A flood disaster resilience analysis method and system based on mediating effect

Abstract

The present invention proposes a flood disaster resilience analysis method and system based on the mediating effect, including: collecting data, establishing a flood disaster resilience interaction system, dividing the threshold of the indicator state of the factors affecting flood disaster resilience, and obtaining indicators of flood disaster resilience in different dimensions; using the mediating effect to preprocess the data, performing correlation analysis on the indicators of flood disaster resilience, and constructing the transmission relationship of each indicator; using the explanatory structural model, constructing a multidimensional network hierarchical structure of flood disaster resilience based on the transmission relationship of each indicator, and analyzing flood disaster resilience. Using the mediating effect, a quantitative correlation calculation method is used for the indicators of flood disaster resilience, quantitative and directional transmission relationships between different indicators are obtained, the multi-level hierarchical relationship of flood disaster resilience indicators is determined, and a multidimensional network hierarchical structure of flood disaster resilience is constructed, which can accurately analyze flood disaster resilience.

Description

Technical Field

[0001] The present invention relates to the field of natural disaster risk and comprehensive disaster reduction technology, and more specifically, to a flood disaster resilience analysis method and system based on intermediary effect. Background Art

[0002] Flood disaster resilience, also known as flood disaster elasticity or flood disaster resistance, refers to the ability of social forces to effectively use various resources to successfully adapt to and respond to risks and quickly restore various social functions under the interference of flood disasters. In the actual analysis and research on flood disaster resilience, there are the following three difficulties. First, the influencing factors of flood disasters are complicated; second, the transmission relationship between the influencing factors of flood disasters is difficult to characterize; finally, the quantitative evaluation system of flood disaster resilience has not yet been determined. The above three points show that the current flood disaster resilience has the characteristics of systematicity, multi-dimensionality, intersectionality and complexity, which makes it difficult to scientifically and accurately propose flood disaster prevention and mitigation decision-making guidance.

[0003] In response to this phenomenon, scholars at home and abroad have proposed several solutions. Feng Qianqian et al. summarized the application of cluster analysis and principal component analysis in flood disaster resilience from the perspective of model construction. From a multi-dimensional analysis, they also achieved good results in flood disaster resilience analysis. (Feng Qianqian, Liu Delin. Factors affecting flood resilience in urban flood disasters and their evaluation - A case study of Hongqi District, Xinxiang City, Henan Province., 2017, 37(04))

[0004] However, the above methods emphasize the multi-level tree structure in terms of the structural function of flood disaster resilience, and pay less attention to the network relationship between each level and the interactive relationship between each influencing factor. At the same time, for the complex system of flood disaster resilience, the fundamental influencing factors are not clearly defined, so that the analysis of flood disaster resilience has the problem of unclear multi-level hierarchical relationships. Summary of the invention

[0005] In order to overcome the defect of unclear multi-level hierarchical relationship in the above-mentioned prior art, the present invention provides a flood disaster resilience analysis method and system based on intermediary effect.

[0006] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0007] In a first aspect, the present invention proposes a flood disaster resilience analysis method based on the intermediary effect, comprising the following steps:

[0008] Step 1: According to the factors affecting flood disaster resilience, collect the corresponding influencing factor data;

[0009] Step 2: According to the preset indicator state division threshold, the indicator state threshold is divided for the influencing factor data to obtain indicators of flood disaster resilience in different dimensions;

[0010] Step 3: Use the mediation effect to preprocess the data, conduct correlation analysis on the indicators of flood disaster resilience, and construct the transmission relationship of each indicator;

[0011] Step 4: Using the explanatory structural model, a multidimensional network hierarchical structure of flood disaster resilience is constructed based on the transmission relationship of each indicator to analyze flood disaster resilience.

