Complex system security assessment method based on causal evidence reasoning

Abstract

The invention discloses a complex system security assessment method based on causal evidence reasoning, and relates to the field of system security assessment, and the method comprises the steps: obtaining historical assessment data of a complex system, and generating a historical data set; constructing a complex system security evaluation index system based on the historical data set; obtaining index data of the to-be-evaluated complex system; mining and quantifying the causal relationship of each index in the index data by adopting a causal evidence reasoning method based on a complex system security evaluation index system to obtain a causal relationship quantized value; and determining a security assessment result of the to-be-assessed complex system by using a causal evidence reasoning method based on the quantitative value of the causal relationship. According to the invention, the accuracy of safety assessment of the complex system can be improved.

Description

Technical Field

[0001] The present application relates to the field of system safety assessment, and in particular to a complex system safety assessment method based on causal evidence reasoning. Background Art

[0002] As engineering systems become larger and more complex, the safety of complex systems becomes more critical. Safety assessment refers to a method of identifying system hazards and seeking countermeasures. By assessing the degree of hazard and consequences, strategies are proposed to ensure the smooth operation of the system, reduce the accident rate and reduce losses. Safety assessment can promote safer and more reasonable management of the system, effectively reduce accidents and hazards, reasonably allocate resources, promote the formulation of safety standards, and improve the professional level of safety technicians.

[0003] At present, there are many complex system safety assessment methods under different backgrounds, mainly including qualitative methods such as questionnaire survey method and safety checklist evaluation method, quantitative methods based on data and analysis, hierarchical analysis method, multi-criteria decision-making methods combining quantitative and qualitative methods such as evidence reasoning, simulation-based methods, network model-based methods, fault-oriented assessment methods, expert system-based methods and hybrid methods, etc. Although these methods have achieved good research results in theory and practice, there are still some problems with these methods. For example, although qualitative methods are simple to understand and easy to master, they are easily affected by subjective factors and deviate.

[0004] Among them, although the quantitative method is relatively objective, it is heavily dependent on the integrity of the original data. In actual situations, it is often difficult to collect data at all levels of the complete system when facing a complex large system, and the data often has the characteristics of missing and incompleteness. The analytic hierarchy process has the problem of dealing with randomness and subjective uncertainty; the simulation-based method requires a lot of information in the modeling process, such as detailed analytical constraints, which is often difficult to obtain directly, and its evaluation results are also uncertain and difficult to explain. The fault-oriented method adopts a single "external" perspective, while many complex system safety assessment problems require a more comprehensive and integrated perspective to determine. Both the expert system method and the hybrid method lack flexibility, and their applicability and portability are poor. Compared with the above methods, the evidence reasoning method can effectively integrate multi-source information for reasoning, and the reasoning process is transparent and explainable, but it requires that the evidence required for reasoning is completely independent, which is difficult to meet the various needs of complex system safety assessment problems. At the same time, the above methods do not consider whether there is a causal relationship between indicators and between indicators and conclusions in the safety assessment of complex systems, making it difficult to achieve accurate assessment of the safety of complex systems. Summary of the invention

[0005] The purpose of this application is to provide a complex system safety assessment method based on causal evidence reasoning, which can improve the accuracy of complex system safety assessment.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In the first aspect, the present application provides a complex system safety assessment method based on causal evidence reasoning, including:

[0008] Obtain historical evaluation data of complex systems and generate historical data sets;

[0009] Constructing a complex system security assessment index system based on the historical data set;

[0010] Obtain indicator data for complex systems to be evaluated;

[0011] Using a causal evidence reasoning method, based on the complex system safety assessment index system, the causal relationship of each indicator in the index data is mined and quantified to obtain a causal relationship quantitative value;

[0012] Based on the causal relationship quantification value, a causal evidence reasoning method is used to determine the safety assessment result of the complex system to be assessed.

[0013] Optionally, obtain historical evaluation data of the complex system to generate a historical data set, including:

[0014] Acquire historical evaluation data of complex systems and preprocess the historical evaluation data; the preprocessing includes: data extraction, data cleaning and data conversion;

[0015] The historical data set is generated based on the preprocessed historical evaluation data.

