Complex system risk quantitative analysis method, device, electronic device, and storage medium

Abstract

The present invention belongs to the field of risk management of complex systems, and provides a method, device, electronic device, and storage medium for quantitative analysis of risks of complex systems. The method includes: identifying risk factors of complex systems; establishing an adjacency matrix X of risk factors of complex systems to obtain a risk factor network diagram of complex systems; calculating the degree of each risk factor and the vulnerability of the relationship edge using a social network analysis method; and calculating the risk of each risk factor of the complex system. The method uses a social network analysis method to accurately measure the risk in a complex system, and by introducing key indicators such as degree, edge betweenness, network connectivity, and average network path length, quantitatively evaluates the risk level of each risk factor of the complex system, greatly reduces the subjective influence of expert experience judgment, and simplifies the calculation process.

Description

Technical Field

[0001] The present invention belongs to the field of complex system risk management, and in particular relates to a complex system risk quantitative analysis method, device, electronic equipment, and storage medium. Background Art

[0002] With the rapid development of science and technology, complex systems are increasingly used in various fields. Complex systems are usually composed of a large number of interrelated subsystems and components. The interactions and nonlinear dynamic behaviors between these subsystems and components make the overall performance of the system difficult to predict. For example, in the financial system, the complex connections between different financial institutions and markets will lead to systemic risks; in the aviation system, the mutual influence of factors such as aircraft, airlines, air traffic control and weather will increase operational risks.

[0003] Traditional risk analysis methods are mainly based on statistics, probability theory and empirical analysis. These methods usually regard the system as a linear and stable model, or focus only on the consideration of a single factor, thus ignoring the intricate interactions between the various factors within the system and their dynamic evolution characteristics. This limitation is particularly prominent when dealing with modern complex systems, because these systems often involve the interweaving of multi-dimensional and multi-level factors, and there is a high degree of interdependence and nonlinear relationships between the factors. Specifically, when dealing with complex system risk assessment, traditional methods often find it difficult to fully capture and integrate the multi-dimensional information and complex dependencies within the system, which in turn limits the comprehensiveness and depth of the assessment results. In addition, with the rapid development of big data technology, the massive data generated within complex systems has provided unprecedented opportunities for risk analysis, but at the same time has also posed severe challenges to the adaptability and simplicity of analysis methods. These data are not only huge in quantity, but also diverse in type and complex in structure. They are also accompanied by the difficulties of high dimensionality and heterogeneity of the data, as well as problems such as model interpretability, transparency, real-time response capability and computational complexity. In addition, in the process of construction and analysis, traditional risk analysis models often need to make a series of assumptions and approximate processing in order to simplify the problem. While simplifying the analysis process, these simplifications and assumptions inevitably introduce errors and uncertainties, which in turn affect the accuracy and reliability of risk assessment results. This impact is particularly significant when facing complex systems with high uncertainty and rapid changes.

[0004] Therefore, there is an urgent need for a new method that can more scientifically quantify the risks of complex systems and improve the efficiency and effectiveness of risk management. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method, device, electronic device, and storage medium for quantitative analysis of complex system risks to improve the accuracy and efficiency of complex system risk quantification.

[0006] The technical solution adopted by the present invention to solve the technical problem is:

[0007] The first aspect of the present invention provides a method for quantitative analysis of complex system risks, comprising the following steps:

[0008] S1: Identify risk factors in complex systems;

[0009] S2: Establish the adjacency matrix X of the risk factors of the complex system and obtain the risk factor network diagram of the complex system;

[0010] S3: Use social network analysis methods to calculate the degree of each risk factor and the vulnerability of the relationship edge;

[0011] S4: Calculate the risk level of each risk factor of a complex system.

