Urban key infrastructure toughness evaluation method under typhoon disaster and related device

Abstract

The invention belongs to the technical field of natural disaster risk and infrastructure toughness evaluation, and discloses an urban key infrastructure toughness evaluation method under typhoon disasters and a related device, and the method comprises the steps: obtaining typhoon disaster data, analyzing main risk factors, and obtaining a risk factor set and a cascade disaster set; analyzing risk factors in a risk factor set contained in the typhoon disaster needing to be analyzed, and obtaining a risk factor coupling type and frequency; acquiring the coupling degree of different risk factors by using the N-K model and the frequency of the risk factor coupling type, and further determining the risk factor which is most easily coupled with other risk factors; decoupling each cascade disaster in the cascade disaster set by using the interpretation structure model to obtain an evolution path of the cascade disaster and further obtain a key node; and determining the failure probability of the key nodes in the selected region by using the interpretation structure model, and evaluating the failure probability by using a sorting centroid method. According to the invention, key infrastructure failure early warning can be realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of natural disaster risk and infrastructure resilience assessment, and specifically designs a method and related device for assessing the resilience of urban critical infrastructure under typhoon disasters. Background Art

[0002] With the continuous change of natural climate, various natural disasters occur frequently, and the interrelationships, complexities, and unconventionalities of modern disasters have become increasingly prominent. The spatial and temporal scales and severity of the impacts of disasters are also affected by the high complexity of modern society and are difficult to predict. Usually, disaster events do not occur alone, but often trigger a series of chain reactions when they occur, resulting in greater losses. As the foundation of urban survival and development, the critical infrastructure system has a direct impact on national economic and social security, and is also vulnerable to various chain reactions and cause serious consequences.

[0003] The following problems exist in the current assessment methods: limitations in index selection, rationality of index weights, and limitations of assessment methods. Summary of the Invention

[0004] To solve the problems existing in the prior art, the purpose of the present invention is to provide a method and related device for assessing the resilience of urban critical infrastructure under typhoon disasters. The present invention uses the NK-ISM hybrid modeling method, and on this basis, uses the dependence idea to cooperate with the ranked centroid method (ROC method) to conduct scenario deduction on the complex cascade effects generated by it, so as to derive the probability of cascade failure of critical infrastructure in the region under the influence of strong precipitation carried by typhoons.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is as follows: A method for assessing the resilience of urban critical infrastructure under typhoon disasters, comprising: Obtain typhoon disaster data, analyze the main risk factors carried by the typhoon according to the typhoon disaster data, and obtain a risk factor set and a cascade disaster set according to the main risk factors; Analyze the risk factors in the risk factor set included in the typhoon disaster to be analyzed, obtain the risk factor coupling type, and determine the frequency of the risk factor coupling type; Use the N-K model and the frequency of the risk factor coupling type to obtain the coupling degree of different risk factors, sort the coupling degrees of different risk factors, and determine the risk factors that are most likely to be coupled with other risk factors; Use the interpretive structural model to decouple each cascade disaster in the cascade disaster set to obtain the evolution path of the cascade disaster, analyze the evolution path of the cascade disaster, and obtain the key nodes; Using the interpretive structural model, determine the failure probability of key nodes under the influence of risk factors that are most likely to couple with other risk factors in the selected area, and use the sorting centroid method to evaluate the failure probability, so as to realize the resilience assessment of urban critical infrastructure under typhoon disasters.

[0006] Preferably, analyze the main risk factors carried by the typhoon based on typhoon disaster data, and obtain the risk factor set and cascading disaster set according to the main risk factors, including: Further divide according to the characteristic indicators included in the main risk factors to form a risk factor set and a cascading disaster set that may be caused by the characteristic indicators.

[0007] Preferably, the main risk factors include strong winds, heavy rainfall, and storm surges. The cascading disaster set includes infrastructure damage, floods, landslides, debris flows, rising water levels, and urban waterlogging.

[0008] Preferably, determine the frequency of risk factor coupling types, including: Calculate the frequency of risk factor coupling types containing different risk factor types according to the risk factors and the states of the risk factors in different risk factor coupling types.

[0009] Preferably, the risk factor that is most likely to couple with other risk factors is heavy precipitation.

[0010] Preferably, the key nodes are infrastructure failures. The infrastructure includes power supply facilities, water supply facilities, transportation facilities, communication facilities, and building facilities. The target facilities of the critical infrastructure include power stations, water supply companies, roads, telecommunications agencies, and residential areas.

