Mountain torrent disaster risk loss quantitative evaluation method and device considering cascade effect

Abstract

The invention discloses a mountain torrent disaster risk loss quantitative evaluation method and device considering a cascade effect, and relates to the technical field of disaster prevention and mitigation, and the method comprises the steps: constructing a hydrodynamic model comprising a reference and a plurality of coupling working conditions to simulate flood routing, outputting a simulation result, and calculating a risk index under each working condition; the cascade risk amplification coefficient is accurately quantified by comparing the index values of the reference and the coupling working condition; based on the cascade risk amplification coefficient, establishing a grading standard and identifying a key risk interval; and finally, the loss of the key risk interval is accurately calculated by embedding the amplification coefficient into a loss evaluation model, so that the whole process of the cascade effect from a physical mechanism to economic loss is accurately quantified and scientifically evaluated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of disaster prevention and mitigation, in particular to a mountain torrent disaster risk loss quantitative evaluation method and device considering cascade effect. BACKGROUND

[0002] As a common natural disaster, mountain torrent disaster poses a serious threat to people's life and property safety and regional economic development. During the occurrence of mountain torrent disaster, there is a significant cascade effect. The traditional mountain torrent disaster risk evaluation method mainly analyzes based on a single disaster factor, and does not fully consider the cascade amplification effect caused by the coupling of multiple factors, which cannot accurately evaluate the mountain torrent disaster risk loss and is difficult to meet the precise disaster prevention and mitigation needs.

[0003] Therefore, it is urgent to establish a mountain torrent disaster risk loss quantitative evaluation method which can accurately quantify the cascade effect and scientifically evaluate the risk loss. SUMMARY

[0004] The purpose of the present application is to provide a mountain torrent disaster risk loss quantitative evaluation method and device considering cascade effect, to realize accurate quantification of cascade effect and scientific evaluation of risk loss, and to provide technical support for disaster prevention and mitigation.

[0005] To achieve the above purpose, the present application provides the following solutions. In a first aspect, the present application provides a mountain torrent disaster risk loss quantitative evaluation method considering cascade effect, comprising: obtaining basic data of a research area; building a hydrodynamic model based on the basic data, setting multiple scenario calculation working conditions, simulating flood evolution process under different working conditions, and outputting simulation results, wherein the working conditions include a reference working condition and at least one coupled working condition, and the coupled working condition considers at least one cascade risk factor; calculating risk index values under each working condition based on the simulation results, and calculating cascade risk amplification coefficients of each risk index based on the risk index values of the reference working condition and the coupled working condition; based on the cascade risk amplification coefficients, establishing a risk amplification effect grading standard, and identifying a key risk amplification interval; building a loss quantitative evaluation model considering cascade effect; for the identified key risk amplification interval, outputting a risk loss evaluation result according to the loss quantitative evaluation model.

[0006] Optionally, the basic data includes terrain data, land use data and disaster-affected distribution data.

[0007] Optionally, the coupled working condition includes a multi-branch confluence working condition, a bridge water-blocking working condition and a composite working condition.

[0008] Optionally, the cascade risk factors include a multi-branch convergence effect, a bridge water-blocking effect, and a flood frequency combination effect.

[0009] Optionally, the risk indicators include a flooded area, a flooded water depth, an affected population, and an economic loss.

[0010] Optionally, based on the cascade risk amplification coefficient, a risk amplification effect grading standard is established, and a critical risk amplification interval is identified, specifically including: The flood inundated area is divided into multiple intervals according to water depth, based on the cascade risk amplification coefficient of each interval, and in combination with the disaster-bearing body density data, an interval that meets a set condition is identified as a critical risk amplification interval, wherein the set condition includes that the disaster-bearing body density is higher than a preset density threshold, and the cascade risk amplification coefficient of the economic loss is higher than a set coefficient threshold.