[0016] Optionally, a causal evidence reasoning method is used to mine and quantify the causal relationship of each indicator in the indicator data based on the complex system safety assessment indicator system to obtain a causal relationship quantitative value, including:

[0017] Using a causal evidence reasoning method, based on the complex system safety assessment index system, a pairwise independence test is performed on any two indicators in the index data to obtain an independence test result;

[0018] Adopting the complex system safety assessment index system, determining the causal relationship between the indicators in the index data based on the independence test result, and using the indicators in the index data as nodes to generate an undirected graph;

[0019] Performing a conditional independence test on each node in the undirected graph, and using a merging rule to direct the edges in the undirected graph based on the conditional independence test result to obtain a directed acyclic graph;

[0020] The causal relationship between the nodes in the directed acyclic graph is quantified by using transfer entropy to obtain a causal relationship quantization value; the causal relationship between the nodes in the directed acyclic graph is represented by edges pointing between the nodes in the acyclic graph.

[0021] Optionally, based on the causal relationship quantified value, a safety assessment result of the complex system to be assessed is determined using a causal evidence reasoning method, including:

[0022] Combining multiple indicators with causal relationships into a composite indicator based on the causal relationship quantification value;

[0023] The expected utility of the composite index is determined, and the expected utility is used as a safety assessment result of the complex system to be assessed.

[0024] Optionally, the expected utility of the composite indicator is expressed as:

[0025]

[0026] In the formula, u (L) represents the expected utility of the indicator combination result, S represents the safety assessment level of the complex system, Θ represents the safety assessment level of the N complex system S n The identification framework composed of S,e(L) It represents the confidence of the complex system safety assessment level S after the combination of L indicators, u(S) represents the expected utility of the complex system safety assessment level S, and e(L) represents the final assessment result after all indicators are combined.

[0027] Optionally, the complex system safety assessment method of causal evidential reasoning further includes:

[0028] The complex system safety assessment index system is updated based on the historical assessment results or current assessment results of the complex system to be assessed.

[0029] Optionally, the complex system safety assessment index system is updated based on the historical assessment results or current assessment results of the complex system to be assessed, including:

[0030] Determine whether there are historical evaluation results for the complex system to be evaluated;

[0031] When the complex system to be evaluated has a historical evaluation result, the historical evaluation result is added as an indicator to the complex system security evaluation indicator system;

[0032] When there is no historical evaluation result for the complex system to be evaluated, the determined safety evaluation result of the complex system to be evaluated is added as an indicator to the complex system safety evaluation indicator system.

[0033] In a second aspect, the present application provides a complex system, which is a network system composed of different components interacting with each other nonlinearly. When the complex system executes the complex system safety assessment method of causal evidential reasoning provided above, safety assessment is achieved.

[0034] According to the specific embodiments provided in this application, this application has the following technical effects:

[0035] This application provides a complex system safety assessment method based on causal evidence reasoning. By adopting the causal evidence reasoning method, based on the complex system safety assessment index system, the causal relationship of each indicator in the index data is mined and quantified, and the causal relationship between indicators and between indicators and conclusions in the safety assessment of complex systems is considered, which can meet various needs of complex system safety assessment problems and greatly improve the accuracy of the causal evidence reasoning method and its application in complex system safety assessment. In addition, based on the quantified value of the causal relationship, the causal evidence reasoning method is used to determine the safety assessment results of the complex system to be evaluated, which can improve the accuracy of the safety assessment of the complex system. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 A flowchart of a complex system safety assessment method based on causal evidence reasoning provided in one embodiment of the present application;

[0038] Figure 2 A block diagram of the application principle of a complex system safety assessment method based on causal evidence reasoning provided in one embodiment of the present application;

[0039] Figure 3 An application flow chart of a complex system safety assessment method based on causal evidence reasoning provided in one embodiment of the present application;

[0040] Figure 4 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0042] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0043] In an exemplary embodiment, the present application provides a complex system security assessment method for causal evidence reasoning, which is executed by a computer device, specifically, it can be executed by a computer device such as a terminal or a server alone, or it can be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to a server as an example for explanation. Figure 1 As shown, the complex system safety assessment method of causal evidence reasoning provided by this application includes:

[0044] Step 100: Obtain historical evaluation data of the complex system and generate a historical data set.

[0045] Step 101: Construct a complex system security assessment index system based on historical data sets. In the actual application process, the historical data sets constructed in step 100 are combined, and the degree of influence of the historical data sets on the security assessment is comprehensively considered to select assessment indicators, and a suitable assessment index system is constructed according to the working mechanism of the assessment indicators.

[0046] Step 102: Obtain indicator data of the complex system to be evaluated.

[0047] Step 103: Using the causal evidence reasoning method, based on the complex system safety assessment index system, the causal relationship of each indicator in the index data is mined and quantified to obtain the causal relationship quantitative value.

[0048] Step 104: Based on the quantified value of the causal relationship, a safety assessment result of the complex system to be assessed is determined using a causal evidence reasoning method.