[0011] Preferably, using the interpretive structural model, determine the failure probability of key nodes under the influence of risk factors that are most likely to couple with other risk factors in the selected area, and use the sorting centroid method to evaluate the failure probability, including: Select the relatively important types of critical infrastructure among the infrastructure types, select the target facilities of these critical infrastructures, and judge the geographical dependence and functional dependence within the affected area; Using the interpretive structural model, regard both geographical dependence and functional dependence as having an influence on each other, transform geographical dependence and functional dependence into an adjacency matrix in the interpretive structural model, calculate the reachability matrix of the adjacency matrix, and obtain the scenario elements and preliminary coupling influence relationships of the critical infrastructure cascading failure scenario map according to the reachability matrix; After obtaining the complete scenario elements, mutual coupling relationships, and importance ranking results of the entire cascading disaster scenario deduction system, the probability of cascading failures of critical infrastructure caused by cascading disasters is evaluated using the ranking centroid method.

[0012] The present invention also provides a resilience assessment system for urban critical infrastructure under typhoon disasters, which is used to implement the above-mentioned method for assessing the resilience of urban critical infrastructure under typhoon disasters. The system includes: The first analysis unit: It is used to obtain typhoon disaster data, analyze the main risk factors carried by the typhoon according to the typhoon disaster data, and obtain a risk factor set and a cascading disaster set based on the main risk factors; The second analysis unit: It is used to analyze the risk factors in the risk factor set included in the typhoon disaster to be analyzed, obtain the risk factor coupling types, and determine the frequencies of the risk factor coupling types; The third analysis unit: It is used to obtain the coupling degrees of different risk factors by using the N-K model and the frequencies of the risk factor coupling types, rank the coupling degrees of different risk factors, and determine the risk factors that are most likely to be coupled with other risk factors; The fourth analysis unit: It is used to decouple each cascading disaster in the cascading disaster set by using the interpretive structural model, obtain the evolution path of the cascading disaster, and analyze the evolution path of the cascading disaster to obtain key nodes; The assessment unit: It is used to use the interpretive structural model to determine the failure probability of the key nodes under the influence of the risk factors that are most likely to be coupled with other risk factors in the selected area, and use the ranking centroid method to evaluate the failure probability, so as to realize the assessment of the resilience of urban critical infrastructure under typhoon disasters.

[0013] The present invention also provides an electronic device, including: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method for assessing the resilience of urban critical infrastructure under typhoon disasters as described above in the present invention.

[0014] The present invention also provides a storage medium on which a computer program is stored, wherein the computer program, when executed by a processor, implements the method for assessing the resilience of urban critical infrastructure under typhoon disasters as described above in the present invention.

[0015] The present invention has the following beneficial effects: The present invention uses the NK-ISM hybrid modeling method to explore the risk coupling mechanism and evolution path of cascading disasters. On this basis, the scenario deduction of cascading failures of critical infrastructure is carried out by using dependence thinking and the ranking centroid method, so as to realize the early warning of critical infrastructure failures. Description of the Drawings

[0016] Figure 1 is the flowchart of the method for evaluating the resilience of urban critical infrastructure under typhoon disasters provided by the present invention; Figure 2 is the flowchart of the N-K model analysis in the embodiment of the present invention: Figure 3 is the flowchart of the ISM model analysis in the embodiment of the present invention: Figure 4 is the hierarchical structure diagram of the typhoon disaster system in the embodiment of the present invention: Figure 5 is the marked map of critical infrastructure in the research area of Yantou Town in the embodiment of the present invention: Figure 6 is the coupling relationship diagram of critical infrastructure in the embodiment of the present invention. Detailed Embodiment

[0017] Next, the technical solutions in the embodiments of the present invention will be completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0018] In view of the deficiencies of the prior art, the present invention provides a method and related device for evaluating the resilience of urban critical infrastructure under typhoon disasters. The present invention uses the NK-ISM hybrid modeling method to quantify and express the risk coupling effect of 78 typhoons affecting a certain province from 2003 to 2022 and the evolution mechanism of the cascading disasters caused by them. And a probability derivation model with the ranking centroid method (ROC method) as the core is introduced to obtain the probability of cascading failures of critical infrastructure in this area under the influence of strong precipitation carried by typhoons, so as to realize the resilience evaluation of infrastructure, and further avoid the failure of urban infrastructure. The NK-ISM hybrid modeling method can comprehensively consider various influencing factors, including physical factors (such as wind speed, precipitation), the vulnerability of infrastructure, and geographical conditions, thus breaking through the limitations of single-index evaluation, and determining the weights of each index through data analysis, avoiding the subjectivity and uncertainty of artificially setting weights. The ROC method introduces the dependence idea and considers the correlation and mutual influence between each index, thus improving the accuracy and reliability of the evaluation results.