[0012] Furthermore, the establishment of the adjacency matrix X of the risk factors of the complex system described in S2 includes the following steps:

[0013] ① Judge the causal relationship between the risk factors of complex systems. If one risk factor can directly cause another risk factor, the relationship between the two risk factors is represented by 1, otherwise it is recorded as 0;

[0014] ② Use a table to describe the relationship, establish the adjacency matrix X of the risk factors of the complex system, and then import it into the Ucinet 6.0 software to form a Ucinet network data;

[0015] ③Perform visualization processing to obtain a network diagram of risk factors for complex systems.

[0016] Furthermore, the step of calculating the degree of each risk factor and the vulnerability of the relationship edge by using the social network analysis method in S3 includes the following steps:

[0017] ① Calculate the out-degree of each node in the risk factor network diagram. The out-degree of the node represents the degree of loss of each risk event. The formula is:

[0018] ;

[0019] Where: K j is the out-degree value of the j-th risk factor, n is the number of columns in the risk adjacency matrix X, X pq represents the value of the pth row and qth column of the risk adjacency matrix X, where p=j;

[0020] ② Calculate the betweenness of each relationship edge in the complex system risk factor network diagram in turn. The betweenness represents the number of shortest paths passing through this edge.

[0021] ③ Remove the relationship edges of the complex system risk factor network graph in turn, calculate and count the average path length of the risk factor network graph after removing the relationship edges; the average path length is the average value of the shortest distances between all node pairs in the risk factor network graph;

[0022] ④ Remove the relationship edges of the risk factor network graph of the complex system in turn, calculate and count the connectivity of the risk factor network graph after removing the relationship edges; the connectivity is the ratio of the connected nodes in the risk factor network graph to the total number of nodes in the risk factor network graph;

[0023] ⑤ Calculate the vulnerability V of each relationship edge from ①-④ ij , the formula is:

[0024] ;

[0025] Where: V ij is the vulnerability of the relationship edge ij, B ij is the betweenness value of the relationship edge ij, L ij represents the average path length of the network after removing the relationship edge ij, H ij Represents the connectivity of the network after removing the relationship edge ij.

[0026] Furthermore, the step of calculating the risk level of each risk factor of the complex system in S4 includes the following steps:

[0027] ① Calculate the impact of each risk factor on other risk factors. The formula is:

[0028] ;

[0029] Where: I i→j is the influence of the ith risk factor on the jth risk factor, P ij is the probability of the relationship edge ij, K j is the out-degree value of the j-th risk factor, V ij is the vulnerability of the relationship edge ij;

[0030] ② Calculate the risk level of each risk factor using the following formula:

[0031] ;

[0032] Where: R i is the risk degree of the ith risk factor, and n is the number of risk factors.

[0033] The second aspect of the present invention is to provide a complex system risk quantification analysis device for implementing the above complex system risk quantification analysis method, which is used for complex system risk quantification analysis, and comprises:

[0034] Identification module, used to identify risk factors of complex systems;

[0035] The adjacency matrix and risk factor network diagram construction module is used to establish the adjacency matrix X of the risk factors of the complex system and obtain the risk factor network diagram of the complex system;

[0036] The calculation module uses social network analysis methods to calculate the degree of each risk factor and the vulnerability of the relationship edge, and further calculates the risk degree of each risk factor of the complex system.

[0037] The third aspect of the present invention is to provide an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the complex system risk quantitative analysis method by calling the computer program stored in the memory.

[0038] A fourth aspect of the present invention is to provide a storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the complex system risk quantitative analysis method are executed.

[0039] Advantages and beneficial effects of the present invention:

[0040] 1. The present invention makes full use of the network relationship structure and indicator attribute analysis process of the social network analysis method (SNA), which can comprehensively capture and integrate the multi-dimensional information and complex dependencies within the complex system, thereby improving the accuracy of the evaluation results; at the same time, through the visualization of the social network and indicator analysis, the results are more intuitive and easy to understand, which enhances the interpretability and transparency of risk assessment and facilitates the understanding and application of decision makers from different backgrounds.

