A civil aviation transportation system risk propagation path identification method, device, medium and product

Abstract

The application discloses a civil aviation transportation system risk propagation path identification method, equipment, medium and product, relates to the civil aviation transportation risk propagation field, and the method comprises the following steps: acquiring subsystem information of a civil aviation transportation system; constructing a finite state machine network model according to the subsystem information; constructing a risk propagation path identification model according to the finite state machine network model; determining the risk propagation mode of the civil aviation transportation system based on the finite state machine network model and the risk propagation path identification model according to the current subsystem information; the risk propagation path identification model comprises an initial state combination determination module, a stable state combination determination module, a safety level division module and an evolution path identification module. The application can dynamically and systematically depict the propagation path of the risk in the civil aviation transportation system and identify the risk propagation mode.

Description

Technical Field

[0001] This application relates to the field of risk transmission in civil aviation transportation, and in particular to a method, equipment, medium and product for identifying risk transmission paths in the civil aviation transportation system. Background Technology

[0002] As the scale of civil aviation transportation continues to expand and operational scenarios become increasingly complex, the interdependencies among various subsystems within the civil aviation transportation system (such as air traffic management, airport operations, airline operations, and communication and navigation) are constantly strengthening, making the overall civil aviation transportation system highly dynamic, coupled, and complex. Against this backdrop, a single equipment failure or localized functional anomaly can rapidly propagate and amplify through complex interactive networks, ultimately triggering systemic risks or even causing systemic paralysis.

[0003] Currently, civil aviation safety risk management mainly relies on ex-post statistical analysis based on historical data, expert experience assessment, and static safety assessment models for specific aspects. For example, some researchers use probabilistic graphical models (such as Bayesian networks or dynamic Bayesian networks) to model risk propagation. This method can naturally characterize the uncertainty in risk propagation, but it requires a large amount of data to learn accurate conditional probability tables to define state transition rules. Some researchers use dynamic macro-flow-stock modeling, which can accurately depict continuous, cumulative evolutionary processes and the delayed feedback effects of policy interventions, but it is difficult to characterize discrete, sudden state jumps. Some researchers use multi-agent self-service interaction modeling, which can better reflect the autonomy and heterogeneity of subsystems and the local interactions of subsystems, but it is difficult to predict and formally analyze risk paths, and it is costly. Some researchers use graph theory and propagation dynamics models for risk analysis, which can quickly screen for vulnerabilities in the civil aviation transportation system, but it lacks the details of the internal states of subsystems and the logic of multiple state transitions.

[0004] It is evident that while these traditional methods have played a crucial role in long-term security practices, they still possess significant limitations. First, existing technologies largely focus on the probability and severity of risk events, failing to reveal the specific paths and mechanisms by which risks evolve over time within complex systems and propagate across subsystems. In particular, they lack effective tracking and identification methods for dynamic processes such as cascading failures caused by multi-stage coupling and the accumulation of latent risks. Second, traditional assessments are often based on binary (safe / unsafe) or discrete level classifications, failing to identify stable states that the system may persist under various operating conditions from the perspective of the overall system state space evolution. This results in an insufficient understanding of the system's potential vulnerabilities and overall robustness. Third, existing methods pay relatively more attention to responses to sudden, explicit risks, but lack effective early identification and warning models for "chronic" risk propagation patterns that initially appear normal but slowly accumulate functional degradation internally, ultimately leading to systemic instability. These types of risks are highly concealed, and once they erupt, the consequences are often severe. Furthermore, when introducing new intelligent systems such as "cloud boxes" to enhance system security, traditional methods struggle to quantitatively assess the specific impact of the new systems on the overall risk propagation pattern, steady-state structure, and system resilience, thus failing to provide accurate model support and decision-making basis for system architecture optimization.

[0005] Therefore, based on the above problems, there is an urgent need to provide a method for identifying the risk propagation path in the civil aviation transportation system, which can dynamically and systematically depict the propagation path of risks within the civil aviation transportation system and identify risk propagation patterns. Summary of the Invention

[0006] The purpose of this application is to provide a method, equipment, medium and product for identifying risk propagation paths in the civil aviation transportation system, which can dynamically and systematically depict the propagation path of risks in the civil aviation transportation system and identify risk propagation patterns.