[0049] In another exemplary embodiment of the present application, after collecting historical evaluation data from existing information of a complex system, in order to improve the accuracy of subsequent evaluation, in this embodiment, it is necessary to perform pre-processing work such as extraction, cleaning, and conversion on the heterogeneous data source data in the historical evaluation data. Based on this, the implementation process of the above step 100 may include:

[0050] Step 1001: Obtain historical evaluation data of a complex system and preprocess the historical evaluation data. The preprocessing includes: data extraction, data cleaning and data conversion.

[0051] Step 1002: Generate a historical data set based on the preprocessed historical evaluation data.

[0052] In another exemplary embodiment of the present application, in order to consider whether there is a causal relationship between the indicator and the conclusion and verify the accuracy of the subsequent evaluation results, in this embodiment, the complex system security evaluation indicator system constructed in step 101 can also be updated based on the historical evaluation results or current evaluation results of the complex system to be evaluated. For example, Figure 3 As shown in the figure, the updating process of the system security evaluation index system includes:

[0053] (1) Update the complex system safety assessment index system based on the historical assessment results or current assessment results of the complex system to be assessed, including:

[0054] (2) Determine whether there are historical evaluation results for the complex system to be evaluated.

[0055] (3) When there are historical evaluation results for the complex system to be evaluated, the historical evaluation results shall be added as an indicator to the complex system security evaluation index system.

[0056] (4) When there is no historical assessment result for the complex system to be assessed, the safety assessment result of the complex system to be assessed shall be added as an indicator to the complex system safety assessment indicator system.

[0057] In another exemplary embodiment of the present application, in order to achieve accurate reasoning of the causal relationship between the indicators, the implementation process of the above step 103 of the present application may include:

[0058] Step 1031: Using the causal evidence reasoning method, based on the complex system safety assessment index system, perform a pairwise independence test on any two indicators in the index data to obtain the independence test results. For example:

[0059] A pairwise independence test is performed between indicator X and indicator Y to confirm whether there is a potential causal relationship between the two indicators, or whether they can be considered conditionally independent. The conditional independence assumption can be expressed as: Y(1), Y(0)⊥Z|X. In the formula, Z is the processing configuration, (Y(1), Y(0)) is the potential result, and ⊥ means independence under condition Z.

[0060] Step 1032: Using the complex system security assessment index system, determine the causal relationship between the indicators in the index data based on the independence test results, and use the indicators in the index data as nodes to generate an undirected graph, wherein the undirected edges of the undirected graph represent the causal relationship between the connected nodes.

[0061] Step 1033: Perform a conditional independence test on each node in the undirected graph, and use a merge rule to direct the edges in the undirected graph based on the conditional independence test results to obtain a directed acyclic graph. The directed acyclic graph is formed by directing these associated edges using a merge rule to characterize the causal relationship between the variables.

[0062] The merging rules include:

[0063] Rule 1: If there is X→YZ in the conditional independence test result, change YZ in the undirected graph to Y→Z.

[0064] Rule 2: If there is X→Z→Y in the conditional independence test result, change XY in the undirected graph to X→Y.

[0065] Rule 3: If the conditional independence test results show X-Z1→Y, X-Z2→Y, and the indicators Z1, Z1 are not adjacent, then change XY in the undirected graph to X→Y.

[0066] Step 1034: quantify the causal relationship between the nodes in the directed acyclic graph using the transfer entropy to obtain a causal relationship quantization value. The causal relationship between the nodes in the directed acyclic graph is represented by the edges pointing between the nodes in the acyclic graph.

[0067] The transfer entropy used in this step is calculated by Shannon entropy, conditional entropy and relative entropy. The calculation formula of Shannon entropy is:

[0068]

[0069] In the formula, H(X) represents the Shannon entropy of index X, p(x i ) indicates that each value x i The probability of x i Represents the i-th value of indicator X.

[0070] The calculation formula of conditional entropy is:

[0071]

[0072] In the formula, H(X|Y) represents the uncertainty of indicator X under the condition of given indicator Y, H(X,Y) represents the joint entropy of X and Y, and p(Y=y i ) indicates that Y takes the value y iThe probability of H(X|Y=y i ) means that when y is known to be Y i The entropy of indicator X under certain conditions is called conditional entropy.

[0073] The calculation formula of relative entropy is:

[0074]

[0075] Where P(i), Q(i) are the probability distributions of indicator I, and K(Q||P) represents the asymmetric measure of the difference between the two probability distributions under the condition of discrete indicator I, namely, relative entropy.