[0019] Specifically, refer to Figure 1 , the method for evaluating the resilience of critical urban infrastructure under typhoon disasters of the present invention includes the following steps: Step 1: Obtain typhoon disaster data, analyze the main risk factors carried by the typhoon, summarize the main consequences caused by the typhoon disaster according to the main risk factors, and establish a risk factor set and a cascading disaster set; specifically, in this step, by consulting national standard documents, the "China Meteorological Disaster Yearbook" issued by the National Meteorological Center, relevant documents issued by the Ministry of Emergency Management of the People's Republic of China, and news media reports, it is known that although the risk factor indicators of cascading disasters are multi-dimensional and multi-source, however, any type of cascading disaster has the same essential characteristics, that is, the risk factor is an intuitive representation indicator of the impact consequences of cascading disasters. In terms of selecting indicators in the present invention, three disaster-causing factor indicators (strong wind, heavy rainfall, storm surge) of cascading disasters that can produce a strong cascading effect are listed. Since the various values of the typhoon are in a constantly changing state during the attack process, further classification is carried out according to the characteristic indicators included in the three main disaster-causing factors to form a risk factor set, and some cascading disasters that may be caused by these indicators, such as: infrastructure damage, flood, landslide, debris flow, rising water level, urban waterlogging; Step 2: Select the typhoon disaster to be analyzed, analyze the risk factors in the risk factor set included in the typhoon disaster, summarize the risk factor coupling types, and determine the frequency of the risk factor coupling types; specifically, in this step, the above-mentioned risk factors included in each cascading disaster are classified and summarized. Assume that the cascading disasters of the studied category only involve six risk factors, namely R1, R2, R3, R4, R5, and R6. Then the minimum risk factor coupling type of each cascading disaster of this type is single-factor coupling (for example, R1-R1, R2-R2), and the maximum is six-factor coupling (for example, R1-R2-R3-R4-R5-R6). Each indicator has two states (that is, 0 represents non-occurrence, and 1 represents occurrence), so there are at most 26 risk factor coupling types in the entire system.

[0020] After obtaining the risk factor coupling types existing in the entire system, according to the different risk factors and their states existing in different risk factor coupling types, the frequency of the risk factor coupling types containing different risk factor types is calculated using the formula.

[0021] (1) When there is a single risk factor indicator in the risk coupling type (taking R1 as an example): (1) (2) When there are two risk factor indicators in the risk coupling type (taking R1 and R4 as an example): (2) (3) When there are three risk factor indicators in this risk coupling type (taking R4, R5, and R6 as examples): (3) (4) When there are four risk factor indicators in this risk coupling type (taking R2, R3, R4, and R5 as examples): (4) (5) When there are five risk factor indicators in this risk coupling type (taking R1, R2, R3, R4, and R5 as examples): (5) (6) When there are six risk factor indicators in this risk coupling type (taking R1, R2, R3, R4, R5, and R6 as examples): (6) Among them, 1, 2, 3, 4, 5, and 6 respectively correspond to represent risk factors R1, R2, R3, R4, R5, and R6; P(1, 2, 3...) represents the frequency of occurrence of the coupling type containing the corresponding risk factors; P a,b,c,d,e,f represents whether risk factors R1, R2, R3, R4, R5, and R6 occur; represents that the risk factor status at this position is uncertain, that is, it may occur or may not occur; S represents the total number of existing risk factor coupling types in this system; Step 3: Use the N-K model and the frequency of the risk factor coupling type to obtain the coupling degree of different risk factors, and sort the coupling degrees of different risk factors to determine that the risk factor most likely to be coupled with other risk factors is heavy precipitation; specifically, in this step, after determining the corresponding frequencies of different risk factor coupling types involved in the cascading disasters in Step 2, use the N-K model to quantify the degree of different risk factor coupling types. It should be noted that single-factor coupling is the exchange of energy and information within the system, and the coupling degree cannot be calculated by a formula.

[0022] (1) Taking two-factor coupling (R1 - R4) as an example: (7) (2) Taking three-factor coupling (R4 - R5 - R6) as an example: (8) (3) Taking four-factor coupling (R2 - R3 - R4 - R5) as an example: (9) (4) Taking five-factor coupling (R1 - R2 - R3 - R4 - R5) as an example: (10) (5) Taking the six-factor coupling (R1 - R2 - R3 - R4 - R5 - R6) as an example: (11) Among them, T m represents the coupling degree of risk factors, where m = 1, 2, 3, 4, 5, 6; After obtaining the coupling degrees of the coupling types of each risk factor, sort them, and determine the risk factor that is most likely to be coupled with other risk factors through analysis.