[0007] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for identifying risk propagation paths in the civil aviation transportation system, including: Obtain information on the subsystems of the civil aviation transportation system; the subsystem information includes: the number of subsystems, the directed interaction relationships between subsystems, and the state set and state transition rules corresponding to each subsystem; Based on the subsystem information, a finite state machine network model is constructed. The finite state machine network model uses subsystems as nodes and directed interaction relationships between subsystems as edges. Each node is associated with a set of states and a state transition rule. A risk propagation path identification model is constructed based on a finite state machine network model. This model includes: an initial state combination determination module, a stable state combination determination module, a safety level classification module, and an evolution path identification module. The initial state combination determination module determines all initial state combinations of the civil aviation transportation system based on the number of subsystems and their state sets. The stable state combination determination module iterates the state of each subsystem in all initial state combinations according to directed interaction relationships and state transition rules until all subsystems enter a stable state, obtaining the corresponding stable state combination, and determines the evolution path of each initial state combination based on the iteration process. A stable state is a state where the state is the same before and after iteration. The safety level classification module determines the health score of each stable state combination and classifies all stable state combinations into different levels of safety zones based on the health score. The evolution path identification module determines the path length of each evolution path and the trend of state value changes in the subsystems during iteration. It also identifies the risk propagation mode of the civil aviation transportation system based on the path length and state value change trend. The risk propagation mode includes: a rapid diffusion mode or a latent decay mode. Based on current subsystem information, and using a finite state machine network model and a risk propagation path identification model, the risk propagation pattern of the civil aviation transportation system is determined.

[0008] Secondly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for identifying risk propagation paths in the civil aviation transportation system.

[0009] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method for identifying risk propagation paths in the civil aviation transportation system.

[0010] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for identifying risk propagation paths in the civil aviation transportation system.

[0011] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a method, device, medium, and product for identifying risk propagation paths in a civil aviation transportation system. It constructs a finite state machine network model by acquiring subsystem information of the civil aviation transportation system, and then builds a risk propagation path identification model based on the finite state machine network model. Based on the current subsystem information, it determines the risk propagation mode of the civil aviation transportation system using the finite state machine network model and the risk propagation path identification model. Specifically, the initial state combination determination module and the stable state combination determination module in the risk propagation path identification model can accurately simulate the dynamic evolution process of each subsystem in the civil aviation transportation system under interaction. The safety level classification module can classify the various stable state combinations that the civil aviation transportation system may reach and the corresponding safety areas. The evolution path identification module can automatically track and analyze the evolution path from any initial state combination to a stable state combination, revealing two typical modes of risk propagation: rapid diffusion and slow decay. This application enables the civil aviation transportation system to shift from post-event handling to pre-event risk identification and in-event tracking of risk management paradigms, providing scientific support and decision-making basis for optimizing risk response strategies, addressing the shortcomings of current risk identification technologies in describing the dynamic propagation of risks and discovering risk propagation patterns, and dynamically and systematically depicting the propagation path of risks within the civil aviation transportation system and identifying risk propagation patterns. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart illustrating a method for identifying risk propagation paths in a civil aviation transportation system, as described in one embodiment of this application. Detailed Implementation

[0014] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0015] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0016] With the increasing complexity and networking of the civil aviation transportation system, there is an urgent need for a method to identify risk propagation paths within the system. This method should be able to dynamically and systematically describe the propagation paths of risks within the civil aviation transportation system, identify risk propagation patterns, and determine the safety levels of different stable state combinations. To address these challenges, this application introduces a Finite State Machine Network (FSMN), which can dynamically and systematically characterize the propagation paths of risks within the civil aviation transportation system and identify risk propagation patterns.

[0017] In one exemplary embodiment, such as Figure 1 As shown, a method for identifying risk propagation paths in the civil aviation transportation system is provided, including the following S1 to S4. Wherein: S1: Obtain information on the subsystems of the civil aviation transportation system.

[0018] Subsystem information includes the number of subsystems, the directed interaction relationships between subsystems, and the state set and state transition rules for each subsystem. Specifically, the civil aviation transportation system includes multiple subsystems with key functions and the directed interaction relationships between them. Each subsystem includes a state set and corresponding state transition rules.