[0041] 2. The calculation method of the present invention is relatively simple and can reduce the calculation complexity and cost while ensuring the evaluation accuracy. It is applicable to a variety of industries, such as finance, energy, aviation, manufacturing, etc., and is used to evaluate and manage potential risks in complex systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a network diagram of risk factors of a water supply system according to an embodiment of the present invention.

[0043] Figure 2 The block diagram is a schematic diagram of an exemplary configuration of a complex system risk quantitative analysis device according to an embodiment of the present invention.

[0044] Figure 3 The block diagram is a computing device for quantitatively analyzing risks of complex systems according to an embodiment of the present invention.

[0045] Among them, 100: complex system risk quantitative analysis device; 101: identification module; 102: adjacency matrix and risk factor network diagram construction module; 103: calculation module; 200: electronic device; 201: memory; 202: processor. DETAILED DESCRIPTION

[0046] The present invention is further described in detail below through specific examples. The following examples are only illustrative and not restrictive, and the protection scope of the present invention cannot be limited thereto.

[0047] The present invention provides a method for quantitative analysis of complex system risks, the steps of which are as follows:

[0048] S1: Identify risk factors in complex systems;

[0049] S2: Establish the adjacency matrix X of the risk factors of the complex system and obtain the risk factor network diagram of the complex system;

[0050] The adjacency matrix X of the complex system risk factors is established, comprising the following steps:

[0051] ① Judge the causal relationship between the risk factors of complex systems. If one risk factor can directly cause another risk factor, the relationship between the two risk factors is represented by "1", otherwise it is recorded as "0";

[0052] Assuming that the complex system has 20 risk factors, the causal relationship between the risk factors of the complex system is analyzed, and finally the judgment results are input into Table 1.

[0053] Table 1 Risk factor causal relationship judgment matrix

[0054] ;

[0055] ② Based on the results of the causal relationship judgment matrix of complex system risk factors, generate the adjacency matrix X of complex system risk factors.

[0056] ;

[0057] Among them: a i,20 is the impact of the i-th risk factor on the 20th risk factor, where i=1, 2, 3,…,20.

[0058] ③Import it into Ucinet 6.0 software to form a Ucinet network data, perform visualization processing, and obtain a risk factor network diagram of the complex system.

[0059] S3: Use social network analysis methods to calculate the degree of each risk factor and the vulnerability of the relationship edge;

[0060] The following steps are involved:

[0061] ① Calculate the out-degree of each node in the risk factor network diagram. The out-degree of the node represents the degree of loss of each risk event, as shown in formula (1);

[0062] (1);

[0063] Where: K j is the out-degree value of the j-th risk factor, n is the number of columns in the risk adjacency matrix X, X pq represents the value of the pth row and qth column of the risk adjacency matrix X, where p=j;

[0064] ② Calculate the betweenness of each relationship edge in the complex system risk factor network diagram in turn. The betweenness represents the number of shortest paths passing through this edge.

[0065] ③ Remove the relationship edges of the complex system risk factor network graph in turn, calculate and count the average path length of the risk factor network graph after removing the relationship edges; the average path length is the average value of the shortest distances between all node pairs in the risk factor network graph;

[0066] ④ Remove the relationship edges of the risk factor network graph of the complex system in turn, calculate and count the connectivity of the risk factor network graph after removing the relationship edges; the connectivity is the ratio of the connected nodes in the risk factor network graph to the total number of nodes in the risk factor network graph;

[0067] ⑤ Calculate the vulnerability V of each relationship edge from ①-④ ij , as shown in formula (2);

[0068] (2);

[0069] Where: V ij is the vulnerability of the relationship edge ij, B ij is the betweenness value of the relationship edge ij, L ij represents the average path length of the network after removing the relationship edge ij, H ij Represents the connectivity of the network after removing the relationship edge ij.