[0012] Preferably, the Step 2 specifically includes the following steps:

[0013] Step 2.1: Establish a flood disaster resilience interactive system, divide the flood disaster resilience interactive system into several subsystems, and each subsystem contains the threshold of flood disaster resilience assessment indicators in the corresponding dimension;

[0014] Step 2.2: Input the data of influencing factors of flood disaster resilience into the flood disaster resilience interactive system. The flood disaster resilience interactive system divides the threshold of indicator status of the influencing factors of flood disaster resilience based on the isometry method to obtain indicators of flood disaster resilience in different dimensions.

[0015] Preferably, in Step 1, the influencing factors of flood disaster resilience include stakeholders involved in flood disaster resilience, resource characteristics, internal and external environment, and social economy.

[0016] Preferably, in Step 2.1, the subsystems include an environmental subsystem, a social subsystem, an economic subsystem, a psychological subsystem, an institutional subsystem and an information communication subsystem.

[0017] Preferably, the Step 3 specifically includes the following steps:

[0018] Step 3.1: Consider the indicators of flood disaster resilience as variables, establish the correlation between the variables based on the mediation effect theory, and obtain the influence relationship between different indicators on flood disaster resilience;

[0019] Step 3.2: Based on the collected influencing factor data, numerical statistics are performed on the indicators of flood disaster resilience in different dimensions to determine the degree of direct impact between different indicators;

[0020] Step 3.3: Using the direct impact degree between different indicators, construct the direct impact matrix X of the flood disaster resilience system. The formula is as follows:

[0021]

[0022] Among them, x ij Represents the i-th index S i For the jth index S j The degree of direct influence; if i = j, then x ij =0;

[0023] Step 3.4: Normalize the direct impact matrix X of the flood disaster resilience system to obtain the normalized matrix G, whose formula is as follows:

[0024]

[0025] Among them, g ij Represents the i-th index S i For the jth index S j Normalized value of the direct impact degree, 0≤g ij ≤1 and

[0026] Step 3.5: Calculate the comprehensive impact matrix T between indicators of each dimension of the flood disaster resilience system:

[0027] T=[t ij ] n×n =G(IG) -1

[0028] Among them, t ji is the element of the comprehensive influence matrix T, and I is the unit matrix.

[0029] Preferably, in Step 3.2, the method of determining the degree of direct impact between different indicators specifically includes:

[0030] According to the collected influencing factor data, the numerical statistics of the indicators of flood disaster resilience in different dimensions are carried out to determine the influencing values ​​of the indicators. If the indicator S i The value is proportional to the resilience of flood disasters, so the index S i Included in the positive impact index set z of flood disaster resilience; if the index S i The value is inversely proportional to the resilience of flood disasters, so the index S i Included in the negative impact indicator set b of flood disaster resilience;

[0031] The range transformation method is used to standardize the index value and obtain the standard value of the positive impact index z i and negative impact index standard value b i , the formula is as follows:

[0032]

[0033]

[0034] in, Indicator S i The maximum value of Indicator S i According to the standardized positive impact index standard value z i and negative impact index standard value b i , the direct impact degree between different indicators of flood disaster resilience can be quantified, thereby determining the direct impact degree between different indicators.

[0035] Preferably, the Step 4 specifically includes the following steps:

[0036] Step 4.1: Calculate the index S i The influence of i and the degree of influence i , and its calculation formula is as follows:

[0037]

[0038]

[0039] Step 4.2: Calculate the index S i Centrality m i and the degree of cause n i , and its calculation formula is as follows:

[0040] m i =f i +e i , (i=1,...,n)

[0041] n i =f i -e i , (i=1, ..., n);

[0042] Step 4.3: Calculate the overall impact matrix of the flood disaster resilience system as H, and its calculation formula is as follows:

[0043] H=[h ij ] n×n =T+I

[0044] Among them, h ij is the element of the overall influence matrix H;

[0045] Step 4.4: Calculate the reachable matrix K of the flood disaster resilience system. The calculation formula is as follows:

[0046] K=[k ij ] n×n

[0047] k ij ={1|hij ≥λ}, (i=1,...,n; j=1,...,n)