[0019] In one exemplary embodiment, the civil aviation transportation system includes Each subsystem (such as data acquisition system, airborne perception and early warning system, ground flight scenario reconstruction system, ground control center system, air traffic control system, and airport system, etc.) For positive integers. Based on the functional dependencies and data flow relationships within the civil aviation transportation system, the directed interaction relationships between subsystems are determined. A directed interaction relationship is defined as a single... The adjacency matrix is ​​calculated using the following formula: ; in, It is a directed interaction matrix. Indicates from subsystem Pointing subsystem The directed interaction relationship, when At that time, subsystem The output will not affect the subsystem. The state when At that time, subsystem The output will affect the subsystem The state.

[0020] Functional dependency refers to the condition that the normal operation of one subsystem is a prerequisite for the functioning of another subsystem. For example, the data acquisition system provides raw data to the airborne perception and early warning system. If the data acquisition system is in an abnormal state, the input quality of the airborne perception and early warning system will be directly affected. Data flow relationship refers to the direction of information, instruction, or status data transmission between subsystems. For example, the ground flight scenario reconstruction system sends the processed 3D situation map to the ground control center system. The ground control center system makes decisions based on the processed 3D situation map and issues dispatch instructions to downstream air traffic control systems, airport systems, etc.

[0021] subsystem state set The expression is ,in, This is a fault condition. It is in a partially downgraded state. The normal state indicates that the subsystem has three states: fault, partial degradation, and normal.

[0022] The input to the state transition rule is the subsystem's own internal state at the previous time step and a combination of input states from other related subsystems. The output is the subsystem's internal state at the next time step. In one exemplary embodiment, the state transition rule is determined based on the functional coupling threshold, calculated as follows: ; in, Representation Subsystem j exist t+1 The state at any given moment, Representation Subsystem j exist t The state at any given moment, Indicates in t At any given moment, all pairs of subsystems j The sum of the state values ​​of the subsystems that are directly affected. For pointing subsystem j The set of upstream subsystems, For subsystem j The state transition threshold.

[0023] S2: Construct a finite state machine network model based on the subsystem information.

[0024] A finite state machine (FSM) network model uses subsystems as nodes and directed interactions between subsystems as edges. Each node is associated with a set of states and a state transition rule. Specifically, the FSM network model consists of a set of nodes, a set of directed edges, and a set of state transition rules. Each node corresponds to a subsystem and is associated with a finite set of states; directed edges represent functional dependencies between subsystems, i.e., directed interactions between subsystems; and the state transition rule on each node determines its next state based on its own state and the states of all upstream nodes.

[0025] S3: Construct a risk propagation path identification model based on the finite state machine network model.

[0026] The risk propagation path identification model includes an initial state combination determination module, a stable state combination determination module, a security level classification module, and an evolution path identification module.

[0027] The initial state combination determination module is used to determine all initial state combinations for the civil aviation transportation system based on the number of subsystems and the state sets of the subsystems. Specifically, it iterates through the state sets of all subsystems to obtain all possible initial state combinations. For example, in a finite state machine network model with 10 nodes, i.e., 10 subsystems, each with 3 states, the number of initial state combinations is... for .

[0028] The stable state combination determination module iterates over the state of each subsystem in all initial state combinations based on directed interaction relationships and state transition rules until all subsystems enter a stable state, obtaining the corresponding stable state combination. It then determines the evolution path of each initial state combination based on the iteration process. Specifically, it combines the current states of the subsystems... As initial state combination and combine the current state Record the first fixed point of the evolution path; use state transition rules to determine the state of each subsystem at the next time step, and obtain a new combination of states. The new state combination is recorded as the second fixed point of the evolution path Path. The state transition rule is repeatedly used to iterate the state combination. Each iteration adds a fixed point to the evolution path Path until all subsystems have the same state before and after the iteration, that is, all subsystems have entered a stable state. At this time, the iteration is terminated, and the state combination at this moment is taken as the stable state combination, thus obtaining the complete evolution path from the initial state combination to the stable state combination.

[0029] The security level classification module is used to determine the health score of each stable state combination and, based on the health score, classify all stable state combinations into different security zones. Specifically, based on the state value of each subsystem in a stable state, the health score of all stable state combinations is determined; based on the health score and preset level thresholds, all stable state combinations are classified into high security zone, medium security zone, and risk zone. The preset level thresholds include a first level threshold and a second level threshold; the first level threshold is greater than the second level threshold.

