CN110543615A - Risk factor interaction analysis method based on SPSS explanation structure model - Google Patents
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Abstract
the invention discloses a risk factor interaction analysis method based on an SPSS explanation structure model, which comprises the steps of identifying relevant factors; obtaining a correlation matrix by adopting a bivariate analysis algorithm; determining a threshold value, generating an adjacency matrix, and screening the incidence matrix through the threshold value; calculating a reachable matrix; performing hierarchical division on the reachable matrix; and drawing a multi-level hierarchical structure model. According to the method, through a correlation analysis method, the coupling correlation strength difference between risk factors is considered, the subjectivity is reduced, the method is more consistent with the actual situation, a fuzzy interpretation structure model is constructed, the conversion from qualitative to quantitative determination of the correlation among the influencing factors is realized, and the reasonability and the accuracy of an analysis result are ensured; therefore, the method has high objectivity and reliability, and is simple and practical.
Description
Technical Field
The invention particularly relates to a risk factor interaction analysis method based on an SPSS (shortest Path first) interpretation structure model.
background
With the popularization and application of a large number of distributed accesses and smart power grids, huge influence is inevitably generated on risk management of the power grids. On the one hand, compared with the traditional single power generation modes of firepower, water and nuclear power generation, the renewable clean energy sources such as wind energy, solar energy and tidal energy are connected from a large amount of distributed energy sources at the user side, so that the risk factors influencing the safety of the power grid are increased day by day, the unpredictability is enhanced, the risk factors are mutually influenced and coupled to form uncertain new risks, and the power grid system is developed more and more intricately. On the other hand, the risk factors are interactive rather than static, the increasing of the risk factors greatly increases the possibility that the risk factors are coupled to form an indeterminate risk, and when the risk factors are coupled, the risk factors are more complicated and difficult to prevent and control, inestimable loss is caused to the power grid, and great difficulty is increased to the risk management of the power grid. Therefore, the exploration of the coupling relation between the risk factors of the power grid is particularly important.
Risk coupling refers to the process that risk influencing factors are transmitted on a risk chain, when risks of other factors meet, interaction mechanisms such as counteraction and expansion occur, the risks are changed, and finally risk loss is caused by deviation of the process of prediction of risks by people, namely the process that two or more risk factors find linear or nonlinear mutual influence.
currently, an Interpretive Structural Model (ISM) is often used to analyze the interaction mechanism between risk factors. The basic idea of the method is as follows: judging the relevance and causality between every two risk factors, constructing an adjacent matrix of the risk factors by combining a method in a graph theory, constructing an accessible matrix of the adjacent matrix by using MATLAB simulation software, decomposing the accessible matrix, dividing the risk factors into a plurality of layers to form a multi-level hierarchical network model, determining the risk factors and the causal association influence thereof in different layers, and revealing the internal rule of the interaction of the risk factors.
According to the traditional explanation structure model, firstly, the coupling among risk factors is judged, an adjacent matrix of the risk factors is constructed by combining a method in a graph theory, an accessible matrix of the adjacent matrix is obtained through calculation, then the accessible matrix is decomposed, the risk factors are divided into a plurality of layers, a multi-level hierarchical network model is formed, the risk factors in different layers and the cause and effect correlation influence of the risk factors are determined, and the internal rule of the interaction of the risk factors is revealed.
The steps of adopting the traditional interpretation structure model algorithm to carry out the risk factor interaction analysis are as follows:
S1, identifying relevant factors; the modes of document analysis, expert interview, questionnaire and the like can be adopted;
S2, generating an adjacency matrix; inviting experts to judge the relevance and causality between every two risk factors, wherein the value of the relevance and causality is 0 or 1; 0 represents that no direct influence exists among the risk factors, namely no coupling effect exists, and 1 represents that direct influence exists among the risk factors, namely the coupling effect exists; establishing a direct relation matrix (adjacency matrix) A among all elements;
S3, generating a reachable matrix; for Si, Sj belongs to S, if any path exists from Si to Sj, the Si is called to reach Sj; calculating a direct relation matrix to obtain a reachable matrix P ═ An +1, wherein the reachable matrix P reflects direct relations and indirect relations among all elements in the system;
And S4, carrying out hierarchical division on the reachable matrix. The reachable matrix P is divided into two sets: r (si) set and a (si) set, and the intersection of r (si) and a (si) is calculated, and the element in r (si) ═ a (si) ═ r (si) is the top element of the system, that is, the element at the top level of MATLAB; after the highest-level elements are obtained, temporarily scratching out corresponding rows and columns of the highest-level elements in the reachable matrix, and thus obtaining a second layer and a third layer.