[0048] k ij ={0|h ij <λ}, (i=1,...,n; j=1,...,n)

[0049] Among them, k ij is the element of the reachable matrix K, and the threshold λ is a configurable parameter;

[0050] Step 4.5: Determine the antecedent set A i and reachable set R i , the formula is as follows:

[0051] A i ={s i |s i ∈S,K ij ≠0, i=1, 2, ..., n}

[0052] R i ={s j |s j ∈S,K ij ≠0, i=1, 2, ..., n}

[0053] Among them, if R i ∩A i =R i , then it represents the reachable set R i Included in the antecedent set A i In the reachable set R i The indicator can be found in the antecedent set A i Found in;

[0054] Step 4.6: Using the reachable set R i Build an index set for all elements in S i The corresponding rows and columns are deleted from the reachable matrix K, and the indicators are divided into different levels to obtain the multidimensional network hierarchical structure of flood disaster resilience, which is used to analyze flood disaster resilience.

[0055] In a second aspect, the present invention further proposes a flood disaster resilience analysis system based on the intermediary effect, which is applied to the flood disaster resilience analysis method based on the intermediary effect described in any of the above schemes, comprising:

[0056] A data collection module is used to collect corresponding influencing factor data according to the influencing factors of flood disaster resilience;

[0057] A threshold division module is used to divide the threshold of the indicator state according to the preset indicator state, divide the threshold of the indicator state of the influencing factor data, and obtain indicators of flood disaster resilience in different dimensions;

[0058] The indicator transfer relationship construction module is used to use the mediating effect to preprocess the data, conduct correlation analysis on the indicators of flood disaster resilience, and construct the transfer relationship of each indicator;

[0059] The multidimensional network construction module is used to construct the multidimensional network hierarchical structure of flood disaster resilience based on the transfer relationship of each indicator using the explanatory structural model;

[0060] The analysis module is used to analyze flood disaster resilience based on the multi-dimensional network hierarchy structure of flood disaster resilience.

[0061] Preferably, in the indicator transfer relationship construction module, the transfer relationship of each indicator is constructed by using the direct influence matrix and the comprehensive influence matrix between indicators.

[0062] Compared with the prior art, the beneficial effects of the technical solution of the present invention are: the present invention utilizes the mediating effect, adopts a quantitative correlation calculation method for the indicators of flood disaster resilience, obtains the quantitative and directional transmission relationship between different indicators, determines the multi-level hierarchical relationship of flood disaster resilience indicators, and constructs a multi-dimensional network hierarchical structure of flood disaster resilience, which can accurately analyze flood disaster resilience. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a flow chart of the flood disaster resilience analysis method based on mediating effect.

[0064] Figure 2 This is the schematic diagram of the flood disaster resilience analysis system based on the mediating effect.

[0065] Figure 3 This is a multi-dimensional network hierarchical structure diagram of flood disaster resilience. DETAILED DESCRIPTION

[0066] The drawings are for illustrative purposes only and should not be construed as limiting the present patent;

[0067] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0068] Example 1

[0069] See also Figure 1 and Figure 3 This embodiment proposes a flood disaster resilience analysis method based on the intermediary effect, including the following steps:

[0070] Step 1: Collect the corresponding influencing factor data according to the influencing factors of flood disaster resilience. In this embodiment, questionnaire surveys, interviews, natural geography and hydrological environment collection and other methods are used to collect and organize the influencing factor data of flood disaster resilience. The influencing factors of flood disaster resilience involved include stakeholders, resource characteristics, internal and external environment, and socio-economic aspects involved in flood disaster resilience.