[0076] Based on the above description, the formula for determining the quantitative value of causality is expressed as:

[0077]

[0078] In the formula, TE X→Y (X t ,Y t ) represents the transfer entropy between index X and index Y at time t, X t Indicates the indicators X, Y at time t t represents the index Y at time t, Y t-Δω The indicator Y represents the time t-Δω, Δω and Δτ represent the past time window and historical delay respectively. t |Y t-Δω ) is defined as the indicator Y at time t-Δω to infer the indicator Y at time t, H(Y t |Y t-Δω ,X t-Δτ ) is defined as the index X at time t-Δτ and the index Y at time t-Δω to infer the index Y at time t, H(X t |X t-Δτ ) is defined as the indicator X at time t-Δτ to infer the indicator X at time t, X t-Δτ The index X, p(x τ ,y,y ω ) represents the index x τ ,y and y ω The joint probability, p(y ω ) indicates that the indicator Y takes the value y ω The probability, p(x τ ,y ω ) represents the index x τ and ω The joint probability of .

[0079] In another exemplary embodiment of the present application, the causal evidence reasoning method is used to evaluate the security of a complex system. First, the causal relationship of the indicator is mined to obtain the quantitative value of the causal relationship, which is then brought into the reasoning process of the traditional evidence reasoning method for integration and improvement. Finally, the security of the complex system is evaluated, so that the reasoning process is more explainable, and finally a more accurate complex system security evaluation result is obtained. Based on this, the implementation process of step 104 may include:

[0080] Step 1041: Combining multiple indicators with causal relationships into a composite indicator based on the causal relationship quantification value.

[0081] Here, it is assumed that the identification framework defines Θ, Θ = {S1,...,S N}, where S i is the i-th evaluation level, i = 1, ..., N, and the power set Θ is composed of 2 N subsets, then:

[0082]

[0083] Where T(Θ) represents the power set of all evaluation levels.

[0084] Assume there are L indicators {e1,e2,...,e L}, the kth index is e k , Where S is a subset of the power set Θ. S,k is the indicator e k The confidence level assigned to the assessment grade S.

[0085] Assume that the indicator e k The weight is w k , in the identification framework, the indicator e k The basic probability mass assigned to the evaluation level S is m S,k , m S,k =w k β S,k , then the index e k The weighted confidence distribution of can be described as m k , Among them, m T(Θ),k Indicator e k The basic probability mass assigned to the power set.

[0086] For L causal indicators {e1,e2,...,e L}, assuming that their reliabilities are given by {r1,...,r L Then, the probability calculation formula of L causally related indicators jointly supporting the safety assessment level S of the complex system is:

[0087]

[0088] In the formula, β S,e(j) It represents the confidence of the first j causal indicators combined with the safety assessment level S of the complex system, j = 2, ..., L. m S,e(j) It is the joint probability mass assigned to the safety assessment level S of the complex system after combining the first j indicators; and represents the unnormalized combined probability mass of the complex system safety assessment level S and the power set T(Θ) in the first j indicators with causal relationships respectively; represents the joint probability mass assigned to the evaluation level C after combining the first j-1 indicators; Indicator e j The basic probability mass assigned to the evaluation grade F.

[0089] According to the above analysis, after L indicators with causal relationships are combined, a new composite indicator e can be generated. L , its probability distribution can be expressed as:

[0090]

[0091] In the formula, β S,e(L) It represents the confidence of the combination of L indicators relative to the safety assessment level S of the complex system, e (L) It can be regarded as a new indicator.

[0092] Step 1042: Determine the expected utility of the composite index, and use the expected utility as the safety assessment result of the complex system to be assessed. Assuming that the utility of state H is u(H), the expected utility of the composite index is expressed as:

[0093]

[0094] In the formula, u (L) represents the expected utility of the indicator combination result, S represents the safety assessment level of the complex system, Θ represents the safety assessment level of the N complex system S n The identification framework composed of S,e(L) It represents the confidence of the complex system safety assessment level S after the combination of L indicators, u(S) represents the expected utility of the complex system safety assessment level S, and e(L) represents the final assessment result after all indicators are combined.

[0095] Furthermore, based on the above description, a causal evidence reasoning complex system safety assessment model can be equivalently constructed, such as Figure 2As shown in the figure, in this model, the complex system safety assessment index system and historical data sets are used as the data basis, and the evidential reasoning method is used to mine causal relationships in real time, quantify causal relationships, and perform multi-source information fusion reasoning to evaluate the reliability of indicators, and then obtain the safety assessment results of the entire complex system, so as to deal with the safety assessment of causal relationships in evidence (i.e., indicators) and causal relationships in complex systems, thereby greatly improving the accuracy of evidential reasoning and its application in safety assessment of complex systems.