S5, drawing a multi-stage hierarchical structure model; after the hierarchical level is distributed, placing the first-level elements on the uppermost layer, placing the second-level elements below the uppermost layer, and sequentially placing the elements from top to bottom by analogy; finally, the rows and columns of the reachable matrix are also arranged according to the order of the level; the reachable matrix can use directed line segments to represent the relationship between adjacent level elements and the relationship between the same level elements, so that a directed graph can be used for representing the hierarchical structure of the system;
and S6, analyzing the interaction of the risk factors by using the drawn multi-level hierarchical structure model.
Description of the symbols: a is an adjacency matrix, P is a reachable matrix, n is the order of the adjacency matrix a, S is a risk factor set, Si, Sj represents the ith and jth risk factors in the set S, i, j is 1,2, …, n, r (Si) set is a set containing all the elements that can be reached by Si, called the reachable set of Si, and a (Si) set is an element containing all the elements that can reach Si, called the antecedent set of Si.
however, the traditional interpretation structure model only considers whether the risk factors have a mutual relationship, and the judgment of the relationship between the risk factors only has 2 values of 1 (influence) and 0 (no influence), and does not relate to the strength of the influence relationship. In practice, however, the strength of action between the risk factors is different. Moreover, whether the risk factors are affected by coupling is judged by an expert scoring method to form an adjacent matrix, so that the subjectivity is strong; finally, only whether the risk factors have mutual relations or not is considered, but the strength of the influence relations is not involved, and quantitative analysis is lacked.
disclosure of Invention
The invention aims to provide a risk factor interaction analysis method based on an SPSS (shortest Path first) interpretation structure model, which is high in objectivity and reliability, simple and practical.
the risk factor interaction analysis method based on the SPSS explanation structure model provided by the invention comprises the following steps:
S1, identifying relevant factors;
s2, obtaining an incidence matrix by adopting a bivariate analysis algorithm;
s3, determining a threshold value, generating an adjacent matrix, and screening the incidence matrix through the threshold value;
S4, calculating a reachable matrix;
s5, performing hierarchical division on the reachable matrix;
s6, drawing a multi-stage hierarchical structure model;
And S7, carrying out risk factor interaction analysis by adopting the multi-stage hierarchical structure model obtained in the step S6.
The identification of the relevant factors in the step S1 is specifically to identify the uncertain factors that may exist through investigation and research, on this basis, identify the key uncertain factors by using a fuzzy theory, and then identify the risk factors by using a genetic neural network model, thereby completing the screening of the key uncertain factors and ensuring the feasibility of the risk factor selection.
step S4, calculating a reachable matrix, specifically, for Si, Sj belongs to S, if any path exists from Si to Sj, Si is called reachable Sj; and calculating a direct relation matrix to obtain a reachable matrix P ═ An +1, wherein the reachable matrix P reflects direct relations and indirect relations among elements in the system.
In step S5, the reachable matrix is hierarchically divided, specifically, the reachable matrix P is divided into two sets: r (si) set and a (si) set, and the intersection of r (si) and a (si) is calculated, and the element in r (si) ═ a (si) ═ r (si) is the top element of the system, that is, the element at the top level of MATLAB; and after the highest-level elements are obtained, temporarily scratching out corresponding rows and columns of the highest-level elements in the reachable matrix, and thus obtaining a second layer and a third layer.