[0071] Step 2: According to the preset indicator state division threshold, the indicator state threshold is divided for the influencing factor data to obtain indicators of flood disaster resilience in different dimensions;

[0072] The Step 2 specifically includes the following steps:

[0073] Step 2.1: Establish a flood disaster resilience interactive system, and divide the flood disaster resilience interactive system into several subsystems. Each subsystem contains the threshold value of flood disaster resilience assessment indicators in the corresponding dimension. Flood disaster resilience is a multi-indicator system involving multiple dimensions of environment, society, economy, psychology, system and information communication. In this embodiment, the flood disaster resilience interactive system is divided into an environmental subsystem, a social subsystem, an economic subsystem, a psychological subsystem, an institutional subsystem and an information communication subsystem.

[0074] Step 2.2: Input the data of influencing factors of flood disaster resilience into the flood disaster resilience interactive system. The flood disaster resilience interactive system divides the threshold of indicator status of the influencing factors of flood disaster resilience based on the isometry method to obtain indicators of flood disaster resilience in different dimensions.

[0075] Step 3: Use the mediation effect to preprocess the data, conduct correlation analysis on the indicators of flood disaster resilience, and construct the transmission relationship of each indicator;

[0076] The Step 3 specifically includes the following steps:

[0077] Step 3.1: Consider the indicators of flood disaster resilience as variables, establish the correlation between the variables based on the mediation effect theory, and obtain the influence relationship between different indicators on flood disaster resilience;

[0078] Step 3.2: According to the collected influencing factor data, numerical statistics are performed on the indicators of flood disaster resilience in different dimensions to determine the degree of direct influence between different indicators. In this embodiment, the method of determining the degree of direct influence between different indicators specifically includes:

[0079] According to the collected influencing factor data, the numerical statistics of the indicators of flood disaster resilience in different dimensions are carried out to determine the influencing values ​​of the indicators. If the indicator S i The value is proportional to the resilience of flood disasters, so the index Si Included in the positive impact index set z of flood disaster resilience; if the index S i The value is inversely proportional to the resilience of flood disasters, so the index S i Included in the negative impact indicator set b of flood disaster resilience;

[0080] The range transformation method is used to standardize the index value and obtain the standard value of the positive impact index z i and negative impact index standard value b i , the formula is as follows:

[0081]

[0082]

[0083] in, Indicator S i The maximum value of Indicator S i According to the standardized positive impact index standard value z i and negative impact index standard value b i , the direct impact degree between different indicators of flood disaster resilience can be quantified, thereby determining the direct impact degree between different indicators. The results are shown in Table 1.

[0084] Let [0, 0.25) be no influence between indicators, [0.25, 0.5) be weak influence between indicators, [0.5, 0.75) be average influence between indicators, and [0.75, 1] ​​be strong influence between indicators.

[0085] Table 1 Quantitative table of direct impact between different indicators of flood disaster resilience

[0086]

[0087] Step 3.3: Using the direct impact degree between different indicators, construct the direct impact matrix X of the flood disaster resilience system. The formula is as follows:

[0088]

[0089] Among them, x ij Represents the i-th index S i For the jth index S j The degree of direct influence; if i = j, then x ij =0; the calculation results of the matrix X that are directly affected are shown in Table 2.

[0090] Table 2 Direct impact matrix between different flood disaster resilience indicators

[0091]

[0092] Step 3.4: Normalize the direct impact matrix X of the flood disaster resilience system to obtain the normalized matrix G, whose formula is as follows:

[0093]

[0094] Among them, g ij Represents the i-th index S i For the jth index S j Normalized value of the direct impact degree, 0≤g ij ≤1 and

[0095] Step 3.5: Calculate the comprehensive impact matrix T between indicators of each dimension of the flood disaster resilience system:

[0096] T=[t ij ] n×n =G(IG) -1

[0097] Among them, t ji is the element of the comprehensive influence matrix T, and I is the unit matrix. The calculation results of the comprehensive influence matrix T are shown in Table 3.

[0098] Table 3 Comprehensive impact matrix among flood disaster resilience indicators

[0099]

[0100] Step 4: Using the explanatory structural model, a multidimensional network hierarchical structure of flood disaster resilience is constructed based on the transmission relationship of each indicator to analyze flood disaster resilience.

