A method for identifying key fault nodes of a ship electronic information system

Abstract

The application discloses a kind of warship electronic information system key fault node identification method, belong to warship fault identification technical field.The present application is to solve the problem that prior art lacks the means for automatically identifying the key fault node of warship electronic information system, the following method is proposed: S100 establishes functional dependence network model, the complex dependence relationship between systems is visualized, and each node is given the performance value;S200 assesses the dependence relationship between nodes by expert scoring method, and assigns weights to them to determine the contribution size to system task;S300 combines the results of the previous two steps, uses the weighted matrix to calculate the node performance, and sorts and identifies the key node.The present application can accurately identify the key fault point, and implement pre-monitoring and regular maintenance on the equipment associated with these key points to ensure the stability and reliability of the system as a whole.

Description

Technical Field

[0001] This invention relates to a method for identifying key fault nodes in a ship's electronic information system, belonging to the field of ship fault identification technology. Background Technology

[0002] The FDNA method models the system and the complex dependencies between systems within it using a Functional Dependency Network (FDN) model. It then analyzes performance using dependency parameters and piecewise linear functions. An FDN is a directed graph where nodes represent systems or functions within the system, and edges represent dependencies between nodes. Based on the von Neumann-Morgenstein (vNM) properties of additivity, monotonically increasing utility, and diminishing marginal utility, FDNA transforms the performance measure of system functions into the operability level of nodes, i.e., the performance of each node.

[0003] Expert scoring, commonly known as the Delphi method, is a widely used expert consultation technique in the decision-making process. This method involves anonymously soliciting opinions from experts in the relevant field, then statistically processing, analyzing, and summarizing these opinions to objectively synthesize the experience and subjective judgments of the majority of experts. It is particularly suitable for complex problems that are difficult to quantify using traditional techniques, such as risk assessment of emerging technologies and forecasting future market trends. The core of expert scoring lies in gradually guiding the expert group to reach a consensus on a particular issue through multiple rounds of opinion solicitation, feedback, and adjustments.

[0004] The new generation of shipboard electronic information systems (ESIS) is characterized by "resource element sharing, software and hardware separation, and dynamic mission reconfiguration." It is a complex system with numerous constituent units. During mission execution, the interactions between these units are complex, exhibiting numerous nonlinear behaviors. Furthermore, the hardware and software are flexibly configured, allowing for dynamic reconfiguration based on mission and state. In the complex maritime environment, failures in any component of the shipboard EIS can cause the system to exhibit characteristics such as "changes in equipment function, uncertain failure correlations, and dynamic system structure," impacting the overall system's mission execution capability and reliability. With the continuous upgrading and development of shipboard EIS, assessing its mission reliability has become a crucial and challenging aspect of ship design, construction, and maintenance. Therefore, there is an urgent need for a critical fault node identification method for shipboard EIS systems with new architectures, enabling the identification and ranking of critical fault nodes to meet the reliability requirements of shipboard EIS systems. Summary of the Invention

[0005] This invention proposes a method for identifying critical fault nodes in shipboard electronic information systems. Addressing the current practice of relying primarily on manual judgment or prior system analysis for critical fault node identification, with limited consideration for automated analysis methods such as information transmission patterns, this invention utilizes Functional Dependency Network Analysis (FDNA) to establish a functional dependency network for the shipboard electronic information system based on its information transmission mechanism. A dependency matrix is ​​then constructed using this functional dependency network and node numbers. Expert scoring is employed to assign different contributions to the functional dependencies between different nodes, thus measuring the contribution of each network node to the overall task of the shipboard electronic information system. Finally, the system effectiveness value of each node is calculated using the weighted functional dependency matrix and ranked to identify the critical nodes of the shipboard electronic information system. This allows for proactive monitoring and maintenance of the equipment associated with these nodes, ensuring the overall safety and reliability of the system, thereby resolving the problems existing in current technologies.

[0006] A method for identifying critical fault nodes in a ship's electronic information system includes the following steps:

[0007] S100, Function Dependency Network Construction: The system and the complex dependencies between systems in the system are modeled through the function dependency network model, and the performance measurement of system functions is converted into the operability level of nodes, that is, the effectiveness of nodes.

[0008] S200. Calculation of Functional Dependency Network Weights Based on Expert Scoring: The expert scoring method is used to give different contributions to the functional dependency relationships between different nodes in order to measure the contribution of different network nodes to the overall mission of the ship's electronic information system.