[0011] Optionally, after performing the step of “dividing the flood inundated area into multiple intervals according to water depth, based on the cascade risk amplification coefficient of each interval, and in combination with the disaster-bearing body density data, identifying an interval that meets a set condition as a critical risk amplification interval”, the mountain flood disaster risk loss quantitative assessment method considering cascade effects further includes: based on a basic risk value and the cascade risk amplification coefficient, performing spatial heterogeneity analysis on the critical risk amplification interval to identify a risk region type, wherein the basic risk value refers to an initial risk level under a baseline working condition, the risk region type includes a high-risk concentrated region, a high-sensitivity amplification region, and a low-risk stable region, the high-risk concentrated region is a region in which the basic risk value is higher than a first threshold and the cascade risk amplification coefficient is between a first range, the high-sensitivity amplification region is a region in which the basic risk value is lower than a second threshold and the cascade risk amplification coefficient is higher than a third threshold, and the low-risk stable region is a region in which the basic risk value is lower than the second threshold and the cascade risk amplification coefficient is lower than a fourth threshold.

[0012] Optionally, the risk loss assessment result includes economic losses of various disaster-bearing bodies and total economic losses.

[0013] In a second aspect, the present application provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the quantitative assessment method of mountain flood disaster risk loss considering cascade effects according to the first aspect.

[0014] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the quantitative assessment method of mountain flood disaster risk loss considering cascade effects according to the first aspect.

[0015] According to the specific embodiments provided in the application, the application has the following technical effects: The application provides a mountain torrent disaster risk loss quantitative evaluation method and device considering cascade effect, which constructs a complete technical logic chain of "data support-scenario comparison-quantitative analysis-grade identification-model evaluation": first, high-precision basic data are obtained, a hydrodynamic model is constructed, a scenario matrix containing a benchmark working condition and multiple coupled working conditions (covering multiple branch and junction, bridge water blocking cascade risk factors) is set, accurate comparison basis is provided for cascade effect analysis, and key simulation results such as flooded area and water depth are output; then, the risk index values (flooded area, flooded water depth, affected population and economic loss) of the benchmark and coupled working conditions are used to calculate the cascade risk amplification coefficient, the multi-factor coupled cascade effect which is difficult to quantify is converted into a calculable dimensionless coefficient, and the cascade effect is accurately measured; then, the risk amplification effect grading standard is established, and the key risk amplification interval is identified, the most significant core area of the cascade effect is accurately locked, and evaluation generalization is avoided; finally, the loss quantitative evaluation model considering the cascade effect is constructed for the key risk interval, the cascade risk amplification coefficient is combined with the basic loss and value coefficient of the hazard-affected body, the organic connection from cascade effect quantification to risk loss calculation is realized, and finally the scientific risk loss evaluation result is output, which solves the problems of single factor analysis, cascade effect difficult to quantify and inaccurate loss evaluation in the traditional method, and realizes the accurate quantification of cascade effect and the scientific evaluation of risk loss. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0017] Figure 1 FIG. 1 is a flowchart of a mountain torrent disaster risk loss quantitative evaluation method considering cascade effect in Embodiment 1 of the application; Figure 2 FIG. 2 is a comparison diagram of loss amounts of each working condition of P=1% design flood in Embodiment 1 of the application.

[0018] Figure 3 FIG. 3 is a comparison diagram of loss amounts of different regions of P=1% design flood in Embodiment 1 of the application.

[0019] Figure 4 FIG. 4 is a structural schematic diagram of a computer device provided in Embodiment 2 of the application. DETAILED DESCRIPTION

[0020] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0021] The cascade effect refers to a nonlinear risk amplification phenomenon generated by the interaction of multiple risk factors (such as multiple branches converging, bridge water blocking, topographic change, etc.) in the process of mountain flood disaster, and is characterized by the risk superposition effect of 1+1>2.

[0022] It is found through research that the traditional mountain flood disaster risk assessment method has many defects, such as the following, and cannot meet the precise disaster prevention and reduction demand, including: (1) Cascade effect is not considered: The traditional method is mostly based on the analysis of a single disaster factor, ignoring the cascade risk amplification effect generated by the coupling of multiple factors, resulting in that the assessment result cannot reflect the actual risk level of the disaster.

[0023] (2) Lack of cascade effect quantification model: In the prior art, there is no mature quantitative model to accurately calculate the cascade risk amplification effect, and the specific influence degree of the cascade effect on the disaster risk cannot be determined.