[0101] The Step 4 specifically includes the following steps:

[0102] Step 4.1: Calculate the index S i The influence of i and the degree of influence i , and its calculation formula is as follows:

[0103]

[0104]

[0105] Step 4.2: Calculate the index S i Centrality m i and the degree of cause n i , and its calculation formula is as follows:

[0106] m i=f i +e i , (i=1,...,n)

[0107] n i =f i -e i , (i=1, ..., n);

[0108] Centrality m i and the degree of cause n i The calculation results are shown in Table 4.

[0109] Table 4 Centrality and causality

[0110]

[0111] Step 4.3: Calculate the overall impact matrix of the flood disaster resilience system as H, and its calculation formula is as follows:

[0112] H=[h ij ] n×n =T+I

[0113] Among them, h ij is the element of the overall influence matrix H;

[0114] Step 4.4: Calculate the reachable matrix K of the flood disaster resilience system. The calculation formula is as follows:

[0115] K=[k ij ] n×n

[0116] k ij ={1|h ij ≥λ}, (i=1,...,n; j=1,...,n)

[0117] k ij ={0|h ij <λ}, (i=1,...,n; j=1,...,n)

[0118] Among them, k ij is the element of the reachable matrix K, and the threshold λ is a configurable parameter. The calculation results of the reachable matrix K are shown in Table 5.

[0119] Table 5. Reachability matrix between flood disaster resilience indicators

[0120]

[0121]

[0122] Step 4.5: Determine the antecedent set A i and reachable set Ri , the formula is as follows:

[0123] A i ={s i |s i ∈S,K ij ≠0, i=1, 2, ..., n}

[0124] R i ={s j |s j ∈S,K ij ≠0, i=1, 2, ..., n}

[0125] Among them, if R i ∩A i =R i , then it represents the reachable set R i Included in the antecedent set A i In the reachable set R i The indicator can be found in the antecedent set A i Found in;

[0126] Step 4.6: Using the reachable set R i Build an index set for all elements in S i The corresponding rows and columns are deleted from the reachable matrix K, and the indicators are divided into different levels to obtain the multidimensional network hierarchy structure of flood disaster resilience, which is used to analyze flood disaster resilience. Figure 3 As shown, Figure 3 This is a multi-dimensional network hierarchical structure diagram of flood disaster resilience in this embodiment.

[0127] In the multidimensional network hierarchy, the indicators at the first level are direct influencing factors in the flood disaster resilience system and are affected by other factors, such as S3, S8, S 13 , S 14 ; The indicators at the last level are fundamental influencing factors in the flood disaster resilience system, affecting other factors, such as S2 and S6; the indicators at the middle level are transitional factors, and the fundamental factors use transitional factors as channels to influence the direct influencing factors, thus ultimately affecting flood disaster resilience.

[0128] This embodiment uses the intermediary effect to calculate the quantitative correlation of flood disaster resilience indicators, obtains quantitative and directional transmission relationships between different indicators, determines the multi-level hierarchical relationship of flood disaster resilience indicators, and constructs a multi-dimensional network hierarchical structure of flood disaster resilience for different types of communities or under different disaster conditions, which can accurately analyze flood disaster resilience. For complex systems with strong interactivity, comprehensiveness and multidimensionality, cause factor sets and result factor sets with different utilities can be established, which is more in line with human cognitive experience and provides a technical basis for model operation.