[0009] S300, System Node Performance Calculation and Key Node Ranking: Based on the results obtained from S100 and S200, the system performance value of each node is calculated and ranked through a weighted functional dependency matrix to obtain the key nodes of the ship's electronic information system, and the equipment referred to by the nodes is monitored and maintained in advance.

[0010] Furthermore, S100 includes the following steps:

[0011] S110. Using the Functional Dependency Network (FDN) model, model the complex dependencies between systems in the system and create a directed graph, i.e., the FDN model, where nodes represent systems or functions in the system and edges represent dependencies between nodes.

[0012] S120. Based on the von Neumann-Morgenstein utility theory, the performance measurement of system function is converted into the operability level of nodes, that is, the efficiency of nodes, thereby assigning an efficiency value to each node.

[0013] Furthermore, in S100, according to system function, the node types in the network include: sensing node P, network node N, computing / display and control hardware node H, display and control node S, and weapon node W.

[0014] Furthermore, the node connection rules and descriptions are as follows:

[0015] P→N: The sensing node sends information to the network node;

[0016] N→H: Network nodes send the information that needs to be processed to computing / display / control hardware nodes;

[0017] H→N: Computational / display control hardware nodes send instructions or processed information to network nodes;

[0018] H→S: The computing / display control hardware node sends the information to be computed to the display control software node;

[0019] S→H: The display and control software node sends the calculated information to the calculation / display and control software node;

[0020] S→S: Display and control software nodes transmit information to the next software node through network nodes;

[0021] N→W: Network nodes transmit attack information to weapon nodes;

[0022] Based on the node connection rules and descriptions, the various components of the ship's electronic information system are classified as FDNA nodes, among which,

[0023] P includes: sensing devices;

[0024] N includes: integrated network equipment;

[0025] H includes: public computing equipment and public display and control equipment;

[0026] S includes: target recognition software, trajectory generation software, threat assessment software, friend or foe identification software, situation display software, weapon allocation software, and target command and control software;

[0027] W includes weaponry equipment.

[0028] Furthermore, S200 includes the following steps:

[0029] S210. Based on the nodes and edges in the FDN model, design the Dependency Strength (SOD) and Dependency Criterion (COD) parameters. The Dependency Strength (SOD) and Dependency Criterion (COD) parameters are used to quantify the dependency relationships between nodes.

[0030] S220. Through expert scoring, the dependency functions between nodes are quantitatively evaluated to obtain scoring results. A judgment matrix is ​​constructed to obtain the weight of each node's contribution to the total system task.

[0031] S230. Utilize expert scoring results to quantitatively evaluate the dependency functions between nodes and assign differentiated weights.

[0032] Furthermore, in S300, the following steps are included:

[0033] S310. Based on the FDN model established in S100 and the weights calculated in S200, construct the relationship dependency matrix of the ship's electronic information system, including the SOD matrix and the COD matrix.

[0034] S320. Using the weights obtained in S200, the efficiency values ​​of the final leaf nodes of the system are calculated through the dependency matrix, SOD matrix, and COD matrix.

[0035] S330. By analyzing the impact of each node failure on the final performance value, i.e., calculating the decrease in performance value after node failure, key nodes can be identified.

[0036] S340. Sort all nodes according to the calculated performance drop, identify the key nodes that have the greatest impact on system performance, and implement pre-monitoring and regular maintenance for these nodes to improve the overall stability and reliability of the system.

[0037] Furthermore, in S310, a dependency matrix C representing the relationships between various nodes of the ship's electronic information system is constructed based on the FDNA network. n×n As shown in the following formula:

[0038]

[0039] Similarly, the dependency parameter matrix, SOD matrix A, can be obtained. n×n and COD matrix B n×n As shown in the following formula:

[0040]

[0041] Furthermore, in S330, the performance value of each node is assumed to be reduced to 50% of its original value, and the difference between the current system performance value and the performance value when all nodes are effective is calculated. The greater the decrease in node performance, the greater and more critical the impact of that node on the system performance.

[0042] A storage medium storing a computer program, which, when executed by a processor, implements the aforementioned method for identifying critical fault nodes in a ship's electronic information system.

[0043] A computer device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the above-described method for identifying critical fault nodes in a ship's electronic information system.