[0024] (3) Loss quantification method is missing: It is difficult to convert the cascade risk amplification effect into specific economic loss, and it is impossible to accurately predict the direct economic loss caused by the disaster, which is not conducive to subsequent disaster relief fund planning and resource allocation.

[0025] (4) Lack of understanding of spatial heterogeneity: There is less research on the spatial heterogeneity characteristics of risk evolution, and the accuracy of the assessment method is low, which cannot accurately identify the risk differences in different regions.

[0026] (5) Difficulty in identifying key risk intervals: There is a lack of effective key risk interval identification method, and it is difficult to locate the most significant risk interval and spatial position of the cascade effect, resulting in a lack of pertinence in disaster prevention and reduction work.

[0027] To this end, the present embodiment realizes the accurate quantification of the cascade risk amplification effect and the scientific assessment of the risk loss by constructing a cascade risk amplification effect quantification model and a loss quantitative assessment system.

[0028] In order to make the above-mentioned purposes, characteristics and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0029] Embodiment 1 The present embodiment provides a mountain flood disaster risk loss quantitative assessment method considering cascade effect, comprising: Obtain basic data of the study area; Constructing a hydrodynamic model based on the basic data, setting multiple scenario calculation conditions, simulating the flood evolution process under different conditions, and outputting simulation results, wherein the conditions include a baseline condition and at least one coupled condition, and the coupled condition considers at least one cascade risk factor; Calculating the risk index value under each working condition based on the simulation results, and calculating the cascade risk amplification factor of each risk index based on the risk index values ​​of the baseline working condition and the coupled working condition; Based on the cascading risk amplification factor, a risk amplification effect grading standard is established to identify key risk amplification intervals; Construct a quantitative loss assessment model that considers cascading effects; For the identified key risk amplification interval, a risk loss assessment result is output according to the loss quantitative assessment model.

[0030] The following combination Figure 1 The quantitative assessment method of mountain torrent disaster risk losses considering the cascading effect of this embodiment is described in detail.

[0031] like Figure 1 As shown in Figure 2, the quantitative assessment method for flash flood disaster risk losses considering cascading effects includes: S1. Basic data acquisition and hydrodynamic modeling Obtain high-precision terrain data, land use data and hazard-prone body distribution data of the study area.

[0032] Based on high-precision terrain data, watershed hydrological parameters (such as confluence path, catchment area, river network, etc.) are extracted; based on the watershed hydrological parameters and land use data, a hydrodynamic model of the two-dimensional shallow water equation (i.e., a two-dimensional hydrodynamic model) is constructed. The land use data is used to determine the roughness parameters of different underlying surfaces in the hydrodynamic model (roughness coefficients of different underlying surfaces such as water areas, cultivated land, forest land, and building land), which directly affects the accuracy of flood evolution simulation; the distribution data of hazard-prone objects (including the spatial distribution and value information of residential houses, infrastructure, agricultural land, etc.) is used for loss assessment calculations in subsequent steps, and is superimposed with the inundation range and water depth output by the hydrodynamic model to calculate the exposure degree of various hazard-prone objects.

[0033] UAV oblique photography technology is used to obtain high-precision terrain data with a resolution better than 10 cm, which is used to construct an accurate digital elevation model. DOM, DSM, and DEM data are obtained by setting predetermined altitude and overlap parameters.

[0034] Build a two-dimensional hydrodynamic model, set multiple scenario calculation conditions, and provide a comparative basis for cascade effect analysis: The purpose of building a two-dimensional hydrodynamic model is to simulate the flood evolution process under different scenarios, output simulation results such as inundation area, water depth, and flow velocity, and provide basic data for the calculation of cascade risk amplification coefficients in step S2.

[0035] The multiple scenario condition setting includes: ① Reference condition: design flood conditions of different return periods; ② Multiple branch confluence condition: achieve synchronization of multiple branch flood peaks through time adjustment; ③ Bridge water blocking condition: set different bridge water blocking conditions; ④ Compound condition: combination scenario of multiple risk factors.