[0129] Example 2

[0130] See also Figure 2 This embodiment proposes a flood disaster resilience analysis system based on the intermediary effect, which is applied to the flood disaster resilience analysis method based on the intermediary effect proposed in Example 1, including:

[0131] A data collection module is used to collect corresponding influencing factor data according to the influencing factors of flood disaster resilience;

[0132] A threshold division module is used to divide the threshold of the indicator state according to the preset indicator state, divide the threshold of the indicator state of the influencing factor data, and obtain indicators of flood disaster resilience in different dimensions;

[0133] The indicator transfer relationship construction module is used to use the mediating effect to preprocess the data, conduct correlation analysis on the indicators of flood disaster resilience, and construct the transfer relationship of each indicator;

[0134] The multidimensional network construction module is used to construct the multidimensional network hierarchical structure of flood disaster resilience based on the transfer relationship of each indicator using the explanatory structural model;

[0135] The analysis module is used to analyze flood disaster resilience based on the multi-dimensional network hierarchy structure of flood disaster resilience.

[0136] In the specific implementation process, the data collection module collects the corresponding influencing factor data according to the influencing factors of flood disaster resilience. The influencing factors involved include stakeholders, resource characteristics, internal and external environment, and social economy involved in flood disaster resilience; the threshold division module divides the threshold of the influencing factor data according to the preset indicator state, and obtains indicators of flood disaster resilience in different dimensions. The indicator transfer relationship construction module uses the mediating effect to preprocess the data collected by the data collection module, analyzes the correlation of the indicators of flood disaster resilience, and uses the direct influence matrix and comprehensive influence matrix between indicators to construct the transfer relationship of each indicator. The multidimensional network construction module uses the explanatory structural model to construct the multidimensional network hierarchical structure of flood disaster resilience based on the transfer relationship of each indicator. The analysis module analyzes flood disaster resilience based on the multidimensional network hierarchical structure of flood disaster resilience.

[0137] The terms used in the drawings to describe positional relationships are only used for illustrative purposes and should not be construed as limiting this patent;

[0138] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A flood disaster resilience analysis method based on mediating effect, characterized in that: The following steps are involved: Step 1: According to the factors affecting flood disaster resilience, collect the corresponding influencing factor data; Step 2: According to the preset indicator state division threshold, the indicator state threshold is divided for the influencing factor data to obtain indicators of flood disaster resilience in different dimensions, including: Step 2.1: Establish a flood disaster resilience interactive system, divide the flood disaster resilience interactive system into several subsystems, and each subsystem contains the threshold of flood disaster resilience assessment indicators in the corresponding dimension; Step 2.2: Input the data of factors affecting flood disaster resilience into the flood disaster resilience interactive system. The flood disaster resilience interactive system divides the index state thresholds of the factors affecting flood disaster resilience based on the isometry method to obtain indicators of flood disaster resilience in different dimensions; Step:3: Use the mediating effect to preprocess the data, conduct correlation analysis on the indicators of flood disaster resilience, and construct the transmission relationship of each indicator, including: Step 3.1: Consider the indicators of flood disaster resilience as variables, establish the correlation between the variables based on the mediation effect theory, and obtain the influence relationship between different indicators on flood disaster resilience; Step 3.2: Based on the collected influencing factor data, numerical statistics are performed on the indicators of flood disaster resilience in different dimensions to determine the degree of direct impact between different indicators; Step 3.3: Using the direct impact degree between different indicators, construct the direct impact matrix X of the flood disaster resilience system. The formula is as follows: Among them, x ij Represents the i-th index S i For the jth index S j The degree of direct influence; if i = j, then x ij =0; Step 3.4: Normalize the direct impact matrix X of the flood disaster resilience system to obtain the normalized matrix G, whose formula is as follows: Among them, g ij Represents the i-th index S i For the jth index S j Normalized value of the direct impact degree, 0≤g ij ≤1 and Step 3.5: Calculate the comprehensive impact matrix T between indicators of each dimension of the flood disaster resilience system: T = [t ij n×n = G(I - G) -1 ​ Among them, y ji is the element of the comprehensive influence matrix T, I is the unit matrix; Step 4: Using the explanatory structural model, a multidimensional network hierarchical structure of flood disaster resilience is constructed based on the transmission relationship of each indicator to analyze flood disaster resilience.