Step S6, drawing a multi-level hierarchical structure model, specifically, after the hierarchical level distribution is completed, placing the first-level elements on the top layer, placing the second-level elements below the top layer, and repeating the above steps to sequentially place the elements from top to bottom; finally, the rows and columns of the reachable matrix are also arranged according to the order of the level; the reachable matrix can represent the relationship between adjacent level elements and the relationship between the same level elements by directional line segments, so a directed graph can be used to represent the hierarchical structure of the system.
step S2, obtaining the correlation matrix by using a bivariate analysis algorithm, specifically, collecting historical data, that is, the value of the risk factor changing with time, generally speaking, the error between the actual value and the predicted value over the years, then completing the calculation and inspection of the correlation coefficient by using the bivariate analysis function in the SPSS software, analyzing the historical data, and calculating the coupling strength between the risk factors.
the calculation of the coupling strength among the risk factors specifically includes the following calculation:
in the formula, r is the coupling strength, xi and yi are ith sampling values of any 2 indexes x and y in the index class, and the sampling values are average values of the 2 index sampling data respectively.
the threshold is determined in step S4, specifically, the smaller the threshold is selected, the coarser the system partition is, and the larger the threshold is selected, the finer the system partition is.
Generating the adjacency matrix in step S4, specifically, processing the association matrix by using the following formula, so as to obtain the adjacency matrix a at the current threshold level as (aij) n × n:
Where aij is an element in the adjacency matrix a, rij is an element in the association matrix R, and λ is a threshold.
According to the risk factor interaction analysis method based on the SPSS interpretation structure model, provided by the invention, through a correlation analysis method, the coupling correlation strength difference between risk factors is considered, the subjectivity is reduced, the method is more consistent with the actual situation, a fuzzy interpretation structure model is constructed, the conversion from qualitative to quantitative determination of the mutual relation between the influence factors is realized, and the reasonability and the accuracy of an analysis result are ensured; therefore, the method has high objectivity and reliability, and is simple and practical.
drawings
FIG. 1 is a schematic process flow diagram of the process of the present invention.
FIG. 2 is a diagram of a four-level model structure according to an embodiment of the present invention.
Detailed Description
FIG. 1 is a schematic flow chart of the method of the present invention: the risk factor interaction analysis method based on the SPSS explanation structure model provided by the invention comprises the following steps:
s1, identifying relevant factors;
The method specifically comprises the steps of identifying uncertain factors possibly existing in the power grid investment process through investigation and research, on the basis, identifying key uncertain factors by adopting a fuzzy theory, then identifying risk factors existing in the power grid investment by adopting a genetic neural network model, completing screening of the key uncertain factors, and ensuring feasibility of selecting the risk factors.
S2, obtaining an incidence matrix by adopting a bivariate analysis algorithm; specifically, historical data are collected, a correlation analysis method is adopted to analyze the historical data, and the coupling effect strength among risk factors is calculated;
In specific implementation, the coupling strength is calculated by the following formula:
In the formula, r is coupling strength, xi and yi are ith sampling values of any 2 indexes x and y in the index class, and the sampling values are average values of the 2 index sampling data respectively;
Meanwhile, when the historical data is analyzed by a correlation analysis method, the correlation analysis in SPSS software can be adopted for analysis;
S3, determining a threshold value, generating an adjacent matrix, and screening the incidence matrix through the threshold value; in specific implementation, the smaller the threshold value selection, the coarser the system division, and the larger the threshold value selection, the finer the system division
S4, calculating a reachable matrix; specifically, for Si, Sj belongs to S, if any path exists from Si to Sj, Si is called to reach Sj; calculating a direct relation matrix to obtain a reachable matrix P ═ An +1, wherein the reachable matrix P reflects direct relations and indirect relations among all elements in the system; specifically, the correlation matrix is processed by using the following formula, so that an adjacency matrix a at the current threshold level is obtained as (aij) n × n:
wherein aij is an element in the adjacency matrix A, rij is an element in the incidence matrix R, and λ is a threshold;
s5, performing hierarchical division on the reachable matrix; specifically, the reachable matrix P is divided into two sets: r (si) set and a (si) set, and the intersection of r (si) and a (si) is calculated, and the element in r (si) ═ a (si) ═ r (si) is the top element of the system, that is, the element at the top level of MATLAB; after the highest-level elements are obtained, temporarily scratching out corresponding rows and columns of the highest-level elements in the reachable matrix, and thus obtaining a second layer and a third layer.