[0070] S4: Calculating the risk level of each risk factor of the complex system, wherein the calculating the risk level of each risk factor of the complex system comprises the following steps:

[0071] ① Calculate the influence of each risk factor on other risk factors, as shown in formula (3);

[0072] (3);

[0073] Where: I i→j is the influence of the ith risk factor on the jth risk factor, P ijis the probability of the relationship edge ij, K j is the out-degree of the j-th risk factor, V ij is the vulnerability of relationship edge ij.

[0074] ② Calculate the risk level of each risk factor, as shown in formula (4);

[0075] (4);

[0076] Where: R i is the risk degree of the ith risk factor, and n is the number of risk factors.

[0077] Taking the urban water supply system in a certain region as an example, a risk quantitative analysis is conducted. Through literature review and network search, 20 high-frequency and representative water supply system risk factors in the region are condensed, as shown in Table 2. The causal relationship between the risk factors of the water supply system is judged, and the adjacency matrix X is established. The adjacency matrix is ​​imported into Ucinet 6.0 software to form a Ucinet network data, which is visualized to obtain the risk factor network diagram, as shown in Figure 1 .

[0078] Table 2 Risk factors of water supply system

[0079] ;

[0080] The degree of each risk factor and the vulnerability of the relationship edge are calculated using the social network analysis method, as shown in Table 3.

[0081] Table 3 Degree (out-degree) of risk factors in water supply system

[0082] ;

[0083] The vulnerability of each relationship edge is calculated using formula (2), as shown in Table 4.

[0084] Table 4 Vulnerability of each edge of the water supply system risk network

[0085] ;

[0086] The influence of each risk factor on other risk factors is calculated by formula (3), as shown in Table 5.

[0087] Table 5 Water system network risk impact

[0088] ;

[0089] The risk level of each risk factor is calculated using formula (4), as shown in Table 6.

[0090] Table 6 Risk factors of water supply system

[0091] ;

[0092] From Table 6, we can find that R 15 Disputes, R4 water pollution, R1 climate change, R 12 Insufficient water pressure, R 13 Water outage and water shortage are the main risks affecting the safety of the water supply system in this area. Therefore, the region should focus on such risks, carry out targeted prevention and control, and improve the risk prevention and control capabilities of the urban water supply system.

[0093] like Figure 2 As shown, the present invention further provides a complex system risk quantitative analysis device 100 for implementing the above complex system risk quantitative analysis method, which is used for complex system risk quantitative analysis, and includes:

[0094] An identification module 101, for identifying risk factors of a complex system;

[0095] The adjacency matrix and risk factor network diagram construction module 102 is used to establish the adjacency matrix X of the risk factors of the complex system and obtain the risk factor network diagram of the complex system;

[0096] The calculation module 103 calculates the degree of each risk factor and the vulnerability of the relationship edge using a social network analysis method, and further calculates the risk degree of each risk factor of the complex system.

[0097] The details of the operations and functions of the various parts of the complex system risk quantitative analysis device 100 may be the same as or similar to the relevant parts of the embodiment of the complex system risk quantitative analysis method of the present disclosure described above, and will not be described in detail here.

[0098] It should be noted here that Figure 2 The structure of the complex system risk quantitative analysis device 100 and its components are only exemplary, and those skilled in the art may modify the structure as needed. Figure 2 Modify the structural block diagram shown.

[0099] The above complex system risk quantitative analysis device 100 may be implemented by hardware, software, or a combination of hardware and software.

[0100] In the present disclosure, the complex system risk quantitative analysis device 100 can be implemented using an electronic device 200. Figure 3 As shown, the electronic device 200 may include at least one processor 202, which executes at least one computer-readable instruction (ie, the above-mentioned element implemented in the form of software) stored or encoded in a computer-readable storage medium (ie, memory 201).

[0101] It should be understood that the computer executable instructions stored in the memory 201, when executed, cause the at least one processor 202 to perform various operations and functions described in the embodiments of the present disclosure.

