CN-122021701-A - Unmanned system toughness analysis and promotion method based on multilevel stream betweenness and adjacent entropy

CN122021701ACN 122021701 ACN122021701 ACN 122021701ACN-122021701-A

Abstract

The invention discloses a multi-level stream medium number and adjacent entropy-based unmanned system toughness analysis and promotion method, which comprises the steps of firstly constructing a function dependent network model to accurately describe the dependency relationship between each functional layer of a target detected, controlled and executed in the unmanned system, then providing a multi-level stream medium number-adjacent information entropy fusion algorithm, accurately identifying key nodes in the system by comprehensively calculating multi-level stream medium numbers, interlayer entropy, layers internal entropy and cross-layer connection coefficients of nodes, and finally constructing a recovery-oriented toughness enhancement framework, wherein the framework adopts a Lagrange damping non-smooth Newton method to optimally allocate protection resources to strengthen the key nodes in a resistance stage, and adopts a network block decomposition method to dynamically reconstruct a damaged network in a recovery stage so as to quickly recover task capacity. The method can comprehensively and accurately identify the key nodes and systematically improve the overall toughness of the unmanned system under disturbance resistance.

Inventors

CHEN ZHIWEI
HU RENJIE
LIU SIYANG
Zhang Luogeng
ZHANG YULU
MA ZIRUI
WU BEI

Assignees

西北工业大学

Dates

Publication Date: 20260512
Application Date: 20260126

Claims (10)

1. The unmanned system toughness analysis and promotion method based on multi-level stream betweenness and adjacent entropy is characterized by comprising the following steps: Step S1, constructing a function dependent network model of the unmanned system; Step S2, calculating comprehensive centrality of the multi-layer stream betweenness and adjacent information entropy of the fusion nodes based on the function dependent network model to identify key nodes; And step S3, reinforcing the unmanned system by adopting a toughness reinforcing frame resisting recovery guidance based on the key node identification result, wherein the frame comprises a resource allocation strategy based on Lagrange damping non-smooth Newton method in a resisting stage and a dynamic reconstruction strategy based on network block decomposition in a recovery stage.
2. The method for analyzing and improving toughness of an unmanned system based on multilevel streaming media and adjacency entropy according to claim 1, wherein in step S1, constructing a function-dependent network model of the unmanned system specifically comprises: Step S1.1, detecting nodes in the unmanned system Control node Execution node And a target node Respectively constructing independent network layers; Step S1.2, constructing a directed edge between the nodes, wherein the directed edge comprises an intra-layer edge and an inter-layer edge, and the inter-layer edge at least comprises 、、 And Four types, the inner layer includes at least 、 And Three types.
3. The unmanned system toughness analysis and promotion method based on multi-level stream betweenness and adjacency entropy according to claim 1, wherein in step S2, the specific process of identifying key nodes is as follows: Step S2.1, defining and calculating the multi-layer stream betweenness of the node; Step S2.2, respectively calculating the inter-layer entropy and the intra-layer entropy of the node based on the multi-layer stream betweenness, wherein the inter-layer entropy is used for quantifying the distribution balance of the node and the upper and lower layer neighbor information interaction, and the intra-layer entropy is used for quantifying the distribution balance of the node and the same layer neighbor cooperative cooperation; s2.3, calculating a cross-layer connection coefficient of the node, wherein the cross-layer connection coefficient comprehensively reflects the instruction receiving and issuing capability of the node in the vertical direction and the cooperative capability in the horizontal direction; s2.4, carrying out normalization processing on the interlayer entropy and the intra-layer entropy, and applying a gain function to the normalized intra-layer entropy to amplify the represented synergistic effect to obtain a synergistic gain coefficient; And step S2.5, obtaining the final comprehensive centrality of the node through a comprehensive calculation function based on the normalized interlayer entropy, the cooperative gain coefficient and the cross-layer connection coefficient.
4. The unmanned system toughness analysis and promotion method based on multi-level stream betweenness and adjacent entropy according to claim 3, wherein in the step S2.1, the multi-level stream betweenness of the node is calculated by setting the maximum propagation steps related to the network layer number, initializing each node sequentially as an information source, simulating a multi-step propagation process that information is uniformly distributed to all outgoing neighbor nodes by the node carrying the information in each step, counting the total amount of information received by each node after all propagation ends, and calculating the ratio of the total amount of information to the total amount of initial information of the whole network as the multi-level stream betweenness of the node.
5. The unmanned system toughness analysis and promotion method based on multi-level stream betweenness and adjacency entropy according to claim 3, wherein in the step S2.2, the calculation method of the inter-layer entropy is as follows: Wherein the method comprises the steps of Is the weight adjustment coefficient of the center-in-out property, And The calculation method of the layer internal entropy is as follows: Wherein the method comprises the steps of Probability is distributed for intra-layer collaborative information.
6. The unmanned system toughness analysis and promotion method based on multi-level stream betweenness and adjacency entropy according to claim 3, wherein in step S2.3, the cross-layer connection coefficients of nodes are The calculation method of (1) is as follows: Wherein, the 、 And The number of neighbors of the upper layer and the lower layer and the same layer respectively; is the network maximum and is used for normalization; And Is a weight coefficient and ; The function is used to prevent numerical saturation.
7. The unmanned system toughness analysis and improvement method based on multi-level stream betweenness and adjacency entropy according to claim 3, wherein in said step S2.4, said collaborative gain coefficient The calculation method of (1) is as follows: Wherein the method comprises the steps of For the normalization layer internal entropy, As a function of gain.
8. The unmanned system toughness analysis and improvement method based on multi-level flow betweenness and adjacency entropy according to claim 3, wherein in the step S2.5, the comprehensive calculation function is: Wherein, the For the normalized inter-layer entropy, As a result of the co-gain factor, Is a cross-layer connection coefficient.
9. The unmanned system toughness analysis and promotion method based on multi-level stream betweenness and adjacency entropy according to claim 1 is characterized in that in the step S3, the resource allocation strategy based on the Lagrange damping non-smooth Newton method in the resistance stage is specifically that an optimization model which aims at maximizing network expected performance after being attacked and takes total protection resources as constraints is established, the Lagrange multiplier method is adopted for processing the constraints and constructing a Lagrange function, and the damping non-smooth Newton method is utilized for iteratively solving an optimal solution meeting KKT conditions to obtain an optimal resource allocation scheme for key nodes.
10. The unmanned system toughness analysis and promotion method based on multi-level stream betweenness and adjacency entropy according to claim 1, wherein in the step S3, the dynamic reconstruction strategy based on network block decomposition in the recovery stage specifically comprises: block deconstructing, identifying and removing failure nodes and associated edges thereof; block reconstruction, namely scheduling available nodes in the redundant network blocks to damaged network blocks; and when block reconstruction is not feasible, sorting and merging damaged blocks according to the target value, and realizing task redistribution.

