CN-119739135-B - Command control system security risk assessment method based on linear influence

CN119739135BCN 119739135 BCN119739135 BCN 119739135BCN-119739135-B

Abstract

The invention discloses a command control system security risk assessment method based on linear influence. The method comprises the steps of firstly establishing a command control system physical connection diagram and an information flow diagram, forming a causal diagram base diagram through cross-layer aggregation of the base diagram, eliminating non-causal relation in the base diagram in a form of a spanning tree from a direction opposite to the direction of conduction, establishing an index extraction system, decomposing resource nodes in the causal diagram into index nodes, carrying out input normalization and data interpolation processing on input system operation original data, calculating a linear relation from an abnormal node to a child node on each path through least square estimation, traversing all paths subsequent to the abnormal node, calculating the influence degree of the abnormal node on each path, and outputting the influence degree, an influence domain range and an alarm grade of the abnormal node on the subsequent node. The method can effectively calculate the influence results of the abnormal nodes under different risk scenes, and improves the reliability and safety of the command control system.

Inventors

WANG RUI
ZHANG XINJUN
YANG JING
Hu Runchang
LU DONG
LV MING
ZHANG JIE

Assignees

南京理工大学

Dates

Publication Date: 20260512
Application Date: 20241128

Claims (7)

1. The command control system security risk assessment method based on linear influence is characterized by comprising the following steps: S1, establishing a command control system physical connection diagram and an information flow diagram, and performing base diagram cross-layer aggregation to form a causal diagram base diagram; s2, eliminating non-causal relation in the base map from opposite direction of the motion factor conduction in a form of a spanning tree to obtain a causal map; s3, establishing an index extraction system, and decomposing the resource nodes in the causal graph into index nodes; s4, carrying out input normalization processing on the input operation original data of the command control system; S5, carrying out data interpolation on the data processed in the step S4; S6, calculating the linear relation from the abnormal node to the child node on each path through least square estimation, wherein the implementation method is as follows: when the relationship between two index nodes is a linear relationship y=ax+b, the measurement data can be expressed as follows: Y=Hx+e Wherein: Wherein { x 0 ,x 1 ,…,x n } and { y 0 ,y 1 ,…,y n } are respectively the sampling data of two index nodes, and { e 0 ,e 1 ,…,e n } is process noise; The i-th measurement residual epsilon 1 is: Wherein Y i is the sampling data of the ith index node, and H i is the ith row of the linear relation coefficient matrix; The sum J of all measurement errors is: Let J pair The first order partial derivative of (2) is equal to 0, and the optimal estimated value can be calculated The following formula is adopted for calculation: s7, traversing all subsequent paths of the abnormal node, and calculating the influence degree of the abnormal node on each path, wherein the calculation method comprises the following steps: The existence of a fault propagation path a→b→c→n, the linear relationship of a to B is y=a 1 x+b 1 , the linear relationship of a to C is y=a 2 x+b 2 , and the linear relationship of a to the nth child node is y=a n x+b n ; The step of calculating the influence degree of the abnormal node on the path is as follows: The absolute values of the linear coefficients of the outlier node to all children nodes on the path are summed using the following formula: a sum ＝|a 1 |+|a 2 |+…+|a n | Wherein n is the total number of child nodes, and a i represents the influence weight of the abnormal node on the ith child node; Mapping a sum to between 0 and 1 using the following activation function yields the corresponding degree of influence Inf val : and S8, outputting the influence degree and the influence domain range of the abnormal node on the subsequent node according to the calculation result in the step S7, and obtaining the current alarm level of the command control system.
2. The method for evaluating the security risk of the command control system based on the linear influence according to claim 1, wherein in the step S1, the step of establishing the physical connection graph comprises the steps of determining each subsystem in the command control system as a node in the graph, identifying physical connection media among the subsystems, determining the physical connection relationship among the subsystems, and representing the physical connection relationship among the subsystems through the connection among the nodes; The information flow graph establishing step includes determining each subsystem in the command control system as a node in the graph, analyzing the data flow direction among the subsystems, determining the propagation path of data from one subsystem to the other subsystem, and representing the data through the connection of the edges among the nodes in the graph; The base map cross-layer aggregation implementation step comprises the steps of corresponding all subsystem nodes of a physical connection map and an information flow map, combining the physical connection map and the information flow map, and reserving connection relations and directions between the nodes in the two maps to obtain a causal map base map.
3. The security risk assessment method of command control system based on linear influence according to claim 1, wherein in the step S2, in the causal graph base graph generated in the step S1, nodes which are not affected due to opposite conduction directions are eliminated through a spanning tree form, firstly, an abnormal node set is generated, the set comprises abnormal nodes marked due to faults, errors or abnormal behaviors, nodes in the set are sequentially selected to be used as current processing nodes, if the node is not in a tabu list, a neighbor resource node set which is directly connected with the node is generated, one neighbor resource node is selected from the neighbor resource node sets, whether the neighbor resource node is a father node of the current node is checked, if yes, the neighbor resource node is added as a neighbor node, connection is established, if no other neighbor resource node is selected until all neighbor nodes are traversed, the process is repeated until all the abnormal nodes are processed, and finally, the processed causal graph is output.
4. The method for evaluating the security risk of a command control system based on linear influence according to claim 1, wherein in the step S3, the index extraction is to decompose the resource nodes in the causal graph into index nodes capable of being quantitatively described, state parameters capable of representing the working performance of the nodes are selected as extracted index nodes according to specific resource nodes, an index extraction system is established, the established index extraction system is divided into two parts, namely, intra-resource index extraction and inter-resource index extraction, the intra-resource index extraction comprises message output density, memory utilization rate, processing time delay and other indexes capable of representing the internal state parameters of the resources, the inter-resource index extraction comprises access message density, output target number, access target number and other indexes capable of representing the state parameters of the resources, and according to the established index extraction system, extraction indexes corresponding to all the resource nodes in the system are determined, and each resource node is decomposed into index nodes.
5. The security risk assessment method of a command control system based on linear influence as set forth in claim 1, wherein in the step S4, according to the index nodes obtained by decomposition in the step S3, state data corresponding to each index node during normal operation of the command control system is collected as original data, and the original data is processed by adopting a linear normalization processing method, and the formula is as follows: Where x is the raw data value, x max and x min are the maximum and minimum values, respectively, in the raw data, and x norm is the normalized data value.
6. The security risk assessment method of command control system based on linear influence according to claim 1, wherein in step S5, the data obtained in step S4 is processed by adopting data interpolation, and the formula is as follows: where t i denotes the time point of interpolation, t start and t end denote the start and end time points of the interpolation interval respectively, Representing the data value at time t i after interpolation, And The data values at times t start and t end are shown, respectively.
7. The security risk assessment method of command control system based on linear influence of claim 1, wherein in step S8, the propagation path with the greatest influence degree is obtained by comparing the influence degree of the abnormal node on all subsequent paths obtained in step S7, the resource set of the node on the path is the influence domain range of the abnormal node, the maximum influence degree obtained in step S7 is compared with a preset threshold, and the current alarm grade Inf lv of the command control system is classified into three grades of slight, general and serious according to the following formula:

Description

Command control system security risk assessment method based on linear influence Technical Field The invention relates to the technical field of security risk assessment of command control systems, in particular to a command control system security risk assessment method based on linear influence. Background With the rapid development of robot technology, robot clusters are increasingly widely applied in various fields, such as industrial production, logistics distribution, environmental monitoring and the like. However, the complexity of cooperation and management of the robot clusters increases, and especially when the number of robots is huge and the task environment is complex, the stability and safety of the robot cluster command control system become key factors for determining whether the system can operate effectively. The existing robot cluster command control system generally adopts a multi-level and multi-node structural design, and covers different functional modules and robot units, and the modules and the units work cooperatively through a complex communication network. In this process, the system is susceptible to various external or internal risk factors, such as communication failures, hardware damages, software vulnerabilities, environmental disturbances, human operational errors, etc., due to the openness, distribution, and interactivity of the system. In order to reduce potential risks, reduce system defects and improve system robustness, safety risk assessment has become a key means, and has great significance for reliability of a robot cluster command control system in future automation tasks. In the prior art, a method for carrying out security risk assessment on a robot cluster command control system is mostly dependent on qualitative analysis and experience judgment, and lacks means for accurately quantifying risks. In addition, existing evaluation systems often ignore dynamic interactions among components within the system, as well as real-time response capabilities in the event of anomalies. This results in difficulty in accurately predicting and evaluating the potential impact of the system when it suffers from interference or failure in practical applications, thereby affecting the scientificity of the command decision and the pertinence of system optimization. Disclosure of Invention Aiming at the defects of the technology, the invention provides a command control system security risk assessment method based on linear influence, and by constructing a dynamic task influence estimation model, the robot cluster command control system can be subjected to real-time security risk assessment under different dangerous scenes, so that a more accurate and real-time risk assessment tool is provided for the robot cluster command control system. The technical scheme for solving the technical problems is that the command control system security risk assessment method based on linear influence comprises the following steps: S1, establishing a command control system physical connection diagram and an information flow diagram, and performing base diagram cross-layer aggregation to form a causal diagram base diagram; S2, eliminating non-causal relation in the base map from opposite direction of the motion factor conduction in the form of a spanning tree to obtain a causal map; s3, establishing an index extraction system, and decomposing the resource nodes in the causal graph into index nodes; s4, carrying out input normalization processing on the input operation original data of the command control system; S5, carrying out data interpolation on the data processed in the step S4; S6, calculating the linear relation from the abnormal node to the child node on each path through least square estimation; s7, traversing all subsequent paths of the abnormal node, and calculating the influence degree of the abnormal node on each path; and S8, outputting the influence degree and the influence domain range of the abnormal node on the subsequent node according to the calculation result in the step S7, and obtaining the alarm level of the command control system. Further, in the step S1, the step of establishing the physical connection graph comprises the steps of determining each subsystem in the command control system as a node in the graph, identifying physical connection media among the subsystems, determining the physical connection relationship among the subsystems, and representing the physical connection relationship by the connection of edges among the nodes in the graph; The information flow graph establishing step includes determining each subsystem in the command control system as a node in the graph, analyzing the data flow direction among the subsystems, determining the propagation path of data from one subsystem to the other subsystem, and representing the data through the connection of the edges among the nodes in the graph; The base map cross-layer aggregation implementation step comprises t