[0036] S2, Construction of cascade risk amplification coefficient quantification model Identify the main cascade risk factors, compare and analyze the changes of specific risk indicators (risk indicators include quantifiable indicators such as inundation area, inundation water depth, affected population, and economic loss) under different scenarios, and establish a cascade risk amplification coefficient quantification model to quantify the amplification effect of each risk factor:

[0037] Among them, is the cascade risk amplification coefficient of the ith indicator, is the value of the ith risk indicator under the coupled scenario, is the value of the ith risk indicator under the reference scenario.

[0038] Superimpose the inundation area and water depth distribution map output by the hydrodynamic model in step S1 with the distribution map of the hazard- bearing body in GIS (Geographic Information System) to determine which hazard- bearing bodies are located in the inundation area and their corresponding inundation water depth. Combined with the vulnerability curve or loss rate function of each type of hazard- bearing body (i.e., the loss proportion of asset value under different water depths), the economic loss of each type of hazard- bearing body can be calculated.

[0039] The main cascade risk factors include: ① Multiple branch confluence effect: adjust the arrival time of branch flood peaks to make multiple branch flood peaks synchronize into the main stream, and analyze the amplification effect of multiple branch synchronization on the main stream risk; ② Bridge water blocking effect: set different obstruction degrees (50%, 90% flow area contraction) of bridge flow section contraction, and analyze the amplification effect of bridge water blocking on upstream risk; ③ Flood frequency combination effect: set combination scenarios of different return period floods, and analyze the risk amplification characteristics under compound scenarios; The cascade risk amplification coefficient is determined by comparative analysis of the reference scenario and the coupling scenario.

[0040] S3, cascade risk amplification effect grading evaluation According to the risk amplification coefficient distribution characteristics of the actual calculation results, cascade risk amplification effect grading standards are established, a plurality of water depth grading thresholds are set, and the risk transmission characteristics of different water depth intervals are analyzed. The specific process is as follows: the submerged area is divided into a plurality of intervals (such as 0-0.05m, 0.05-0.3m, 0.3-0.5m, 0.5-1.0m, 1.0-2.0m, >2.0m) according to water depth, the submerged area, the number of affected hazard- bearing bodies and the economic loss in each water depth interval under the reference scenario and the coupling scenario are respectively counted, the cascade risk amplification coefficient of each water depth interval is calculated, and the water depth interval with the most significant risk amplification effect is determined by comparative analysis.

[0041] The water depth interval with the most significant risk amplification effect identified is the key area of attention for loss quantitative evaluation, and is used for preferential allocation of disaster prevention and mitigation resources. These intervals are the areas with the most prominent cascade effect, and detailed loss evaluation and protection measures need to be deployed.

[0042] According to the risk transmission mode of different water depth intervals, a water depth-loss rate nonlinear relationship model is established to identify the key risk amplification interval. The key risk amplification interval identified is defined as the area with a risk amplification coefficient λ>1.5 and a high hazard-bearing body density, which meets the following two conditions: (1) the risk amplification coefficient caused by the cascade effect exceeds the set threshold; (2) there are many hazard-bearing bodies (residential buildings, infrastructure, etc.) in the area.

[0043] It should be noted that when identifying the key risk amplification interval, a comprehensive determination method is adopted: first, the risk amplification coefficients of various risk indicators (submerged area, affected population, economic loss, etc.) are calculated; the economic loss risk amplification coefficient is used as the main determination index; when the economic loss risk amplification coefficient λ>1.5 and the hazard-bearing body density in the area is high, the key risk amplification interval is determined (other risk indicators are used as auxiliary references to help comprehensively evaluate the risk situation).

[0044] The identified key risk amplification interval is analyzed for spatial heterogeneity to identify the risk area type, which includes: ① High risk concentration type: refers to an area with high basic risk (loss is already large under the reference scenario) and moderate amplification coefficient (1.2<λ<1.5); this type of area is a high-risk area itself, and the cascade effect will further exacerbate the risk. Among them, the basic risk refers to the initial risk level under the reference scenario, which is obtained by comprehensive evaluation of the submerged water depth, hazard-bearing body density and other indicators under the reference scenario; ② High sensitivity amplification type: refers to an area with low basic risk and high amplification coefficient; ③ Low-risk stable: refers to an area with low basic risk and low amplification coefficient.