2. The flood disaster resilience analysis method based on the mediating effect according to claim 1 is characterized in that: In Step 1, the influencing factors of flood disaster resilience include stakeholders involved in flood disaster resilience, resource characteristics, internal and external environment, and socio-economy.

3. The flood disaster resilience analysis method based on the mediating effect according to claim 1 is characterized in that: In Step 2.1, the subsystems include the environmental subsystem, social subsystem, economic subsystem, psychological subsystem, institutional subsystem and information communication subsystem.

4. The flood disaster resilience analysis method based on the mediating effect according to claim 3 is characterized in that: In Step 3.2, the methods for determining the degree of direct impact between different indicators include: According to the collected influencing factor data, the numerical statistics of the indicators of flood disaster resilience in different dimensions are carried out to determine the influencing values ​​of the indicators. If the indicator S i The value is proportional to the resilience of flood disasters, so the index S i Included in the positive impact index set z of flood disaster resilience; if the index S i The value is inversely proportional to the resilience of flood disasters, so the index S i Included in the negative impact indicator set b of flood disaster resilience; The range transformation method is used to standardize the index value and obtain the standard value of the positive impact index z i and negative impact index standard value b i , the formula is as follows: in, Indicator S i The maximum value of Indicator S i The minimum value of .

5. The flood disaster resilience analysis method based on mediating effect according to claim 3 is characterized in that: The Step 4 specifically includes the following steps: Step 4.1: Calculate the index S i The influence of i and the degree of influence i , and its calculation formula is as follows: Step 4.2: Calculate the index S i Centrality m i and the degree of cause n i , and its calculation formula is as follows: m i =f i +e i ,(i=1,…,n) n i =f i -e i ,(i=1,…,n); Step 4.3: Calculate the overall impact matrix of the flood disaster resilience system as H, and its calculation formula is as follows: Among them, h ij is the element of the overall influence matrix H; Step 4.4: Calculate the reachable matrix K of the flood disaster resilience system. The calculation formula is as follows: k ij ={1|h ij ≥λ},(i=1,…,n;j=1,…,n) k ij ={0|h ij <λ},(i=1,…,n;j=1,…,n) Among them, k ij is the element of the reachable matrix K, and the threshold λ is a configurable parameter; Step 4.5: Determine the antecedent set A i and reachable set R i , the formula is as follows: Among them, if R i ∩A i =R i , then it represents the reachable set R i Included in the antecedent set A i In the reachable set R i The indicator can be found in the antecedent set A i Found in; Step 4.6: Using the reachable set R i Build an index set for all elements in S i The corresponding rows and columns are deleted from the reachable matrix K, and the indicators are divided into different levels to obtain the multidimensional network hierarchical structure of flood disaster resilience, which is used to analyze flood disaster resilience.

6. A flood disaster resilience analysis system based on intermediary effect, applied to the flood disaster resilience analysis method based on intermediary effect as claimed in any one of claims 1 to 5, characterized in that: include: A data collection module is used to collect corresponding influencing factor data according to the influencing factors of flood disaster resilience; A threshold division module is used to divide the threshold of the indicator state according to the preset indicator state, divide the threshold of the indicator state of the influencing factor data, and obtain indicators of flood disaster resilience in different dimensions; The indicator transfer relationship construction module is used to use the mediating effect to preprocess the data, conduct correlation analysis on the indicators of flood disaster resilience, and construct the transfer relationship of each indicator; The multidimensional network construction module is used to construct the multidimensional network hierarchical structure of flood disaster resilience based on the transfer relationship of each indicator using the explanatory structural model; The analysis module is used to analyze flood disaster resilience based on the multi-dimensional network hierarchy structure of flood disaster resilience.

7. The flood disaster resilience analysis system based on intermediary effect according to claim 6 is characterized in that: In the indicator transfer relationship construction module, the direct impact matrix and the comprehensive impact matrix between indicators are used to construct the transfer relationship of each indicator.

Images