[0044] The beneficial effects of this invention are as follows: This invention presents a method for identifying critical fault nodes in ship electronic information systems. Currently, the identification of critical fault points in ship electronic information systems mainly relies on manual judgment or pre-assessment, and often neglects the use of automated information flow analysis. This invention utilizes FDNA (Functional Dependency Network) to analyze the information transmission paths of ship electronic information systems and constructs a functional dependency network. Within this network framework, a dependency association matrix is ​​formed by combining node numbers. This strategy further adopts an expert scoring method to quantitatively evaluate the dependent functions between nodes, assigning differentiated weights to determine the relative importance of each node to the overall system task. By considering the weighted dependency association matrix, the system performance indicators of each node are calculated and ranked, thereby accurately identifying critical fault points. Pre-monitoring and regular maintenance are implemented on the equipment associated with these critical points to ensure the overall stability and reliability of the system. Currently, this method has been verified on typical ship electronic information systems and can solve the problem of critical node identification in new architecture ship electronic information systems. Attached Figure Description

[0045] Figure 1 This is an example of a function-dependent network model;

[0046] Figure 2 A flowchart of the ship's electronic information system for air defense missions;

[0047] Figure 3 The network is a functional dependency of the ship's electronic information system for air defense missions. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] A method for identifying critical fault nodes in a ship's electronic information system includes the following steps:

[0050] S100, Function Dependency Network Construction: The system and the complex dependencies between systems in the system are modeled through the function dependency network model, and the performance measurement of system functions is converted into the operability level of nodes, that is, the effectiveness of nodes.

[0051] S200. Calculation of Functional Dependency Network Weights Based on Expert Scoring: The expert scoring method is used to give different contributions to the functional dependency relationships between different nodes in order to measure the contribution of different network nodes to the overall mission of the ship's electronic information system.

[0052] S300, System Node Performance Calculation and Key Node Ranking: Based on the results obtained from S100 and S200, the system performance value of each node is calculated and ranked through a weighted functional dependency matrix to obtain the key nodes of the ship's electronic information system, and the equipment referred to by the nodes is monitored and maintained in advance.

[0053] Specifically, the critical fault node identification method for ship electronic information systems of this invention effectively identifies critical fault nodes in the system by constructing a functional dependency network model, determining weights through expert scoring, and calculating a weighted functional dependency matrix. This method not only improves the stability and reliability of the system and optimizes maintenance strategies, but also enhances decision support and improves task execution capabilities. Simultaneously, it reduces human error, adapts to complex environments, strengthens the system's dynamic reconfiguration capabilities, and promotes system upgrades, providing a scientific and automated solution for the management and maintenance of ship electronic information systems.

[0054] Furthermore, S100 includes the following steps:

[0055] S110. Using the Functional Dependency Network (FDN) model, model the complex dependencies between systems in the system and create a directed graph, i.e., the FDN model, where nodes represent systems or functions in the system and edges represent dependencies between nodes.

[0056] S120. Based on the von Neumann-Morgenstein utility theory, the performance measurement of system function is converted into the operability level of nodes, that is, the efficiency of nodes, thereby assigning an efficiency value to each node.

[0057] Specifically, in S110, by creating a directed graph to represent the system or function and the dependencies between them, the FDN model provides an intuitive and systematic approach to understanding and analyzing the complex interactions in shipboard electronic information systems. This model helps identify key components in the system and their interdependencies, providing a solid foundation for further analysis and decision-making. In S120, the performance metrics of system functions are converted into the operability levels of nodes, i.e., effectiveness values, using von Neumann-Morgenstein utility theory, which quantifies the contribution of each node to the system. This quantitative method provides an objective and standardized metric for evaluating system performance and identifying critical failure nodes, facilitating more accurate system effectiveness analysis and failure impact assessment.

[0058] Furthermore, in S100, according to system function, the node types in the network include: sensing node P, network node N, computing / display and control hardware node H, display and control node S, and weapon node W.