S6, drawing a multi-stage hierarchical structure model; after the hierarchical level distribution is finished, placing the first-level elements on the uppermost layer, placing the second-level elements below the uppermost layer, and repeating the above steps to sequentially place the elements from top to bottom; finally, the rows and columns of the reachable matrix are also arranged according to the order of the level; the reachable matrix can use directed line segments to represent the relationship between adjacent level elements and the relationship between the same level elements, so that a directed graph can be used for representing the hierarchical structure of the system;
And S7, carrying out risk factor interaction analysis by adopting the multi-stage hierarchical structure model obtained in the step S6.
The method of the invention can be used for evaluating the risk of power grid investment: through the constructed multi-level hierarchical structure model, the conversion relation between risks is determined, the risk factor at the top layer is the main risk influencing the investment of the power grid, the risk factor at the bottom layer is the risk source influencing the investment of the power grid, and the risk at the middle level forms a conversion relation chain from the risk at the bottom layer to the risk at the top layer. Through the constructed multi-level hierarchical model, the interaction mechanism among risks can be known, the risk dimensionality reduction is realized in the aspect of risk quantification, and the risk analysis is simplified. In the aspect of risk prevention, risks are prevented from being generated from the bottom layer, the propagation of the risks is blocked from the middle layer, and the risk influence is reduced from each link.
the process of the invention is further illustrated below with reference to one example:
if 16 risk factors are identified for a certain power grid investment risk, namely R1, R2, R3, R.fara and R16, correlation analysis is performed on collected historical data to obtain the correlation degree among the risk factors, as shown in Table 1:
TABLE 1 matrix table of degree of association
| R1 | R2 | R3 | R4 | R5 | R6 | R7 | R8 | R9 | R10 | R11 | R12 | R13 | R14 | R15 | R16 | |
| R1 | 1.00 | 0.77 | 0.68 | 0.85 | 0.79 | 0.75 | 0.76 | 0.85 | 0.73 | 0.77 | 0.74 | 0.88 | 0.87 | 0.72 | 0.78 | 0.79 |
| R2 | 0.77 | 1.00 | 0.69 | 0.85 | 0.80 | 0.81 | 0.71 | 0.73 | 0.75 | 0.75 | 0.75 | 0.78 | 0.71 | 0.71 | 0.72 | 0.79 |
| R3 | 0.68 | 0.69 | 1.00 | 0.79 | 0.76 | 0.76 | 0.70 | 0.72 | 0.70 | 0.71 | 0.72 | 0.78 | 0.72 | 0.76 | 0.69 | 0.78 |
| R4 | 0.85 | 0.85 | 0.79 | 1.00 | 0.81 | 0.81 | 0.78 | 0.81 | 0.78 | 0.79 | 0.77 | 0.84 | 0.79 | 0.76 | 0.75 | 0.85 |
| R5 | 0.79 | 0.80 | 0.76 | 0.81 | 1.00 | 0.75 | 0.68 | 0.72 | 0.68 | 0.66 | 0.67 | 0.77 | 0.71 | 0.76 | 0.78 | 0.75 |
| R6 | 0.75 | 0.81 | 0.76 | 0.81 | 0.75 | 1.00 | 0.70 | 0.73 | 0.79 | 0.79 | 0.80 | 0.80 | 0.75 | 0.73 | 0.68 | 0.86 |
| R7 | 0.76 | 0.71 | 0.70 | 0.78 | 0.68 | 0.70 | 1.00 | 0.82 | 0.81 | 0.81 | 0.72 | 0.85 | 0.81 | 0.75 | 0.73 | 0.79 |
| R8 | 0.85 | 0.73 | 0.72 | 0.81 | 0.72 | 0.73 | 0.82 | 1.00 | 0.80 | 0.82 | 0.75 | 0.91 | 0.86 | 0.74 | 0.73 | 0.81 |
| R9 | 0.73 | 0.75 | 0.70 | 0.78 | 0.68 | 0.79 | 0.81 | 0.80 | 1.00 | 0.82 | 0.75 | 0.82 | 0.75 | 0.71 | 0.67 | 0.81 |
| R10 | 0.77 | 0.75 | 0.71 | 0.79 | 0.66 | 0.79 | 0.81 | 0.82 | 0.82 | 1.00 | 0.80 | 0.86 | 0.82 | 0.73 | 0.67 | 0.83 |
| R11 | 0.74 | 0.75 | 0.72 | 0.77 | 0.67 | 0.80 | 0.72 | 0.75 | 0.75 | 0.80 | 1.00 | 0.79 | 0.75 | 0.72 | 0.68 | 0.83 |
| R12 | 0.88 | 0.78 | 0.78 | 0.84 | 0.77 | 0.80 | 0.85 | 0.91 | 0.82 | 0.86 | 0.79 | 1.00 | 0.85 | 0.71 | 0.70 | 0.83 |
| R13 | 0.87 | 0.81 | 0.72 | 0.79 | 0.71 | 0.75 | 0.81 | 0.86 | 0.75 | 0.82 | 0.75 | 0.85 | 1.00 | 0.66 | 0.74 | 0.78 |
| R14 | 0.72 | 0.71 | 0.76 | 0.76 | 0.76 | 0.73 | 0.75 | 0.74 | 0.71 | 0.73 | 0.72 | 0.71 | 0.66 | 1.00 | 0.76 | 0.76 |
| R15 | 0.78 | 0.72 | 0.69 | 0.75 | 0.78 | 0.68 | 0.73 | 0.73 | 0.67 | 0.67 | 0.68 | 0.70 | 0.74 | 0.76 | 1.00 | 0.74 |