[0096] In an exemplary embodiment, a complex system is provided, which is a network system composed of different components interacting with each other nonlinearly. The complex system involved in this application includes: computer equipment, power grid, transportation, aerospace or communication system facilities. When the complex system executes the complex system safety assessment method of causal evidence reasoning provided above, safety assessment is achieved.

[0097] In an exemplary embodiment, an example of a complex system is provided, namely a computer device, which may be a server or a terminal, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store complex system security assessment data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a complex system security assessment method of causal evidence reasoning is implemented.

[0098] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0099] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0100] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0101] 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, stored data, displayed data, 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 relevant data must comply with relevant regulations.

[0102] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present 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 may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0103] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0104] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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.

[0105] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A complex system safety assessment method based on causal evidence reasoning, characterized in that: The complex system safety assessment method of causal evidential reasoning includes: Obtain historical evaluation data of complex systems and generate historical data sets; Constructing a complex system security assessment index system based on the historical data set; Obtain indicator data of the complex system to be evaluated; Using a causal evidence reasoning method, based on the complex system safety assessment index system, the causal relationship of each indicator in the index data is mined and quantified to obtain a causal relationship quantitative value; Based on the causal relationship quantification value, a causal evidence reasoning method is used to determine the safety assessment result of the complex system to be assessed.

2. The complex system safety assessment method based on causal evidence reasoning according to claim 1 is characterized in that: Obtain historical assessment data of complex systems and generate historical data sets, including: Acquire historical evaluation data of complex systems and preprocess the historical evaluation data; the preprocessing includes: data extraction, data cleaning and data conversion; The historical data set is generated based on the preprocessed historical evaluation data.

3. The complex system safety assessment method based on causal evidence reasoning according to claim 1 is characterized in that: Using the causal evidence reasoning method, based on the complex system safety assessment index system, the causal relationship of each indicator in the index data is mined and quantified to obtain the causal relationship quantitative value, including: Using a causal evidence reasoning method, based on the complex system safety assessment index system, a pairwise independence test is performed on any two indicators in the index data to obtain an independence test result; Adopting the complex system security assessment index system, determining the causal relationship between the indicators in the index data based on the independence test result, and using the indicators in the index data as nodes to generate an undirected graph; Performing a conditional independence test on each node in the undirected graph, and using a merging rule to direct the edges in the undirected graph based on the conditional independence test result to obtain a directed acyclic graph; The causal relationship between the nodes in the directed acyclic graph is quantified by using transfer entropy to obtain a causal relationship quantization value; the causal relationship between the nodes in the directed acyclic graph is represented by edges pointing between the nodes in the acyclic graph.

4. The complex system safety assessment method based on causal evidence reasoning according to claim 1 is characterized in that: Based on the quantitative value of the causal relationship, the safety assessment result of the complex system to be assessed is determined by using the causal evidence reasoning method, including: Combining multiple indicators with causal relationships into a composite indicator based on the causal relationship quantification value; The expected utility of the composite index is determined, and the expected utility is used as a safety assessment result of the complex system to be assessed.

5. The complex system safety assessment method based on causal evidence reasoning according to claim 4 is characterized in that: The expected utility of the composite index is expressed as: In the formula, u(L) represents the expected utility of the indicator combination result, S represents the safety assessment level of the complex system, Θ represents the safety assessment level of the N complex system S n The identification framework composed of S,e(L) It represents the confidence of the complex system safety assessment level S after the combination of L indicators, u(S) represents the expected utility of the complex system safety assessment level S, and e(L) represents the final assessment result after all indicators are combined.

6. The complex system safety assessment method based on causal evidence reasoning according to claim 1 is characterized in that: The complex system safety assessment method of causal evidence reasoning also includes: The complex system safety assessment index system is updated based on the historical assessment results or current assessment results of the complex system to be assessed.

7. The complex system safety assessment method based on causal evidence reasoning according to claim 6 is characterized in that: Update the complex system safety assessment index system based on the historical assessment results or current assessment results of the complex system to be assessed, including: Determine whether there are historical evaluation results for the complex system to be evaluated; When the complex system to be evaluated has a historical evaluation result, the historical evaluation result is added as an indicator to the complex system security evaluation indicator system; When there is no historical evaluation result for the complex system to be evaluated, the determined safety evaluation result of the complex system to be evaluated is added as an indicator to the complex system safety evaluation indicator system.

8. A complex system, wherein the complex system is a network system composed of different components that interact with each other nonlinearly, characterized in that: When the complex system executes the complex system safety assessment method of causal evidential reasoning as described in any one of claims 1 to 7, safety assessment is achieved.

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