[0059] Furthermore, the node connection rules and descriptions are as follows:

[0060] P→N: The sensing node sends information to the network node;

[0061] N→H: Network nodes send the information that needs to be processed to computing / display / control hardware nodes;

[0062] H→N: Computational / display control hardware nodes send instructions or processed information to network nodes;

[0063] H→S: The computing / display control hardware node sends the information to be computed to the display control software node;

[0064] S→H: The display and control software node sends the calculated information to the calculation / display and control software node;

[0065] S→S: Display and control software nodes transmit information to the next software node through network nodes;

[0066] N→W: Network nodes transmit attack information to weapon nodes;

[0067] Based on the node connection rules and descriptions, the various components of the ship's electronic information system are classified as FDNA nodes, among which,

[0068] P includes: sensing devices;

[0069] N includes: integrated network equipment;

[0070] H includes: public computing equipment and public display and control equipment;

[0071] S includes: target recognition software, trajectory generation software, threat assessment software, friend or foe identification software, situation display software, weapon allocation software, and target command and control software;

[0072] W includes weaponry equipment.

[0073] Specifically, in S100, the components of the ship's electronic information system are subdivided according to system function into sensing nodes P, network nodes N, computing / display / control hardware nodes H, display / control nodes S, and weapon nodes W, and clear node connection rules and descriptions are defined. The beneficial effects of this scheme include:

[0074] 1. Enhance system understanding: Clearly classifying and defining different types of nodes and their functions helps to gain a deeper understanding of the internal operating mechanisms of the system and the interactions between components.

[0075] 2. Optimize information flow: By defining the connection rules between nodes, ensure that information flows efficiently and orderly in the system, thereby improving the overall processing speed and response capability.

[0076] 3. Improved System Design: The classification and connection rules proposed in this invention help to consider the dependencies and interactions of components during the system design phase, which helps to create a more robust and modular system architecture.

[0077] 4. Improved fault diagnosis: Clear node classification and connection rules enable faster location of the source of the problem when a system failure occurs, simplifying the fault diagnosis process.

[0078] 5. Facilitate maintenance and upgrades: Clear node classification helps maintenance personnel identify and maintain critical components, and also facilitates improvements to specific parts during system upgrades.

[0079] 6. Improve task adaptability: Through this structured approach, the system can better adapt to different task requirements, achieve optimal resource allocation and dynamic task reconfiguration.

[0080] 7. Enhanced security: Strictly defining the information transmission path from the perception node P to the weapon node W helps to enhance system security and ensure the secure transmission of critical information.

[0081] 8. Support for critical node identification: The detailed classification and connection rules of this invention provide a foundation for subsequent functional dependency network analysis, which helps to identify the critical nodes that contribute the most to system performance, thereby enabling effective monitoring and maintenance.

[0082] Furthermore, S200 includes the following steps:

[0083] S210. Based on the nodes and edges in the FDN model, design the Dependency Strength (SOD) and Dependency Criterion (COD) parameters. The Dependency Strength (SOD) and Dependency Criterion (COD) parameters are used to quantify the dependency relationships between nodes.

[0084] S220. Through expert scoring, the dependency functions between nodes are quantitatively evaluated to obtain scoring results. A judgment matrix is ​​constructed to obtain the weight of each node's contribution to the total system task.

[0085] S230. Utilize expert scoring results to quantitatively evaluate the dependency functions between nodes and assign differentiated weights.

[0086] Specifically, S200, through the design of dependency strength (SOD) and dependency criticality (COD) parameters, enables the solution of this invention to accurately quantify the dependencies between nodes, providing detailed quantitative data for system analysis. Utilizing expert scoring, the solution integrates the knowledge and experience of domain experts, enhancing the accuracy and authority of the assessment by constructing a judgment matrix. Based on the expert scoring results, the solution of this invention assigns differentiated weights to the dependent functions between nodes, ensuring an accurate assessment of each node's contribution to the overall system task. These differentiated weights provide strong support for system decision-making, helping to identify and manage critical nodes and optimize resource allocation. Through quantitative assessment and weight allocation, the solution of this invention improves the depth of analysis of the system's internal dependency structure, contributing to a deeper understanding of the system's complexity and dynamics. Accurate weight allocation helps prioritize and strengthen nodes that contribute more to system performance during system maintenance and upgrades. By identifying and strengthening critical nodes, the solution helps improve the system's resilience to failures, thereby enhancing overall robustness.

[0087] Furthermore, in S300, the following steps are included:

[0088] S310. Based on the FDN model established in S100 and the weights calculated in S200, construct the relationship dependency matrix of the ship's electronic information system, including the SOD matrix and the COD matrix.

[0089] S320. Using the weights obtained in S200, the efficiency values ​​of the final leaf nodes of the system are calculated through the dependency matrix, SOD matrix, and COD matrix.