Description

Unmanned system toughness analysis and promotion method based on multilevel stream betweenness and adjacent entropy Technical Field The invention belongs to the technical field of unmanned systems and complex networks, and particularly relates to an unmanned system toughness analysis and promotion method based on multilevel stream betweenness and adjacent entropy. Background Along with the continuous improvement of the intelligent and clustering degree of the unmanned system, the unmanned system (USoS) has become a core force for executing complex tasks such as rescue and the like. The system forms a closed loop 'detection-control-execution-evaluation' task loop through the function coordination of the heterogeneous unmanned platform so as to realize the task target. However, USoS is very susceptible to multi-source disturbance such as interference in a complex countermeasure environment, so that a key node is invalid, a task loop is interrupted, and the overall task execution efficiency of the system is seriously weakened. Therefore, how to accurately identify key nodes for maintaining the system function and effectively improve the toughness recovery capability of the system after being disturbed on the basis is a key point for ensuring USoS to run continuously and reliably. Currently, some researches on key node identification and toughness improvement of USoS still have obvious limitations: In the aspect of key node identification, the existing method is mostly based on traditional network centrality indexes such as centrality, betweenness centrality, proximity centrality and the like. These methods rely mainly on the static topology of the network and fail to fully combine the dynamic information flow characteristics of nodes in a multi-layer functional network and cross-level functional dependencies. In USoS such systems with obvious functional layers (such as a probe layer, a control layer, an execution layer and a target layer), the importance of a node is not only dependent on the number of connections, but also closely related to the capability of the node in information transfer, instruction transfer and collaboration among different layers. The traditional single topology index is difficult to comprehensively describe the function roles and dynamic contributions of the nodes in the multi-layer network, so that the identification result is not accurate enough, and key nodes with decisive effect on the task loop integrity are often missed or misjudged. In terms of toughness promotion, most of the existing strategies fracture node-level reinforcement (protection resource allocation) and network-level reconstruction (topology restoration), and lack an integrated toughness enhancement framework. In the recovery stage, the existing reconstruction strategy focuses on local connectivity recovery in a multi-way, and the quick reconstruction requirement of a complex task loop structure formed by multi-class functional nodes in USoS is ignored, so that the system can be slowly recovered after suffering from multi-node failure, and even can not be recovered to a state capable of executing basic tasks. In summary, the current field lacks a key node identification method capable of systematically fusing the multi-layer network topology, dynamic information flow and functional cooperation relationship, and lacks a toughness enhancement framework for organically integrating active protection and dynamic recovery, so that the further improvement of the survivability and self-adaptive recovery capability of USoS in a complex countermeasure environment is restricted. Therefore, a more comprehensive, precise and efficient method for analyzing and enhancing toughness is needed to support the flexible design and resource optimization configuration of USoS in uncertain disturbance environments. Disclosure of Invention Aiming at the problems that the performance retention and recovery capability of an unmanned system under multiple disturbance are difficult to accurately quantify and the function dependency structure and dynamic elasticity of the unmanned system are difficult to comprehensively describe in the prior art, the invention provides an unmanned system toughness analysis and promotion method based on multilayer flow betweenness and adjacent entropy, and the integral recovery-resistant enhancement framework is constructed by fusing the multilayer network topology structure and the dynamic information flow characteristics, so that the overall toughness of the unmanned system under the complex disturbance environment is remarkably promoted. The conception and principle of the invention are as follows: First, a Function Dependent Network (FDN) model is built for the functional heterogeneity of USoS to accurately describe the interactions between the "probe-control-execution-target" functional layers. Then, considering the multidimensional characterization of node criticality, fusing topological structure and info