[0045] This step establishes a set of interlocking identification processes through refined water depth division. The system realizes the leap from physical mechanism analysis to precise risk control. First, by comparing the economic loss rate of different water depth intervals (such as 0.3-0.5m, 1.0-2.0m) under the benchmark and coupled conditions, the water depth interval with the most significant risk amplification effect is identified, which reveals the physical law and core damage threshold of the cascading effect on floods. Then, the above physical law is superimposed with the spatial distribution of disaster-bearing bodies to lock in areas that meet both the "in the significantly amplified water depth interval" and "high disaster-bearing body density" conditions, thereby identifying the key risk amplification interval and achieving the key transformation of risk from "physical dimension" to "social and economic impact dimension". Finally, based on the combination of "basic risk level" and "risk amplification coefficient" of each key area, its type is determined, and it is divided into "high-risk concentrated type", "high-sensitive amplification type", etc., thereby clarifying its risk cause nature and providing direct basis for differentiated prevention and control strategy formulation. The entire process is progressive and gradually focused, with water depth division as the quantitative cornerstone throughout the process, ensuring the scientificity and accuracy of the analysis.

[0046] S4, risk loss quantitative assessment and output For the key risk amplification interval identified in step S3, a loss quantitative assessment model considering cascading effects is established. This model directly quantifies the cascading risk amplification effect as a loss amplification coefficient, realizing the organic connection from risk quantification to loss assessment:

[0047] wherein, is the total direct economic loss, is the basic loss of the jth disaster-bearing body kth unit, is the corresponding risk amplification coefficient, is the disaster-bearing body value coefficient; and the risk loss assessment result information is output.

[0048] For each scenario (including multi-branch convergence conditions, bridge water-blocking conditions, and composite conditions), risk area type judgment and loss quantitative assessment are required. The loss quantitative assessment classification calculation includes: ① Residential building loss:

[0049] wherein, represents the economic loss of residential buildings (ten thousand yuan), represents the number of affected houses (units), represents the unit price of houses (ten thousand yuan / unit), The house damage rate (%), The cascade risk amplification coefficient of house loss; ② Household property loss:

[0050] Among them, The economic loss of household property (ten thousand yuan), The number of affected households (households), The average property value of households (ten thousand yuan / household), The property loss rate (%),

[0051] The cascade risk amplification coefficient of household property loss; ③ Agricultural loss:

[0052] Among them, The economic loss of agriculture (ten thousand yuan), The affected crop area (mu), The unit area output value (ten thousand yuan / mu), The crop damage rate (%), The cascade risk amplification coefficient of agricultural loss; ④ Infrastructure loss:

[0053] Among them, The economic loss of infrastructure (ten thousand yuan), The asset value of infrastructure (ten thousand yuan), The damage degree of infrastructure (%), The cascade risk amplification coefficient of infrastructure loss; Among them, the cascade risk amplification coefficient λ of each type of loss is determined by the economic loss ratio of the disaster-bearing body in the coupling scenario and the baseline scenario, and the differentiated damage degree caused by different inundation water depths is considered comprehensively in the calculation.

[0054] The above mountain flood risk loss quantitative assessment method considering cascade effect of the embodiment: ①innovatively realizes the accurate quantification of cascade effect: a cascade risk amplification quantification method based on scenario comparison analysis is established. ②Identify the key mechanism and interval of risk amplification: the system reveals that the specific water depth interval is the core interval of cascade risk amplification, providing scientific basis for accurate prevention and control. ③Innovatively establish a cascade risk amplification effect grading evaluation system: a cascade risk amplification effect grading standard is established, which provides support for formulating differentiated risk management strategies for different regions. ④Improve the accuracy and reliability of mountain flood risk assessment: through independent risk assessment and loss calculation of each scenario, the disaster risk characteristics under different conditions can be fully mastered, providing scientific support for emergency plan formulation.

[0055] Taking a certain basin as the research object, the basin area is 152km 2 , and the cascade risk assessment method of the embodiment is used.