[0090] S330. By analyzing the impact of each node failure on the final performance value, i.e., calculating the decrease in performance value after node failure, key nodes can be identified.

[0091] S340. Sort all nodes according to the calculated performance drop, identify the key nodes that have the greatest impact on system performance, and implement pre-monitoring and regular maintenance for these nodes to improve the overall stability and reliability of the system.

[0092] Specifically, in step S300, this invention constructs a dependency matrix by combining the FDN model and weights obtained from expert scoring, accurately calculates the performance value of the final leaf nodes, and analyzes the impact of node failure on performance to identify critical nodes. This process not only improves system stability and reliability but also promotes the rational allocation of resources and the formulation of preventive maintenance strategies through priority ranking, providing strong data support for decision-makers, strengthening risk management, and thus enhancing the overall performance and mission certainty of the ship's electronic information system.

[0093] Furthermore, in S310, a dependency matrix C representing the relationships between various nodes of the ship's electronic information system is constructed based on the FDNA network. n×n As shown in the following formula:

[0094]

[0095] Similarly, the dependency parameter matrix, SOD matrix A, can be obtained. n×n and COD matrix B n×n As shown in the following formula:

[0096]

[0097] Furthermore, in S330, the performance value of each node is assumed to be reduced to 50% of its original value, and the difference between the current system performance value and the performance value when all nodes are effective is calculated. The greater the decrease in node performance, the greater and more critical the impact of that node on the system performance.

[0098] Specifically, S330 simulates and reduces the performance value of each node to 50% of its original value, effectively quantifying the specific impact of node failure on system performance, thereby identifying the critical nodes with the greatest impact on system performance. This method not only improves the understanding of system vulnerabilities but also, by determining the performance degradation value, provides prioritization for resource allocation, risk management, and system maintenance, enhancing system robustness and supporting data-driven decision-making, ultimately improving the stability and reliability of the entire ship's electronic information system.

[0099] A storage medium storing a computer program, which, when executed by a processor, implements the aforementioned method for identifying critical fault nodes in a ship's electronic information system.

[0100] Specifically, the computer program stored on this storage medium, when executed by a processor, enables a highly efficient and automated method for identifying critical fault nodes in shipboard electronic information systems. Its beneficial effects include improving the speed and accuracy of fault diagnosis, reducing reliance on manual analysis, and optimizing the maintenance and operation processes of shipboard electronic information systems. Furthermore, the application of this program enhances the system's predictive maintenance capabilities, reduces system failure rates, improves mission reliability, and enhances the overall combat effectiveness of the ship. In this way, it ensures that critical components of the shipboard electronic information system receive appropriate attention and protection, thereby improving the stability and security of the entire system.

[0101] A computer device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the above-described method for identifying critical fault nodes in a ship's electronic information system.

[0102] Specifically, this computer equipment, equipped with memory, a processor, and dedicated computer programs, is capable of efficiently executing automatic identification methods for critical fault nodes in shipboard electronic information systems. Its beneficial effects include automating and refining the fault detection process, improving system maintenance efficiency and operational stability, while providing strong data support for decision-makers, ensuring the reliability and security of shipboard electronic information systems under various operating environments.

[0103] The following are specific embodiments of the present invention:

[0104] (1) System functions depend on network construction

[0105] The FDNA method models the system and the complex dependencies between systems within it using a Functional Dependency Network (FDN) model, and then analyzes performance using dependency parameters and piecewise linear functions. An FDN is a directed graph where nodes represent systems or functions within the system, and edges represent dependencies between nodes. Based on the von Neumann-Morgenstein (vNM) properties of additivity, monotonically increasing utility, and diminishing marginal utility, FDNA transforms the performance measure of system functions into the operability level of nodes, i.e., the performance of a node. The dependency between nodes is defined as follows: if a node N... j The generation of efficiency requires another node N. i The input (such as data, materials, or energy) is called the node N. j Depends on node N i There are also N nodes. j It may depend on multiple nodes simultaneously. Node N j N is called the receiving node. iThese are called supply nodes. A function-dependent network model can be viewed as a logical topology, expressing the propagation process of performance within the network. From a graph theory perspective, nodes are described as follows: the node being pointed to is called the parent node, the node pointing to is called the child node, a node without a supply node is called a leaf node, and a node not depended on by any node is called the root node. A typical function-dependent network is as follows: Figure 1 As shown.