[0023] Step 4: Use the Interpretive Structural Modeling (ISM model) to decouple each cascading disaster in the cascading disaster set to obtain the evolution path of the cascading disaster, and analyze that the key node is infrastructure failure; specifically, in this step, use the Interpretive Structural Modeling (ISM model) to decouple the risk factors in the complex system of cascading disasters, and hierarchize the evolution process to achieve a structured expression of the evolution path of the cascading disaster and the coupling mechanism of risk factors. The detailed steps of using the ISM model are as follows: (1) When using the ISM model, first establish a table of element relationships, and then establish an adjacency matrix based on the table of element relationships. When establishing the adjacency matrix A, the setting of the elements in the matrix should follow the following formula (12), where a ij represents the element in the i-th row and j-th column: (12) (2) After obtaining the adjacency matrix A, calculate the reachability matrix M of the adjacency matrix A according to formula (13): (13) Since the elements in the adjacency matrix are only 1 and 0, it is a Boolean matrix. Therefore, when calculating, it is necessary to follow the Boolean matrix logic sum and logical multiplication: 0 + 0 = 0 0 + 1 = 1 1 + 0 = 1 1 + 1 = 1 0 × 0 = 0 0 × 1 = 0 1 × 0 = 0 1 × 1 = 1 (3) After obtaining the reachability matrix M, it needs to be hierarchically decomposed. Before decomposing the reachability matrix, it is necessary to analyze the reachable set and the antecedent set of the reachability matrix: ① Reachable set R(S i ): In the row corresponding to the element S i in the reachability matrix, the set of column elements corresponding to the matrix elements containing 1.

[0024] ② Antecedent set Q(S i ): In the reachability matrix, the set of elements S iIn the corresponding column, the set of row elements corresponding to the matrix elements containing 1.

[0025] ③ Intersection A = R(S i ) ∩ Q(S i ).

[0026] After obtaining the reachable set and the antecedent set, list them in a table to facilitate further decomposition of the reachable matrix at a higher level, thereby obtaining a hierarchical diagram.

[0027] The purpose of hierarchical division is to better understand the hierarchical structure in the system. The highest level represents the ultimate goal of the system, while the lower levels represent the reasons for the composition of the higher-level.

[0028] After obtaining the reachable set and the antecedent set, they can be listed in a table to further decompose the reachable matrix at a higher level to obtain a hierarchical structure diagram.

[0029] The purpose of hierarchical decomposition is to more clearly understand the hierarchical relationship between the elements in the system. The top layer represents the ultimate goal of the system, and each layer below represents the reason for the upper layer. The method of hierarchical decomposition is based on the condition of R(S i ) ∩ Q(S i ) = R(S i ) to extract the hierarchy.

[0030] By constructing the hierarchical structure of the entire typhoon disaster system, the expression of the evolution mechanism of the typhoon disaster system can be realized, and the hierarchical structure diagram of the typhoon disaster system can be obtained. As Figure 4 shown, it can be analyzed from it that the damage to infrastructure is the core factor of the entire disaster system.

[0031] Step 5: Use the Interpretive Structural Modeling (ISM) model to determine the failure probability of key infrastructure under the influence of the risk factor (heavy precipitation) that is most likely to couple with other risk factors in the selected area, and use the sorting centroid method to evaluate its failure probability. Specifically, in this step, select relatively important key infrastructure types in the infrastructure type, such as: power supply facilities, water supply facilities, transportation facilities, communication facilities, building facilities, and select the target facilities of these key infrastructures (such as: power stations, water supply companies, roads, telecommunications agencies, residential areas), judge the geographical dependence (G) and functional dependence (F) in the affected area, and construct a dependence judgment table.

[0032] Using the ISM model thinking, both geographical dependence (G) and functional dependence (F) are regarded as having an impact on each other, which is transformed into an adjacency matrix in the ISM model. Then, the reachability matrix of this matrix is calculated using formula (13) and decomposed to obtain the scenario elements and preliminary coupling impact relationships of the critical infrastructure cascading failure scenario map.

[0033] In the scenario map, the importance of critical infrastructure is represented by the total number of input and output impacts (when the total number of input and output impacts is the same, the importance is ranked according to the maximum input impact (higher vulnerability)). At the same time, it also reflects various scenarios and coupling relationships of critical infrastructure failure under cascading disasters, providing an intuitive expression of different scenarios of critical infrastructure cascading failure.

[0034] After obtaining the complete scenario elements, mutual coupling relationships, and importance ranking results of the entire cascading disaster scenario deduction system, the centroid method of ranking is used to deduce the probability of critical infrastructure cascading failure caused by cascading disasters.

[0035] The centroid method of ranking is mainly used to derive the probabilities of four different types of events.

[0036] Ⅰ: Mutually exclusive binary events They will be ranked according to the likelihood of the above events occurring, and the ranking information will be converted into probability values. Among them, the ranking information can be described as P1≥P2≥P3≥...≥Pn and , and converting the information into probability expression requires using the centroid method of ranking to make an equivalent calculation for each probability. See formula (14): (14) Ⅱ: Probability density function of a variable In this type of event, experts need to determine the range of the event being analyzed and divide the entire range interval into a set of time intervals. Compared with Ⅰ, its analysis algorithm has no difference, except that the ranking of the likelihood of events is changed to the ranking of time intervals.