[0056] (1) Basic data acquisition and modeling Data acquisition: adopt unmanned aerial vehicle to carry APS-130 five-lens camera for oblique photography, set flight height 130m, heading and lateral overlap degree 80%, obtain DOM, DSM, DEM data with resolution 5cm.

[0057] Water dynamics modeling: adopt MIKE21 Flow Model FM to construct two-dimensional water dynamics model, the modeling range area is 9.71km 2 , set 8787 triangular calculation grids, 4627 calculation nodes, the grid length of key area is 10-20m.

[0058] Parameter setting: roughness is set as water area 0.025, farmland 0.055, forest land 0.07, building land 0.09.

[0059] (2) Cascade risk amplification quantification model construction Identify main risk factors and quantification: ①Multi-branch convergence effect: tributary flood peak arrives at confluence section 6 minutes in advance; ②Bridge water blocking effect: set half-block (50% flow area) and full-block (90% blockage) two cases; ③Flood frequency combination effect: P=1%, 2%, 5%, 10%, 20% five return period combinations; Each risk factor is quantified through comparison analysis of benchmark scenario and coupled scenario.

[0060] According to the actual calculation results, the cascade risk amplification effect grading standard is established, and based on the calculation results of the embodiment, the risk amplification coefficient distribution is: the inundation area amplification coefficient is 1.14 (slight amplification), the affected population amplification coefficient is 1.25 (moderate amplification), and the economic loss amplification coefficient is 1.56 (significant amplification).

[0061] (3): Grading evaluation of cascade risk amplification effect ①Taking the P=1% flood as an example, the risk indicators of each scenario are compared: The reference scenario: the inundation area is 2.89km 2 , the maximum water depth is 4.50m, the affected population is 53,000 people, and the economic loss is 91.72 million yuan; The bridge full-block scenario: the inundation area is 3.28km 2 , the maximum water depth is 5.28m, the affected population is 65,000 people, and the economic loss is 125.88 million yuan; The multi-branch convergence scenario: the inundation area is 3.15km 2 , the maximum water depth is 4.98m, the affected population is 59,000 people, and the economic loss is 108.45 million yuan; The composite (bridge full-block + multi-branch convergence) scenario: the inundation area is 3.30km 2 , the maximum water depth is 5.29m, the affected population is 66,000 people, and the economic loss is 143.11 million yuan.

[0062] ②Calculation and grading of risk amplification coefficient of each scenario: Bridge full-block scenario: Inundation area amplification coefficient: λA = 3.28km 2 / 2.89km 2 = 1.13 Affected population amplification coefficient: λP = 65,000 people / 53,000 people = 1.23 Economic loss amplification coefficient: λE = 125.88 million yuan / 91.72 million yuan = 1.37 Multi-branch convergence scenario: Inundation area amplification coefficient: λA = 3.15km 2 / 2.89km 2 = 1.09 Affected population amplification coefficient: λP = 59,000 people / 53,000 people = 1.11 Economic loss amplification coefficient: λE = 108.45 million yuan / 91.72 million yuan = 1.18 Composite (bridge full-block + multi-branch convergence) scenario: Inundation area amplification coefficient: λA = 3.30km 2 / 2.89km 2 = 1.14 Population amplification factor: λP = 0.66 / 0.53 = 1.25 Economic loss amplification factor: λE = 14311 / 9172 = 1.56 According to the above risk amplification factors, the numerical range of λ in this embodiment is classified as follows: slight amplification (1.0≤λ<1.2), moderate amplification (1.2≤λ<1.5), significant amplification (1.5≤λ<2.0), and extremely strong amplification (λ≥2.0).

[0063] ③ Identification of key risk amplification interval: In this embodiment, water depth classification thresholds of 0.05m, 0.3m, 0.5m, 1.0m and 2.0m are set. It is found that the 1.0-2.0m water depth interval is the core interval of cascading risk amplification in the composite (bridge full block + multiple branches converging) scenario. The economic loss in this interval increases from 3154 million yuan to 8443 million yuan, with an increase of 168%, as shown in Figure 2 . Therefore, this water depth interval becomes the focus of loss assessment in step 4.