[0106] In functional dependency network analysis, a set of dependencies is described by Dependency Strength (SOD) and Dependency Criticality (COD). SOD represents the proportion of improved performance of a receiving node under the influence of a dependency. The SOD of a set of dependencies is determined by the parameter α. ij Indicates that i is the number of the supply node, j is the number of the receiving node, and α ij The value range is [0,1], α ij A higher value indicates a stronger dependence of the receiving node on the supplying node, and a greater contribution from the supplying node to the receiving node. However, the operability level of a receiving node is also limited by the operability level of its supplying nodes. If a receiving node depends on multiple supplying nodes, and assuming that the operability level of one supplying node is very low or zero, even if other supplying nodes are at high operability levels, the failure of that supplying node cannot be compensated for, thus limiting the effectiveness of the receiving node. This phenomenon is captured by the Dependency Criterion (COD). The COD of a set of dependencies is represented by the parameter β. ij It means that β ij The value range is [0, 100]. Receiver node N j The efficiency of O j It will never be greater than 0 j +β ij . β ij The smaller the value, the more likely node N is to be affected. i For node N j The more critical, the more β ij =0 means if node N i If the receiving node N fails completely, then... j It will also become completely ineffective, β ij =100 indicates node N i For supply N j There are no constraints. Based on the efficiency function proposed by the FDNA method using the restricted average weakest chain principle, the efficiency of a receiving node is a function of the efficiency of its supply nodes. The efficiency function consists of two parts: the SOD function and the COD function. The SOD function is aggregated by taking the mean, and the COD function is aggregated by taking the minimum. The smaller of the two values ​​is selected as the final calculation result to complete the dynamic selection. If the receiving node N... jThere are h supply nodes, and their efficiency is O. j Calculated by the following formula:

[0107]

[0108] Where 0≤α ij ≤1, 0≤β ij ≤100, 0≤O i ≤100; where α ij β ij They are nodes N respectively j For node N i Dependency strength and dependency criticality parameters; O i For node N i The efficiency value.

[0109] To improve the applicability of the FDNA method in the field of operational system effectiveness assessment, it is necessary to clearly define the node types and connection rules between nodes in the function-dependent network. Based on the IACM model proposed by Cares and the networked modeling method proposed by Professor Tan Yuejin of the National University of Defense Technology, this invention further categorizes the nodes in the network into the following five major categories according to system function:

[0110] (1) Perception Node: Perception devices include radio frequency integrated devices, optical frequency devices, radar, sonar, etc. They are abbreviated as perception node P.

[0111] (2) Network nodes, such as integrated network devices, mainly provide network resources for command and control software. They are simply referred to as hardware network nodes N.

[0112] (3) Computation and Demonstration Hardware, such as public computing service equipment and public display and control equipment, are required to provide computing resources for command and control software. They are referred to as Computation and Demonstration Hardware H.

[0113] (4) Computation and Demonstration Software Nodes, such as point generation software agents, target recognition software agents, and trajectory generation software agents, perform comprehensive information processing. These are abbreviated as Computation and Demonstration Software Nodes (S).

[0114] (5) Weapon nodes, such as ship-launched missiles and long-range artillery, attack enemy ships, aircraft, submarines, etc. They are simply referred to as weapon nodes (W).

[0115] The main connection rules and physical meanings between the above types of nodes are shown in Table 1 below.

[0116]

[0117] Table 1 Node Connection Rules and Descriptions

[0118] The functionally dependent network model and the complex network model, after being improved and standardized, have strong similarities. However, the FDNA method and the performance evaluation method based on complex network theory are still fundamentally different. The differences between the functionally dependent network model and the complex network model are summarized in Table 2.

[0119]

[0120]

[0121] Table 2. Differences between Functionally Dependent Network Models and Complex Network Models

[0122] Based on the above rules, the various components of the ship's electronic information system can be classified into FDNA nodes as shown in Table 3 below:

[0123]

[0124] Table 3 Hardware Device Unit Names

[0125] (2) Calculation of Function Dependency Network Weights Based on Expert Scoring

[0126] To calculate the weight of each node, we need to adjust α in equation (1). ij and β ij To ensure that the values ​​reflect the importance and functional relationships of each node in practice, several experts in the field of ship electronic information system design can compare the importance of each node pairwise to construct a judgment matrix. This matrix quantifies the review criteria, using the magnitude of the values ​​to reflect the importance of the review indicators. The scale is shown in Table 4 below.