[0030] In one exemplary embodiment, for each combination of stable states Calculate the corresponding health score. The formula for calculating the health score is as follows: ; in, Rate your health. For the number of subsystems, Number the subsystems. For subsystem The state value in a steady state.

[0031] Using computing devices (computers, servers, embedded computing platforms), statistically analyze all combinations that evolve to the same stable state. The initial state combination is defined as the number of such initial state combinations, and the number of such initial state combinations is defined as the stable state combination. The range of influence represents the stable state combination. The coverage scale of security domains (including high security domains and medium security domains) or risk domains.

[0032] Based on the health score and the scope of influence, all stable state combinations are divided into different levels of safety zones. These safety zone levels include high safety zone, medium safety zone, and low safety zone (risk zone). When the health score is greater than the first-level threshold, the corresponding stable state combination is in the high safety zone; when the health score is greater than the second-level threshold but less than the first-level threshold, the corresponding stable state combination is in the medium safety zone; and when the health score is less than the second-level threshold, the corresponding stable state combination is in the low safety zone. The calculation formula is as follows: ; ; ; in, Rate your health. As the first-level threshold, in this application, , As the second-level threshold, in this application, .

[0033] The evolution path identification module is used to determine the path length of each evolution path and the trend of state value changes of the subsystem during the iteration process. At the same time, it identifies the risk propagation pattern of the civil aviation transportation system based on the path length and the trend of state value changes. In an exemplary embodiment, the path length of each evolution path is determined, and the evolution path is divided into long paths and short paths based on a preset length threshold; the preset length threshold includes a first length threshold and a second length threshold; the first length threshold is less than the second length threshold.

[0034] Specifically, it will satisfy the formula The evolutionary path is divided into short paths; those satisfying the formula The evolutionary path is divided into long paths; among them, The path length of the evolution path. The first length threshold, , The second length threshold, , .

[0035] Risk propagation patterns include rapid diffusion patterns or latent decay patterns. Specifically, the proportion of stable state combinations corresponding to all short paths falling into the risk domain is statistically analyzed. If the proportion is greater than a preset risk threshold, the civil aviation transportation system is determined to have a rapid diffusion pattern. For each long path, the change sequence of the state values ​​of each subsystem is analyzed time-by-time along the evolution steps. If there is a trend of the subsystem state values ​​gradually and continuously degrading (e.g., from 2 to 1 and then to 0) in multiple consecutive time steps, the system is determined to have a latent decay pattern.

[0036] In one exemplary embodiment, a path length distribution statistical chart is generated for each evolution path, and the common characteristics of short paths are analyzed: short paths have fewer evolution steps, indicating that disturbances propagate rapidly in the civil aviation transportation system. Further analysis of the proportion of the stable state combinations corresponding to short paths that ultimately fall into the risk domain reveals that if the proportion is extremely high (e.g., >99%), the civil aviation transportation system is determined to have a risk propagation pattern of rapid diffusion and structural degradation, i.e., the risk propagation pattern of the civil aviation transportation system is a rapid diffusion pattern. Further analysis of the evolution process of long paths shows that although the initial state value of the subsystem is relatively safe (e.g., the initial state value of the subsystem is 2), the state value of the subsystem gradually and continuously degrades in multiple iterations. Therefore, the civil aviation transportation system is determined to have a risk propagation pattern of latent risk and slow decline, i.e., the risk propagation pattern of the civil aviation transportation system is a latent decline pattern.

[0037] In this application, "degradation" refers to reducing the state value of a subsystem by one level according to the state transition rules. Specifically, each subsystem is associated with a set of states, for example... When the state value of a subsystem decreases by one (e.g., 2→1 or 1→0), the subsystem is considered to have undergone a degradation. If the state value of the same subsystem decreases continuously during multiple iterations (e.g., 2→1→0), the subsystem is considered to have undergone continuous degradation. If the state values ​​of different subsystems of the civil aviation transportation system decrease sequentially at different time steps, rather than all of them collapsing simultaneously at the same time step, the civil aviation transportation system is considered to be gradually degrading. After the accumulation of multiple time steps, the civil aviation transportation system gradually slides from the safe domain to the risk domain.

[0038] S4: Based on the current subsystem information, determine the risk propagation pattern of the civil aviation transportation system using a finite state machine network model and a risk propagation path identification model.