[0064] (4): Quantitative assessment and output of risk loss ① Spatial heterogeneity analysis (taking the composite scenario as an example): The spatial heterogeneity analysis results are shown in Figure 3 , where Damxi Village is a high-risk concentration type, with a basic scenario loss of 4896 million yuan and a composite scenario loss of 8064 million yuan after cascading effect, with an amplification factor of 1.65. High sensitivity amplification types include Yangjiao Village (basic scenario loss of 1404 million yuan, composite scenario loss of 2683 million yuan after cascading effect, amplification factor of 1.91), Songyan Village (basic scenario loss of 480 million yuan, composite scenario loss of 756 million yuan after cascading effect, amplification factor of 1.57) and Xie Village (basic scenario loss of 163 million yuan, composite scenario loss of 272 million yuan after cascading effect, amplification factor of 1.66); the remaining administrative villages are low-risk stable types.

[0065] ② Quantitative assessment of classified losses (taking the composite scenario as an example): According to the quantitative loss assessment, compared with the baseline scenario, the comparison of each type of loss in the composite scenario is as follows: residential house loss increases from 5621 million yuan to 9336 million yuan (amplification factor 1.66), household property loss increases from 1793 million yuan to 2893 million yuan (amplification factor 1.61), agricultural loss increases from 1527 million yuan to 1763 million yuan (amplification factor 1.15); infrastructure loss increases from 231 million yuan to 319 million yuan (amplification factor 1.38), and total loss increases from 9172 million yuan to 14311 million yuan (total amplification factor 1.56).

[0066] The total loss of each scenario is as follows: the total loss of the reference scenario is 91.72 million yuan, the total loss of the bridge water blocking scenario is 125.88 million yuan (amplification factor 1.37), the total loss of the multi-branch convergence scenario is 108.45 million yuan (amplification factor 1.18), and the total loss of the composite scenario is 143.11 million yuan (amplification factor 1.56). Through the independent evaluation of each scenario, it is identified that the composite scenario has the highest risk amplification effect, and the bridge water blocking is the main cascading risk amplification factor. Among various disaster-bearing bodies, the residential house is most significantly affected by the cascading effect, followed by family property and infrastructure, and the cascading amplification effect of agricultural loss is relatively small.

[0067] In this embodiment, the bridge water blocking is successfully identified as the main cascading risk amplification factor through the intelligent quantitative analysis of the cascading risk of the system, the risk amplification effect under different scenarios is quantified, the loss quantitative evaluation system considering the cascading effect is established, and the precise quantitative evaluation of the mountain flood disaster risk loss is realized, which provides important technical support for the mountain flood disaster prevention and control decision and emergency management.

[0068] Embodiment 2 This embodiment provides a computer device which can be a server or a terminal, and the internal structure diagram thereof can be as shown in Figure 4 The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the data related to the mountain flood disaster risk loss quantitative evaluation method considering the cascading effect. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through network connection. The computer program is executed by the processor to implement the mountain flood disaster risk loss quantitative evaluation method considering the cascading effect in embodiment 1.

[0069] Those skilled in the art can understand that Figure 4The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In one exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in the above method embodiments.

[0070] Embodiment 3 The embodiment provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the method for quantitatively evaluating a flash flood disaster risk loss considering a cascade effect in the above embodiment 1.

[0071] Embodiment 4 The embodiment provides a computer program product, which includes a computer program, and the computer program is executed by a processor to implement the method for quantitatively evaluating a flash flood disaster risk loss considering a cascade effect in the above embodiment 1.

[0072] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of the related data need to comply with relevant regulations.

[0073] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to a memory, a database or other medium used in the embodiments provided in the present application can include at least one of a non-volatile and a volatile memory. The non-volatile memory can include a read-only memory (ROM), a magnetic tape, a floppy disk, a flash memory, an optical storage, a high-density embedded non-volatile memory, a resistive random access memory (ReRAM), a magnetoresistive random access memory (MRAM), a ferroelectric random access memory (FRAM), a phase change memory (PCM), a graphene memory, etc. The volatile memory can include a random access memory (RAM) or an external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as a static random access memory (SRAM) or a dynamic random access memory (DRAM), etc.