[0127]

[0128] Table 4α ij Expert scores

[0129] Where β ij The expert judgment methods are shown in Table 5 below:

[0130]

[0131] Table 5β ij Expert scoring (3) System node performance calculation and key node ranking

[0132] After obtaining the weights of each node, the dependency matrix C between the nodes of the ship's electronic information system can be constructed based on the FDNA network.n×n As shown in the following formula:

[0133]

[0134] Similarly, the dependency parameter matrix, SOD matrix A, can be obtained. n×n and COD matrix B n×n As shown in the following formula:

[0135]

[0136] Finally, the efficiency values ​​of the final leaf nodes of the system are calculated using the SOD matrix, COD matrix, and equation (1). In order to obtain the key nodes in the network, it is necessary to analyze the impact of the failure of each node on the final efficiency value. Therefore, the efficiency value of each node is reduced to 50% of its original value, and the difference between the efficiency value after the efficiency reduction and the efficiency value when all nodes are effective is calculated. max -O i O max It is the original performance value, O i It is the performance value of the output node after the i-th node fails, and the nodes are sorted. The higher the performance value, the more critical the node.

[0137] Taking the ship's electronic information system for air defense as an example, the key node identification process is as follows:

[0138] The air defense mission flowchart of the ship's electronic information system is as follows: Figure 2 As shown,

[0139] ①System functionality depends on network infrastructure:

[0140] The functional dependency network for air defense missions of the ship's electronic information system obtained using the FDNA method is as follows: Figure 3 As shown;

[0141] ② Calculation of function-dependent network weights based on expert scoring:

[0142] Since the input source is a single source, therefore for α ij In terms of functionality, successor nodes often have a nearly complete determining effect on their predecessor nodes. Therefore, for α... ij Most people chose α, which was determined by expert scoring, from dependency level 5 and 6. ij As shown in Table 6 below:

[0143]

[0144]

[0145] Table 6α ij Expert scores and betaij The matrix weights are shown in the table below:

[0146]

[0147] Table 7β ij Expert scores

[0148] ③ System node performance calculation and critical node ranking:

[0149] The dependency matrix C, SOD matrix A, and COD matrix B of the ship's electronic information system are obtained through equations (2), (3), and (4) as shown in the following equations:

[0150]

[0151]

[0152] First, assuming that all nodes are functioning correctly, i.e., the effectiveness value of each node is 100, we obtain the effectiveness value O for calculating the weapon node. Wmax It is 95.72.

[0153] Next, assuming the effectiveness value drops to 50% of its original value when the node fails, i.e., the effectiveness value is 50, we will then calculate the effectiveness value O of the weapon node. Wi and O Wmax The difference is the system performance reduction value. The larger the reduction value, the more critical the node is. The importance of each node is shown in the table below. If 5 key nodes are selected, they are: sensing equipment, weapon equipment, integrated network equipment, common computing equipment, and common display and control equipment.

[0154]

[0155]

[0156] This invention presents a method for identifying critical fault nodes in shipboard electronic information systems. Addressing the current practice of relying primarily on manual judgment or pre-assessment for critical fault identification, which often neglects automated information flow analysis, this invention employs FDNA (Functional Dependency Network) techniques to analyze the information transmission pathways of the shipboard electronic information system and construct a functional dependency network. Within this network framework, a dependency correlation matrix is ​​formed by combining node numbers. This strategy further adopts an expert scoring method to quantitatively evaluate the functional dependencies between nodes, assigning differentiated weights to determine the relative importance of each node to the overall system task. By considering the weighted dependency correlation matrix, the system performance indicators of each node are calculated and ranked, thereby accurately identifying critical fault points. Pre-monitoring and regular maintenance are implemented on the equipment associated with these critical points to ensure the overall stability and reliability of the system.

[0157] Currently, this method has been verified on typical ship electronic information systems and can solve the problem of identifying key nodes in new ship electronic information systems.