| R16 | 0.79 | 0.79 | 0.78 | 0.85 | 0.75 | 0.86 | 0.79 | 0.81 | 0.81 | 0.83 | 0.83 | 0.83 | 0.78 | 0.76 | 0.74 | 1.00 |
Through expert discussion, a threshold value λ of 0.82 is selected to perform data screening on the association degree matrix, so as to obtain an adjacency matrix a, whose values are shown in table 2:
TABLE 2 adjacency matrix table
| R1 | R2 | R3 | R4 | R5 | R6 | R7 | R8 | R9 | R10 | R11 | R12 | R13 | R14 | R15 | R16 | |
| R1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 |
| R2 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| R3 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| R4 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| R5 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| R6 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| R7 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| R8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 |
| R9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| R10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 |
| R11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
| R12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 |
| R13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| R14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| R15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| R16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
through boolean calculations, a reachable matrix P is obtained, whose values are shown in table 3:
TABLE 3 reachable matrix table
| R1 | R2 | R3 | R4 | R5 | R6 | R7 | R8 | R9 | R10 | R11 | R12 | R13 | R14 | R15 | R16 | |
| R1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 |
| R2 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 |
| R3 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| R4 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 |
| R5 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| R6 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| R7 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 |
| R8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 |
| R9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 |
| R10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 |
| R11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
| R12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 |
| R13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| R14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| R15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| R16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
According to the reachable matrix, the explanation structure model of the power grid investment risk can be divided into four layers, the bottom layer contains five risk factors which are respectively R3, R5, R13, R14 and R16 and represent the most fundamental key influence factors of the power grid investment project development; the second layer contains four risk factors which are respectively R6, R11, R12 and R15, the third layer contains five risk factors which are respectively R4, R7, R8, R9 and R10, the topmost layer contains two risk factors which are respectively R1 and R2, and the risk factors represent the most direct key influence factors of the power grid investment project. The structure diagram of the four-stage model is shown in FIG. 2.
As can be seen from the four-level model structure diagram of fig. 2, the risk factors of the respective layers are independent from each other, where R3, R5, R13, R14, and R16 are taken as source risks and directly affect the risk factors R6, R11, and R12 of the second layer, and the risk factors of the second layer directly affect the risk factors R4, R7, R8, R9, and R10 of the third layer, thereby forming an action process from the source risks to the direct risks affecting the project. By preventing the risk of the source at the bottommost layer from being generated and blocking the propagation of the risks at the second layer and the third layer, the influence of the risks on the whole project can be reduced from the risk generation root and the risk propagation process.