[0037] Ⅲ: Random independent binary events In this type of event, when deriving the probability of the event, the initial analysis algorithm needs to be modified to a certain extent. Therefore, it is necessary to assign order statistics to each sample in the statistical sample to achieve the ranking of events, and make the k-th order statistic of the sample be the k-th minimum value of the sample. When n values in the observed unit interval are independent and uniformly distributed, the i-th (i≤n) ordered element is the (n + 1 - i)-th order statistic of the uniform distribution. At this time, its expected value is , so the probability inference of each event can be seen in formula (15): (15) Ⅳ: Low - probability binary events In this type of event, whether it is a mutually exclusive binary event or a randomly independent binary event, the analysis algorithms in I and III need to be modified. First, an expert is required to numerically evaluate the probability of not having any specified event, which is also called P0 (this value is generally taken as 10% or 30%). At the same time, when the event type is I, the Φ - measure has the same relative magnitude as the probability derived from formula (14). The calculation of the Φ - measure can be seen in formula (16): (16) When the event type is III, the calculation of the Φ - measure can be seen in formula (17): (17) The Φ - measure calculated by formulas (16) and (17) does not represent probability, but represents a ratio - scale measure of event similarity. And formulas (16) and (17) can be regarded as the Φ1 - measure always being equal to 1. Therefore, the two can be mutually mapped and expressed through positive multiple variations, as shown in formula (18): (18) b ij represents the likelihood ratio between event i and event j.

[0038] When j = 1, it can be obtained that:

[0039] At this time, formula (19) can be obtained: (19) However, in order to derive the probability of a specific event, the Φ - measure needs to be normalized. When the event type is I, since P1 + P2+P3 +...+P n = 1 - P0, at this time, the normalization equation of Ludke et al. is adjusted, that is, formula (20): (20) When the event type is III, since (1 - P1)·(1 - P2)·(1 - P3)·...·(1 - P n ) = P0, at this time, formula (21) can be deduced: (21) Embodiment The method for evaluating the resilience of urban critical infrastructure under typhoon disasters in this embodiment includes the following steps: Step 1: Summarize the indicators included in each of the 78 typhoon disasters in a certain province during the period from 2003 to 2022, and list three disaster-causing factor indicators of cascading disasters that can produce strong cascading effects: strong wind (F1), heavy rainfall (F2), and storm surge (F3). Some sets of cascading disasters that these indicators may cause, such as: infrastructure damage (S1), flood (S2), landslide (S3), debris flow (S4), rising water level (S5), urban waterlogging (S6), and the resulting final disasters: environmental damage, casualties or property losses (C).

[0040] Since the values of each item are in a constantly changing state during the attack of the typhoon, according to the characteristic indicators included in the three main disaster-causing factors of strong wind (F1), heavy rainfall (F2), and storm surge (F3), the risk factors are further divided to obtain the risk factor set (where the risk factors of the storm surge are mainly characterized by whether economic losses are actually caused), as shown in Table 1: Table 1

[0041] Step 2: According to the risk factor indicators established in Table 1, taking whether they appear as the criterion, summarize the risk factor indicators included in each of the 78 typhoon disasters in a certain province during the period from 2003 to 2022. There are four coupling modes in total: single-factor coupling, two-factor coupling, multi-factor coupling, and five-factor coupling: The single-factor coupling mode refers to the internal risk interaction of a single factor that affects the cascading effect of typhoon disasters, manifested as the coupling effect between the same factors, mainly including two coupling types: R1-R1 and R4-R4; The two-factor coupling mode refers to the interaction between two different factors that affect the cascading effect of typhoon disasters, mainly including two coupling types: R1-R4 and R4-R5; The multi-factor coupling mode (with three or more risk factors) refers to the interaction between multiple different factors that affect the cascading effect of typhoon disasters, mainly including sixteen coupling types: R1-R2-R5, R1-R4-R5, R2-R4-R5, R4-R5-R6, R1-R2-R4-R5, R2-R3-R4-R5, R1-R2-R3-R4, R1-R2-R5-R6, R3-R4-R5-R6, R2-R3-R5-R6, R2-R4-R5-R6, R1-R4-R5-R6, R1-R2-R3-R4-R5, R1-R2-R4-R5-R6, R1-R2-R3-R5-R6, R1-R2-R3-R4-R5-R6. The risk coupling frequencies can be calculated according to Formulas (1)-(6), as shown in Table 2.

[0042] Table 2

[0043] Step 3: After determining the coupling types and corresponding frequencies of risk factors in Step 2, the quantification process for different risk factor coupling types can be started. It should be noted that single-factor coupling is the exchange of energy and information within the system, and the coupling degree cannot be calculated by formula. The coupling degrees of different risk factor coupling types are calculated according to Formulas (7)-(11), as shown in Table 3.

[0044] Table 3

[0045] It can be seen from Table 3 that the increase in the number of risk factors involved in the coupling will lead to an upward trend in the corresponding risk coupling degree. The increase in risk coupling will strengthen the cascading effect caused by typhoons and make the evolution path of cascading disasters more complex. Among different risk coupling types, the six-factor risk coupling degree is the highest, and the two-factor risk coupling degree is the lowest, indicating that the losses caused by typhoon disasters are the result of the combined action of multiple factors. Among them, R5 (generating heavy rain or above) is the risk factor that is most likely to be coupled with other risk factors and is also the main factor leading to the occurrence of various cascading disasters.

