled edge, and denote it as node i; if queue Q A Traversal complete, end.

10. A system for constructing a two-layer interdependent command and control network interlayer coupling strategy, characterized in that, The system includes a processor (100), the processor (100) comprising: The first calculation module (110) defines the node traversal betweenness, which is quantified based on the amount of information accumulated by the network node during multiple information traversals. The second calculation module (120) calculates the inter-layer node dependency strength based on the node betweenness, wherein the inter-layer node dependency strength is positively correlated with the product of the betweennesses of the nodes in the two-layer network; The third calculation module (130) establishes a node combat attribute matching degree model to evaluate the cross-layer node collaboration capability; wherein, the node combat attribute matching degree model is calculated based on the node's own combat capability and the collaboration factor of the collaborating node. The fourth calculation module (140) combines the inter-layer node dependency strength and the combat attribute matching degree to construct an inter-layer coupling probability function containing a hierarchical adjustment factor; The fifth calculation module (150) uses the roulette wheel algorithm to dynamically select coupling nodes based on the inter-layer coupling probability function to generate inter-layer coupling strategies.

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