[0039] In one exemplary embodiment, the risk propagation path identification model outputs indicators including: stable state combinations for each security level, corresponding health scores and impact ranges; a schematic diagram of the distribution of high-security, medium-security, and risk domains in the full state space; a path length distribution diagram of the evolution path; and case details of rapid diffusion and latent decay modes. It also includes a comparative analysis report of the indicators before and after changes to the civil aviation transportation system.

[0040] This application enables dynamic and quantitative tracking and diagnosis of the entire process of risk propagation in the civil aviation transportation system. Specific technical effects include: First, by constructing a risk propagation path identification model, defining the states and state transition rules of each subsystem, and performing evolutionary simulation across the entire state space, it automatically tracks and records all evolutionary paths of the civil aviation transportation system from any initial state combination to a stable state combination. Second, based on the evolutionary paths, it automatically identifies all convergent stable state combinations of the civil aviation transportation system and classifies these stable state combinations into safety levels (e.g., high-safety domain, medium-safety domain, and risk domain) according to health scores and impact ranges. Third, through statistical analysis and feature extraction of all evolutionary paths, it automatically identifies two typical system risk propagation patterns: rapid diffusion and structural degradation patterns, and latent risk and slow decline patterns, and outputs key evolutionary path cases.

[0041] Furthermore, this application can accurately simulate and record the complete evolution path of risks within the civil aviation transportation system, spanning subsystems and time steps, starting from any initial combination of states, such as [(2,2,2,2,...)→(2,2,2,1,...)→(2,2,2,0,...)]. This makes the black-box process of risk propagation transparent and traceable, achieving dynamic and visual analysis of the risk propagation process and overcoming the limitations of static and fragmented analysis in traditional methods. This application can not only determine whether the civil aviation transportation system is safe, but also identify multiple stable state combinations that the system may remain in for extended periods. Using two quantitative indicators—health score and impact range—it continuously divides the entire state space into different levels of safety zones. This upgrades safety assessment from judging a single result to a holistic understanding of the attractiveness landscape of the entire civil aviation transportation system, providing a systematic and quantitative safety situation assessment capability and overcoming the shortcomings of traditional binary or discrete-level assessments. This application can automatically filter and identify latent risk paths exhibiting slow decline characteristics from a vast number of evolution paths. This allows for early warning of cumulative functional degradation trends within the civil aviation transportation system even when its performance is still normal, enabling early intervention and overcoming the insensitivity of existing technologies to slow evolution risks. Furthermore, by comparing and analyzing changes in key indicators such as the distribution ratio of stable state combinations, the proportion of safe / risk domain sizes, and the distribution of state transition path lengths across different civil aviation transportation system architectures, this application can quantitatively assess the actual effect of a technology or strategy on improving the safety and resilience of the entire civil aviation transportation system. This provides precise support for decision-making, forming a quantitative analysis platform that supports the optimization of civil aviation transportation system architecture and strategy evaluation, solving the problem that traditional methods struggle to assess the effectiveness of systemic measures.

[0042] In summary, the technical effect of this application is not a single functional improvement, but a systematic solution to the four major problems in civil aviation risk identification through a complete method chain: unclear propagation path, inability to fully assess the security situation, inability to prevent latent risks, and inability to accurately calculate the effectiveness of measures. It achieves a leap from static, local, and reactive risk management to dynamic, global, and proactive intelligent safety governance.

[0043] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is connected to the system bus via the I / O interfaces. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores risk propagation path identification data for the civil aviation transportation system. The I / O interfaces of the computer device are used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for identifying risk propagation paths in the civil aviation transportation system.

[0044] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0045] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0046] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0047] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0048] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0049] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, etc., and are not limited to these.