[0046] Step 4: Use the Interpretative Structural Method (ISM model for short) to decouple the risk factors in cascading disasters and hierarchize the evolution process to achieve the expression of the evolution path of cascading disasters.

[0047] Following Formula (12), based on the risk factor indicators (i.e., R1, R2, R3, R4, R5, R6) and cascading disaster types (i.e., S1, S2, S3, S4, S5, S6, C), an adjacency matrix for the evolution mode of typhoons and their generated cascading disasters is established, as shown in Table 4: Table 4

[0048] Calculate the adjacency matrix to construct the hierarchical structure of the entire typhoon disaster system and achieve the expression of the evolution mechanism of the typhoon disaster system. It can be seen Figure 4 , and through analysis, it can be obtained that the damage to infrastructure is the core factor of the entire disaster system.

[0049] Step 5: Select five key infrastructure types that are relatively important among the infrastructure types, namely power supply facilities, water supply facilities, transportation facilities, communication facilities, and building facilities. Select the target facilities for these five major types of key infrastructure, judge the geographical dependence (G) and functional dependence (F) within the affected area, and construct a dependence judgment table. Search for Yantan Town, Yongjia County, Wenzhou City through satellite cloud maps to select the research area, divide the key infrastructure types within this area, and mark the satellite map, as shown in Table 5 and Figure 5 。

[0050] Table 5

[0051] From Figure 5 a brief analysis, the following conclusions can be drawn: (1) There is only one power supply facility in this area, so this facility needs to supply electricity to all other facilities (including water supply facilities, communication facilities, residential areas, and public roads), that is, the power supply facility has functional dependence on other types of facilities. Similarly, the communication facility also needs to provide signals and networks for other types of facilities, that is, the communication facility also has functional dependence on other types of facilities. Due to the particularity of the water supply facility in providing substances (water resources), the water supply facility only provides water resources for residential areas and provides the cooling water required for the normal operation of power supply facilities and communication facilities.

[0052] (2) As a basic link in the supply process of power supply facilities, water supply facilities, and communication facilities, residential areas, due to the geographical correlation between adjacent residential areas, when a certain residential area is affected, it will affect the living environment and state of its adjacent residential areas. Similarly, as a necessary infrastructure for transportation and travel, when a certain residential area is affected, it will inevitably be affected due to the evacuation of personnel and the transportation of materials. Therefore, residential areas have functional dependence on adjacent transportation facilities.

[0053] (3) Since the locations of power supply facilities, communication facilities, and water supply facilities are connected to some roads, and roads are the only way to reach these key infrastructures, they play a necessary role in the material transportation and personnel access within these key infrastructures. Therefore, these roads have both functional dependence and geographical dependence on the adjacent key infrastructures mentioned above; and some residential areas are adjacent to the above key infrastructures. When the residential areas are affected and damaged, it will have an impact on them. This impact is mainly manifested as the indirect damage of the main facilities inside due to the damage of the residential areas, or the loss of internal staff or operational errors caused by the public pressure and panic psychology in the surrounding areas.

[0054] Therefore, based on the various possible situations analyzed in the above conclusions, the causal relationships of functional dependence and geographical dependence among various key infrastructures in the study area are identified, and Tables 6.1 and 6.2 can be obtained (Note: In Table 6.1, due to a large amount of data, starting from the 6th column in the second row, the data in each column are R1, R2, R3, R4, R5, R6, R7, R8, R9, R10, R11, R12, R13, R14, R15, R16, R17, R18, R19, R21, R22, R23, R24, R25 in sequence; in each row starting from the 6th row, the data in the second column are R1, R2, R3, R4, R5, R6, R7, R8, R9, R10, R11, R12, R13, R14, R15, R16, R17, R18, R19, R21, R22, R23, R24, R25, R1, R2, R3, R4, R5, R6, R7, R8, R9, R10, R11, R12, R13 in sequence): Table 6.1

[0055] Table 6.2

[0056] And by applying the ISM model thinking, both geographical dependence (G) and functional dependence (F) are regarded as having an impact on each other, and they are transformed into the adjacency matrix in the ISM model. Then, by applying formula (13) to calculate the reachability matrix of this matrix and decompose it, the scenario elements and preliminary coupling influence relationships of the cascading failure scenario map of key infrastructures can be obtained.