Claims (10)
1. a risk factor interaction analysis method based on an SPSS explanation structure model comprises the following steps:
S1, identifying relevant factors;
s2, obtaining an incidence matrix by adopting a bivariate analysis algorithm;
S3, determining a threshold value, generating an adjacent matrix, and screening the incidence matrix through the threshold value;
s4, calculating a reachable matrix;
s5, performing hierarchical division on the reachable matrix;
s6, drawing a multi-stage hierarchical structure model;
And S7, carrying out risk factor interaction analysis by adopting the multi-stage hierarchical structure model obtained in the step S6.
2. The risk factor interaction analysis method based on the SPSS interpretation structure model according to claim 1, wherein the identification of the relevant factors in step S1 is specifically to identify possible uncertain factors through research and study, and on this basis, a fuzzy theory is used to identify key uncertain factors, and then a genetic neural network model is used to identify risk factors, so as to complete the screening of key uncertain factors and ensure the feasibility of risk factor selection.
3. the SPSS-interpretation structure model-based risk factor interaction analysis method according to claim 1, wherein the calculation of the reachable matrix in step S5 is performed, specifically for Si, Sj ∈ S, if any path exists from Si to Sj, Si is called reachable Sj; and calculating a direct relation matrix to obtain a reachable matrix P ═ An +1, wherein the reachable matrix P reflects direct relations and indirect relations among elements in the system.
4. the risk factor interaction analysis method based on the SPSS interpreted structure model according to claim 1, wherein the reachable matrices are hierarchically divided in step S6, specifically, the reachable matrices P are divided into two sets: r (si) set and a (si) set, and the intersection of r (si) and a (si) is calculated, and the element in r (si) ═ a (si) ═ r (si) is the top element of the system, that is, the element at the top level of MATLAB; and after the highest-level elements are obtained, temporarily scratching out corresponding rows and columns of the highest-level elements in the reachable matrix, and thus obtaining a second layer and a third layer.
5. The method for analyzing risk factor interaction based on SPSS interpretation of structural model according to claim 1, wherein the step S7 of drawing a multi-level hierarchical structural model is to put the top level element and the bottom level element at the top level after the hierarchical level assignment is completed, and then sequentially put the elements from top to bottom; finally, the rows and columns of the reachable matrix are also arranged according to the order of the level; the reachable matrix can represent the relationship between adjacent level elements and the relationship between the same level elements by directional line segments, so a directed graph can be used to represent the hierarchical structure of the system.
6. the risk factor interaction analysis method based on the SPSS interpretation structure model according to any one of claims 1 to 5, wherein the step S2 is performed by obtaining the correlation matrix by using a bivariate analysis algorithm, specifically, by collecting historical data, and then performing calculation and inspection of the correlation coefficient by using the bivariate analysis algorithm, analyzing the historical data, and calculating the coupling strength between the risk factors.
7. the SPSS-interpreted structure model-based risk factor interaction analysis method of claim 6, wherein the calculation of the coupling strength between the risk factors is specifically performed by calculating the coupling strength according to the following equation:
In the formula, r is the coupling strength, xi and yi are ith sampling values of any 2 indexes x and y in the index class, and the sampling values are average values of the 2 index sampling data respectively.
8. the risk factor interaction analysis method based on the SPSS interpretation structure model according to any one of claims 1 to 5, wherein the step S3 is performed by using a bivariate analysis algorithm to obtain the correlation matrix, specifically, the calculation and inspection of the correlation coefficient are performed by using a bivariate analysis function in SPSS software.
9. The risk factor interaction analysis method based on the SPSS interpretation structure model according to any one of claims 1 to 5, wherein the threshold is determined in step S4, and specifically, the smaller the threshold is selected, the coarser the system partition is, and the larger the threshold is selected, the finer the system partition is.
10. The risk factor interaction analysis method based on the SPSS interpreted structure model according to any one of claims 1 to 5, wherein the adjacency matrix is generated in step S4, and specifically, the association matrix is processed by using the following formula, so as to obtain an adjacency matrix a ═ n (aij) n × n at the current threshold level:
Where aij is an element in the adjacency matrix a, rij is an element in the association matrix R, and λ is a threshold.
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