[0102] According to one embodiment, a machine-readable storage medium is provided. The machine-readable storage medium may have machine-executable instructions (ie, the above-mentioned elements implemented in the form of software), which, when executed by a machine, enables the machine to perform various operations and functions in the embodiments of the present disclosure.

[0103] The above description is only a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several modifications and improvements without departing from the inventive concept, which all belong to the protection scope of the present invention.

Claims

1. A method for quantitative analysis of complex system risk, characterized in that: The following steps are involved: S1: Identify risk factors in complex systems; S2: Building an adjacency matrix of risk factors for complex systems X , and obtain the risk factor network diagram of the complex system; S3: Use social network analysis methods to calculate the degree of each risk factor and the vulnerability of the relationship edge; S4: Calculate the risk level of each risk factor of a complex system; The calculation of the risk level of each risk factor of the complex system comprises the following steps: ① Calculate the impact of each risk factor on other risk factors. The formula is: in: I i→j For the i The risk factors for j The impact of risk factors, P ij For relationship edge ij The probability of K j For the j The out-degree value of a risk factor, V ij For relationship edge ij the vulnerability of ② Calculate the risk level of each risk factor using the following formula: in: R i is the risk level of the ith risk factor, n is the number of risk factors; The adjacency matrix of complex system risk factors is established as described in S2 X The following steps are involved: ① Judge the causal relationship between the risk factors of complex systems. If one risk factor can directly cause another risk factor, the relationship between the two risk factors is represented by 1, otherwise it is recorded as 0; ② Use a table to describe the relationship and establish an adjacency matrix of complex system risk factors X , and then import it into Ucinet 6.0 software to form a Ucinet network data; ③ Perform visualization to obtain a risk factor network diagram of a complex system; The calculation of the degree of each risk factor and the vulnerability of the relationship edge using the social network analysis method described in S3 includes the following steps: ① Calculate the out-degree of each node in the risk factor network diagram. The out-degree of the node represents the degree of loss of each risk event. The formula is: in: K j For the j The out-degree value of a risk factor, n is the risk adjacency matrix X The number of columns, X pq Represents the risk adjacency matrix X No. p Line q The value of the column, where p = j ; ② Calculate the betweenness of each relationship edge in the complex system risk factor network diagram in turn. The betweenness represents the number of shortest paths passing through this edge. ③ Remove the relationship edges of the complex system risk factor network graph in turn, calculate and count the average path length of the risk factor network graph after removing the relationship edges; the average path length is the average value of the shortest distances between all node pairs in the risk factor network graph; ④ Remove the relationship edges of the risk factor network graph of the complex system in turn, calculate and count the connectivity of the risk factor network graph after removing the relationship edges; the connectivity is the ratio of the connected nodes in the risk factor network graph to the total number of nodes in the risk factor network graph; ⑤ Calculate the vulnerability of each relationship edge from ①-④ V ij , the formula is: in: V ij For relationship edge ij The vulnerability of B ij For relationship edge ij The betweenness value of L ij Indicates the removal of a relationship edge ij The average path length of the posterior network, H ij Indicates the removal of a relationship edge ij The connectivity of the network.

2. A complex system risk quantitative analysis device according to the complex system risk quantitative analysis method according to claim 1, used for complex system risk quantitative analysis, characterized in that: include: Identification module, used to identify risk factors of complex systems; Adjacency matrix and risk factor network graph construction module, used to build the adjacency matrix of risk factors in complex systems X , and obtain the risk factor network diagram of the complex system; The calculation module uses social network analysis methods to calculate the degree of each risk factor and the vulnerability of the relationship edge, and further calculates the risk degree of each risk factor of the complex system.

3. An electronic device, characterized in that: It comprises a processor and a memory, wherein a computer program is stored in the memory, and the processor is used to execute the steps of the complex system risk quantitative analysis method according to claim 1 by calling the computer program stored in the memory.

4. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the complex system risk quantitative analysis method according to claim 1 are executed.

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