[0057] In the process of applying the interpretive structural model, first, the relationships between the things to be studied need to be input in the form of an adjacency matrix, and the elements in the matrix can express the relationships between the research things in the form of 0 or 1. It can be seen that the results analyzed in Table 6 can be used as the prototype of the adjacency matrix, and the dependence of any key infrastructure on other key infrastructures is the influence between the elements. Therefore, by transforming Table 6 into an adjacency matrix as the input of the interpretive structural model and applying formula (12) and formula (13), the coupling relationships between key infrastructures in the study area can be obtained. It can be seen Figure 6 .

[0058] From Figure 6 it can be seen that there are different input and output influences among different key infrastructures in the study area. When conducting the importance ranking, the following principles should be based on: (1)The total number of input and output impacts of any critical infrastructure represents its importance level. The larger the total number of impacts, the higher its importance level. This is because the magnitude of the total number of impacts intuitively indicates the amount of tasks undertaken by the critical infrastructure in the entire critical infrastructure system. A larger total number of impacts means that it plays a greater role and is subject to more influences during the entire operation process.

[0059] (2)When the total number of input impacts and output impacts of any critical infrastructure is the same, consider the magnitude of its total number of input impacts. This is because under the same circumstances, the more input impacts there are, the more influences the critical infrastructure is subject to during the operation of the entire critical infrastructure system. This also simultaneously makes it more vulnerable to the cascading failures of other facilities, that is, it has a higher vulnerability. Therefore, when the total number of impacts of any critical infrastructure is the same, the more the total number of input impacts, the higher its importance level.

[0060] (3)The results of analyzing the total number of input and output impacts of critical infrastructure in the study area and ranking their importance levels can be seen in Table 7 (the left number in the total number of impacts represents the number of input impacts, and the right number represents the number of output impacts) and Table 8.

[0061] Table 7

[0062] Table 8

[0063] Due to the development of modern technology, various types of critical infrastructure already have relatively high reliability. However, under the influence of high-intensity natural disasters, there is still a possibility of failure scenarios, which belong to low-probability events. At the same time, under the influence of the same external natural disasters (i.e., heavy rainfall carried by typhoons), the critical infrastructure in the study area exists independently, and the probability of their cascading failures does not affect each other. Therefore, using Formula (17), Formula (18), Formula (19), and Formula (21), the probability of cascading failures of critical infrastructure in the study area under the influence of heavy rainfall is evaluated, and the results can be seen in Table 9.

[0064] Table 9

[0065] As can be seen from the above solution, the present invention uses the NK-ISM hybrid modeling method to explore the risk coupling mechanism and evolution path of cascading disasters. On this basis, using the dependence thinking and the sorting centroid method, the probability of critical infrastructure failure is evaluated, so as to realize the early warning of critical infrastructure failure.

[0066] In addition, an embodiment of the present invention further provides a system for implementing the above-mentioned method for evaluating the resilience of urban critical infrastructure under typhoon disasters according to the present invention. The system includes: The first analysis unit: used to obtain typhoon disaster data, analyze the main risk factors carried by the typhoon according to the typhoon disaster data, and obtain a risk factor set and a cascading disaster set according to the main risk factors; The second analysis unit: used to analyze the risk factors in the risk factor set included in the typhoon disaster to be analyzed, obtain the risk factor coupling type, and determine the frequency of the risk factor coupling type; The third analysis unit: used to obtain the coupling degree of different risk factors by using the N-K model and the frequency of the risk factor coupling type, sort the coupling degrees of different risk factors, and determine the risk factors that are most likely to be coupled with other risk factors; The fourth analysis unit: used to decouple each cascading disaster in the cascading disaster set by using the interpretive structural model to obtain the evolution path of the cascading disaster, and analyze the evolution path of the cascading disaster to obtain key nodes; The evaluation unit: used to determine the failure probability of the key nodes under the influence of the risk factors that are most likely to be coupled with other risk factors in the selected area by using the interpretive structural model, and evaluate the failure probability by using the sorting centroid method, so as to realize the evaluation of the resilience of urban critical infrastructure under typhoon disasters.

[0067] An embodiment of the present invention further provides a corresponding electronic device and a computer-readable storage medium for implementing the solution provided by the embodiment of the present invention.

[0068] Wherein, the device includes a memory and a processor. The memory is used to store instructions or codes, and the processor is used to execute the instructions or codes so that the device executes the method for evaluating the resilience of urban critical infrastructure under typhoon disasters according to any embodiment of the present application.

[0069] A computer program is stored on the storage medium. When the computer program is executed by the processor, the method for evaluating the resilience of urban critical infrastructure under typhoon disasters according to any embodiment of the present application is implemented.