[0050] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0051] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for identifying risk propagation paths in a civil aviation transportation system, characterized in that, The method includes: Obtain information on the subsystems of the civil aviation transportation system; the subsystem information includes: the number of subsystems, the directed interaction relationships between subsystems, and the state set and state transition rules corresponding to each subsystem; Based on the subsystem information, a finite state machine network model is constructed. The finite state machine network model uses subsystems as nodes and directed interaction relationships between subsystems as edges. Each node is associated with a set of states and a state transition rule. A risk propagation path identification model is constructed based on a finite state machine network model. This model includes: an initial state combination determination module, a stable state combination determination module, a safety level classification module, and an evolution path identification module. The initial state combination determination module determines all initial state combinations of the civil aviation transportation system based on the number of subsystems and their state sets. The stable state combination determination module iterates the state of each subsystem in all initial state combinations according to directed interaction relationships and state transition rules until all subsystems enter a stable state, obtaining the corresponding stable state combination, and determines the evolution path of each initial state combination based on the iteration process. A stable state is a state where the state is the same before and after iteration. The safety level classification module determines the health score of each stable state combination and classifies all stable state combinations into different levels of safety zones based on the health score. The evolution path identification module determines the path length of each evolution path and the trend of state value changes in the subsystems during iteration. It also identifies the risk propagation mode of the civil aviation transportation system based on the path length and state value change trend. The risk propagation mode includes: a rapid diffusion mode or a latent decay mode. Based on current subsystem information, and using a finite state machine network model and a risk propagation path identification model, the risk propagation pattern of the civil aviation transportation system is determined.

2. The method for identifying risk propagation paths in the civil aviation transportation system according to claim 1, characterized in that, The acquisition of subsystem information of the civil aviation transportation system specifically includes: Using expressions Determine the set of states; where, For a set of states, This is a fault condition. It is in a partially downgraded state. This is the normal state; Using formula Determine the directed interaction relationship; where, It is a directed interaction matrix. Indicates from subsystem Pointing subsystem The directed interaction relationship, when At that time, subsystem The output will not affect the subsystem. The state when At that time, subsystem The output will affect the subsystem state, Number of subsystems; Using formula Determine the state transition rules; in, Representation Subsystem j exist t+1 The state at any given moment, Representation Subsystem j exist t The state at any given moment, Indicates in t At any given moment, all pairs of subsystems j The sum of the state values ​​of the subsystems that are directly affected. For pointing subsystem j The set of upstream subsystems, For subsystem j The state transition threshold.

3. The method for identifying risk propagation paths in the civil aviation transportation system according to claim 1, characterized in that, The processing steps of the security level classification module include: Based on the state value of each subsystem in a steady state, determine the health score of all steady state combinations; Based on the health score and a preset level threshold, all stable state combinations are divided into a high-security domain, a medium-security domain, and a risk domain; the preset level threshold includes a first level threshold and a second level threshold; the first level threshold is greater than the second level threshold.

4. The method for identifying risk propagation paths in the civil aviation transportation system according to claim 3, characterized in that, The process of determining the health score for all steady-state combinations based on the state value of each subsystem in a steady state specifically includes: Using formula Determine the health score of the steady-state combination; in, Rate your health. For the number of subsystems, Number the subsystems. For subsystem The state value in a steady state.

5. The method for identifying risk propagation paths in the civil aviation transportation system according to claim 3, characterized in that, Based on health scores and preset level thresholds, all stable state combinations are divided into high-security, medium-security, and risk domains, specifically including: Using formula The stable state combinations are divided into high-security domains; among which... Rate your health. The threshold is the first level. Using formula The stable state combinations are divided into medium-security regions; among which... The threshold is the second level. Using formula The stable state combination is divided into risk domains.

6. The method for identifying risk propagation paths in the civil aviation transportation system according to claim 3, characterized in that, The processing steps of the evolution path identification module include: The path length of each evolution path is determined, and the evolution path is divided into long paths and short paths based on a preset length threshold; the preset length threshold includes: a first length threshold and a second length threshold; the first length threshold is less than the second length threshold; The proportion of stable state combinations corresponding to all short paths falling into the risk domain is counted. If the proportion is greater than the preset risk threshold, it is determined that there is a rapid diffusion pattern in the civil aviation transportation system. By statistically analyzing the state value changes of all long paths during the iteration process, if the state value of a subsystem gradually and continuously degrades during the iteration process, it is determined that the civil aviation transportation system has a latent decline pattern.

7. The method for identifying risk propagation paths in the civil aviation transportation system according to claim 6, characterized in that, The process of determining the path length of each evolutionary path and dividing the evolutionary paths into long paths and short paths based on a preset length threshold specifically includes: Will satisfy the formula The evolutionary path is divided into short paths; Will satisfy the formula The evolutionary path is divided into long paths; in, The path length of the evolution path. The first length threshold, The second length threshold, .

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the civil aviation transportation system risk propagation path identification method according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for identifying risk propagation paths in the civil aviation transportation system as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method for identifying risk propagation paths in the civil aviation transportation system as described in any one of claims 1-7.

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