[0074] The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0075] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combinations of the technical features do not exist contradictory, they should be considered as the scope of the present application.

[0076] The principles and implementation modes of the present application are described by using specific examples in the present application. The above embodiments are only used to help understand the method and its core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range can be changed. In conclusion, the content of the present application should not be understood as a limitation.

Claims

1. A flash flood disaster risk loss quantitative assessment method considering cascade effect, characterized in that, The flash flood risk loss quantitative assessment method considering the cascade effect comprises the following steps: acquiring basic data of a study area; constructing a water dynamic model based on the basic data, setting multiple scenario calculation working conditions, simulating flood evolution processes under different working conditions, and outputting simulation results, wherein the working conditions include a reference working condition and at least one coupled working condition, and the coupled working condition considers at least one cascade risk factor; calculating risk index values under each working condition based on the simulation results, and calculating a cascade risk amplification coefficient of each risk index based on the risk index values of the reference working condition and the coupled working condition; based on the cascade risk amplification coefficient, establishing a risk amplification effect classification standard, and identifying a key risk amplification interval; constructing a loss quantitative assessment model considering the cascade effect; outputting a risk loss assessment result according to the loss quantitative assessment model for the identified key risk amplification interval. 2.The flash flood risk loss quantitative assessment method considering cascade effect according to claim 1, wherein, The basic data includes terrain data, land use data, and disaster-bearing distribution data. 3.The flash flood risk loss quantitative assessment method considering cascade effect according to claim 1, characterized in that, The coupled working conditions include a multiple-branch confluence working condition, a bridge water-blocking working condition, and a composite working condition. 4.The flash flood risk loss quantitative assessment method considering cascade effect according to claim 1, wherein, The cascade risk factors include a multiple-branch confluence effect, a bridge water-blocking effect, and a flood frequency combination effect. 5.The flash flood risk loss quantitative assessment method considering cascade effect according to claim 1, wherein, The risk indexes include a flooded area, a flooded water depth, an affected population, and an economic loss. 6.The flash flood risk loss quantitative assessment method considering cascade effect according to claim 1, wherein, Based on the cascade risk amplification coefficient, a risk amplification effect classification standard is established, and a key risk amplification interval is identified, specifically comprising: dividing the flood inundated area into multiple intervals according to water depth, identifying intervals that meet a set condition as a key risk amplification interval based on the cascade risk amplification coefficient of each interval and in combination with disaster-bearing body density data, wherein the set condition includes that the disaster-bearing body density is higher than a preset density threshold and the cascade risk amplification coefficient of economic loss is higher than a set coefficient threshold. 7.The flash flood risk loss quantitative assessment method considering cascade effect according to claim 6, characterized in that, After performing the step of "dividing the flood inundated area into multiple intervals according to water depth, identifying intervals that meet a set condition as a key risk amplification interval based on the cascade risk amplification coefficient of each interval and in combination with disaster-bearing body density data", the flash flood risk loss quantitative assessment method considering the cascade effect further comprises: performing spatial heterogeneity analysis on the key risk amplification interval based on a basic risk value and the cascade risk amplification coefficient, and identifying a risk area type, wherein the basic risk value refers to an initial risk level under the reference working condition, the risk area type includes a high-risk concentrated type area, a high-sensitivity amplification type area, and a low-risk stable type area, the high-risk concentrated type area is an area where the basic risk value is higher than a first threshold and the cascade risk amplification coefficient is between a first range, the high-sensitivity amplification type area is an area where the basic risk value is lower than a second threshold and the cascade risk amplification coefficient is higher than a third threshold, and the low-risk stable type area is an area where the basic risk value is lower than the second threshold and the cascade risk amplification coefficient is lower than a fourth threshold. 8.The flash flood risk loss quantitative assessment method considering cascade effect according to claim 1, wherein, The risk loss assessment result includes economic losses of various disaster-bearing bodies and total economic losses.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method for quantitatively evaluating flash flood risk loss considering cascade effect according to any one of claims 1-8.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the method for quantitatively evaluating flash flood risk loss considering cascade effect according to any one of claims 1-8.

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