[0070] Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: it is still possible to modify the specific implementation manners of the present invention or make equivalent substitutions, and any modification or equivalent substitution that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A method for assessing the resilience of key urban infrastructure under typhoon disasters, characterized in that: include: Obtain typhoon disaster data, analyze the main risk factors carried by typhoons based on the typhoon disaster data, and obtain risk factor sets and cascading disaster sets based on the main risk factors; Analyze the risk factors in the risk factor set included in the typhoon disaster to be analyzed, obtain the risk factor coupling type, and determine the frequency of the risk factor coupling type; The NK model and the frequency of risk factor coupling types were used to obtain the coupling degrees of different risk factors, and the coupling degrees of different risk factors were ranked to determine the risk factors that were most likely to couple with other risk factors. Using the explanatory structural model, we decouple the cascading disasters in the cascading disaster set, obtain the evolution path of the cascading disasters, analyze the evolution path of the cascading disasters, and obtain the key nodes; The explanatory structural model is used to determine the failure probability of key nodes in the selected area under the influence of risk factors that are most easily coupled with other risk factors, and the sorting centroid method is used to evaluate the failure probability, thereby realizing the resilience assessment of key urban infrastructure under typhoon disasters.

2. The method for evaluating the resilience of key urban infrastructure under typhoon disasters according to claim 1 is characterized in that: According to the typhoon disaster data, the main risk factors brought by the typhoon are analyzed, and the risk factor set and cascade disaster set are obtained according to the main risk factors, including: Further classification is carried out according to the characteristic indicators contained in the main risk factors to form a set of risk factors and a set of cascading disasters that may be caused by the characteristic indicators.

3. The method for evaluating the resilience of key urban infrastructure under typhoon disasters according to claim 2 is characterized in that: The main risk factors mentioned include high winds, heavy rainfall and storm surges. The collection of cascading disasters includes infrastructure damage, floods, landslides, mudslides, rising tides, and urban waterlogging.

4. The method for evaluating the resilience of key urban infrastructure under typhoon disasters according to claim 1 is characterized in that: Determine the frequency of risk factor coupling types, including: According to the different risk factors existing in different risk factor coupling types and the different states of the risk factors, the frequencies of risk factor coupling types containing different risk factor types are calculated.

5. The method for evaluating the resilience of key urban infrastructure under typhoon disasters according to claim 1 is characterized in that: The risk factor that is most likely to couple with other risk factors is heavy precipitation.

6. The method for evaluating the resilience of key urban infrastructure under typhoon disasters according to claim 1 is characterized in that: The critical nodes are infrastructure failures, and the infrastructure includes power supply facilities, water supply facilities, transportation facilities, communication facilities and building facilities. The target facilities of the critical infrastructure include power supply stations, water supply companies, roads, telecommunication agencies and residential areas.

7. The method for evaluating the resilience of key urban infrastructure under typhoon disasters according to claim 6 is characterized in that: Using the explanatory structural model, determine the failure probability of key nodes in the selected area under the influence of the risk factors that are most likely to couple with other risk factors, and use the sorted centroid method to evaluate the failure probability, including: Select the more important critical infrastructure types among the infrastructure types, select the target facilities of these critical infrastructures, and judge the geographical dependence and functional dependence in the affected areas; Using the explanatory structural model, both geographical dependence and functional dependence are considered to have an impact on each other, and the geographical dependence and functional dependence are transformed into the adjacency matrix in the explanatory structural model, and the reachability matrix of the adjacency matrix is ​​calculated. According to the reachability matrix, the scenario elements and preliminary coupling impact relationships of the critical infrastructure cascading failure scenario map are obtained; After obtaining the complete scenario elements, mutual coupling relationships and importance ranking results of the entire cascading disaster scenario simulation system, the sorting centroid method is used to evaluate the probability of cascading failure of critical infrastructure caused by cascading disasters.

8. A system for assessing the resilience of key urban infrastructure under typhoon disasters, characterized in that: include: The first analysis unit is used to obtain typhoon disaster data, analyze the main risk factors carried by the typhoon based on the typhoon disaster data, and obtain the risk factor set and cascade disaster set based on the main risk factors; The second analysis unit is used to analyze the risk factors in the risk factor set included in the typhoon disaster to be analyzed, obtain the risk factor coupling type, and determine the frequency of the risk factor coupling type; The third analysis unit is used to obtain the coupling degree of different risk factors using the NK model and the frequency of risk factor coupling types, rank the coupling degrees of different risk factors, and determine the risk factors that are most likely to couple with other risk factors; The fourth analysis unit is used to use the explanatory structural model to decouple the cascading disasters in the cascading disaster set, obtain the evolution path of the cascading disasters, analyze the evolution path of the cascading disasters, and obtain the key nodes; Assessment unit: It is used to use the explanatory structural model to determine the failure probability of key nodes in the selected area under the influence of risk factors that are most easily coupled with other risk factors, and to evaluate the failure probability using the sorting centroid method, so as to achieve the resilience assessment of key urban infrastructure under typhoon disasters.

9. An electronic device, characterized in that: include: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method for assessing the resilience of key urban infrastructure under typhoon disasters as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: A computer program is stored thereon, wherein when the computer program is executed by a processor, the method for assessing the resilience of key urban infrastructure under typhoon disasters as described in any one of claims 1 to 7 is implemented.

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