[0158] The present invention provides a detailed description of a method for identifying key fault nodes in a ship's electronic information system. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for identifying critical fault nodes in a ship's electronic information system, characterized in that, The method for identifying key fault nodes in the ship's electronic information system includes the following steps: S100, Functional Dependency Network Construction: This involves modeling the system and the complex dependencies between systems within it using a functional dependency network model. The performance metrics of system functions are then converted into the operability level of nodes, i.e., node effectiveness. S100 includes the following steps: S110. Using the Functional Dependency Network (FDN) model, model the complex dependencies between systems in the system and create a directed graph, i.e., the FDN model, where nodes represent systems or functions in the system and edges represent dependencies between nodes. S120. Based on the von Neumann-Morgenstein utility theory, the performance measurement of system function is converted into the operability level of nodes, that is, the efficiency of nodes, thereby assigning an efficiency value to each node. S200, Functional Dependency Network Weight Calculation Based on Expert Scoring: This method uses expert scoring to assign different contributions to the functional dependencies between different nodes, measuring the contribution of different network nodes to the overall mission of the ship's electronic information system. S200 includes the following steps: S210. Based on the nodes and edges in the FDN model, design the Dependency Strength (SOD) and Dependency Criterion (COD) parameters, which are used to quantify the dependency relationships between nodes. S220. Through expert scoring, the dependency functions between nodes are quantitatively evaluated to obtain scoring results. A judgment matrix is ​​constructed to obtain the weight of each node's contribution to the total system task. S230. Utilize expert scoring results to quantitatively evaluate the dependency functions between nodes and assign differentiated weights. S300, System Node Performance Calculation and Key Node Ranking: Based on the results obtained from S100 and S200, the system performance value of each node is calculated and ranked using a weighted functional dependency matrix to obtain the key nodes of the ship's electronic information system. The equipment referred to by these nodes is then monitored and maintained in advance. In S300, the following steps are included: S310. Based on the FDN model established in S100 and the weights calculated in S200, construct the relationship dependency matrix of the ship's electronic information system, including the SOD matrix and the COD matrix. S320. Using the weights obtained in S200, the efficiency values ​​of the final leaf nodes of the system are calculated through the dependency matrix, SOD matrix, and COD matrix. S330. By analyzing the impact of each node failure on the final performance value, i.e., calculating the decrease in performance value after node failure, key nodes can be identified. S340. Sort all nodes according to the calculated performance drop, identify the key nodes that have the greatest impact on system performance, and implement pre-monitoring and regular maintenance for these nodes to improve the overall stability and reliability of the system.

2. The method for identifying critical fault nodes in a ship's electronic information system according to claim 1, characterized in that, In S100, according to system function, the node types in the network include: sensing node P, network node N, computing / display and control hardware node H, display and control node S, and weapon node W.

3. The method for identifying critical fault nodes in a ship's electronic information system according to claim 2, characterized in that, Node connection rules and descriptions are as follows: P→N: The sensing node sends information to the network node; N→H: Network nodes send the information that needs to be processed to computing / display control hardware nodes; H→N: Computational / display control hardware nodes send instructions or processed information to network nodes; H→S: The computing / display control hardware node sends the information to be computed to the display control software node; S→H: The display and control software node sends the calculated information to the calculation / display and control software node; S→S: Display and control software nodes transmit information to the next software node through network nodes; N→W: Network nodes transmit attack information to weapon nodes; Based on the node connection rules and descriptions, the various components of the ship's electronic information system are classified as FDNA nodes, where, P includes: sensing devices; N includes: integrated network equipment; H includes: public computing equipment and public display and control equipment; S includes: target recognition software, trajectory generation software, threat assessment software, friend or foe identification software, situation display software, weapon allocation software, and target command and control software; W includes weaponry equipment.

4. The method for identifying critical fault nodes in a ship's electronic information system according to claim 3, characterized in that, In S310, a dependency matrix relating various nodes of the ship's electronic information system is constructed based on the FDNA network. As shown in the following formula: Similarly, the dependency parameter matrix and the SOD matrix can be obtained. and COD matrix As shown in the following formula: 。 5. The method for identifying critical fault nodes in a ship's electronic information system according to claim 4, characterized in that, In S330, the performance value of each node is assumed to be reduced to 50% of its original value, and the difference between the current system performance value and the performance value when all nodes are effective is calculated. The larger the decrease in node performance, the greater and more critical the impact of that node on the system performance.

6. A storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for identifying key fault nodes in a ship's electronic information system as described in any one of claims 1-5.

7. A computer device, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a method for identifying critical fault nodes in a ship's electronic information system as described in any one of claims 1-5.

Images