Flood process risk assessment method, device, equipment, medium and product

Abstract

The invention relates to a risk assessment method, device and equipment for a flood process, a medium and a product, and belongs to the technical field of urban flood risk assessment and prevention and control, and the method comprises the steps: calculating an influence parameter of at least one disaster-bearing body according to a hydraulic parameter of a hydraulic condition; obtaining risk distribution of each disaster-bearing body at any moment according to the influence parameter of each disaster-bearing body and the distribution density of the disaster-bearing body at any moment; at any moment, the weight value of the disaster-bearing body is calculated according to the instability threshold value of the influence parameter, and the dynamic weight value of each disaster-bearing body is obtained; and evaluating according to the dynamic weight values of all the disaster-bearing bodies and the risk distribution of all the disaster-bearing bodies at any moment to obtain a flood process moment-by-moment comprehensive risk result comprising all the disaster-bearing bodies. And the flood process risk is dynamically evaluated through the dynamic weight value, so that the risk evaluation result is more consistent with the actual scene and is more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of urban flood risk assessment and prevention and control, and particularly relates to a risk assessment method, device, equipment, medium and product for a flood process. Background Art

[0002] Driven by the dual factors of global climate change and urbanization process, the intensity and frequency of urban waterlogging disasters are rising synchronously, which not only poses a serious threat to the safety of residents' lives and property, but also significantly restricts the social and economic development.

[0003] The current mainstream methods judge the damage degree of disaster-bearing bodies based on the inundation depth and flow velocity, but most of them are based on statistical principles and are difficult to characterize the essential characteristics of the destructive power of floods on different disaster-bearing bodies.

[0004] Based on this, the early warning accuracy rate of traditional flood risk assessment models is seriously insufficient in practical applications, and it is difficult to meet the needs of precise prevention and control of smart cities, seriously hindering the improvement of the prevention and control efficiency of urban flood disasters. Summary of the Invention

[0005] To solve the above problems, the present application provides a risk assessment method, device, equipment, medium and product for a flood process.

[0006] The first aspect of the embodiments of the present application provides a risk assessment method for a flood process, including:

[0007] Obtain hydraulic parameters under at least one hydraulic condition;

[0008] Calculate influence parameters of at least one disaster-bearing body according to the hydraulic parameters of the hydraulic condition; wherein, one disaster-bearing body corresponds to one influence parameter;

[0009] Obtain the risk distribution of each disaster-bearing body at any moment according to the influence parameter of each disaster-bearing body and the distribution density of the disaster-bearing body at any moment;

[0010] At any moment, calculate the weight value of the disaster-bearing body according to the instability threshold of the influence parameter, and obtain the dynamic weight value of each disaster-bearing body;

[0011] Evaluate according to the dynamic weight values of all disaster-bearing bodies and the risk distribution of all disaster-bearing bodies at any moment, and obtain the comprehensive risk result of each moment of the flood process including all disaster-bearing bodies.

[0012] Optionally, the calculating the weight value of the disaster-bearing body according to the instability threshold of the influence parameter specifically includes:

[0013] Obtain the instability threshold corresponding to each instability state according to at least one instability state of the influence parameters of each disaster-bearing body;

[0014] Construct a judgment matrix for the criterion layer of the analytic hierarchy process according to the instability thresholds of all influence parameters;

[0015] Calculate the weight value of the disaster-bearing body according to the judgment matrix.

[0016] Optionally, the disaster-bearing bodies include: people, transportation, and buildings;

[0017] The obtaining of the instability threshold corresponding to each instability state according to at least one instability state of the influence parameters of each disaster-bearing body specifically includes:

[0018] Based on various instability states of the influence parameters corresponding to people, obtain the first instability threshold and the second instability threshold of people;

[0019] Based on various instability states of the influence parameters corresponding to transportation, obtain the first instability threshold and the second instability threshold of transportation.

[0020] Optionally, the evaluation based on the dynamic weight values of all disaster-bearing bodies and the risk distribution of all disaster-bearing bodies at any moment to obtain the flood process moment-by-moment comprehensive risk result including all disaster-bearing bodies specifically includes:

[0021] Calculate the single risk of each disaster-bearing body according to the distribution of the disaster-bearing body and its influence parameters;

[0022] Take the single risk of each disaster-bearing body as the evaluation index of the fuzzy matter element;

[0023] Construct multiple fuzzy matter element matrices according to multiple evaluation indexes and multiple moments, and calculate the dynamic weight value of each disaster-bearing body according to the fuzzy matter element matrix;

[0024] Conduct comprehensive risk calculation according to the single risk of each disaster-bearing body and the dynamic weight value of each disaster-bearing body to obtain the flood process moment-by-moment comprehensive risk result including all disaster-bearing bodies.

[0025] Optionally, the calculation of the influence parameters of at least one disaster-bearing body according to the hydraulic parameters of the hydraulic conditions specifically includes:

[0026] Calculate the influence parameters of at least one disaster-bearing body according to the hydraulic parameters of the hydraulic conditions in combination with the unit-width flow energy and momentum equations, where the hydraulic parameters of the hydraulic conditions include: submergence depth and submergence velocity.

[0027] Optionally, it further includes:

[0028] Simulate the flooding process through a city flooding model to obtain the flood depth and flood velocity under at least one hydraulic condition.

[0029] The second aspect of the embodiments of the present application provides a risk assessment device for a flood process, including:

[0030] A hydraulic parameter acquisition module, configured to acquire hydraulic parameters under at least one hydraulic condition;

[0031] An impact parameter calculation module, configured to calculate the impact parameters of at least one disaster-bearing body according to the hydraulic parameters of the hydraulic conditions; wherein, one disaster-bearing body corresponds to one impact parameter;

[0032] A risk distribution module, configured to obtain the risk distribution of each of the disaster-bearing bodies at any moment according to the impact parameter of each of the disaster-bearing bodies and the distribution density of the disaster-bearing bodies at any moment;

[0033] A dynamic weight calculation module, configured to calculate the weight value of the disaster-bearing body according to the instability threshold of the impact parameter at any moment, and obtain the dynamic weight value of each of the disaster-bearing bodies;

[0034] A risk assessment module, configured to perform an assessment according to the dynamic weight values of all the disaster-bearing bodies and the risk distribution of all the disaster-bearing bodies at any moment, and obtain the comprehensive risk results of each moment of the flood process including all the disaster-bearing bodies.

[0035] The third aspect of the embodiments of the present application provides an electronic device, including a memory and a processor, wherein,

[0036] The memory is used to store a program;

[0037] The processor is coupled to the memory and is configured to execute the program stored in the memory to implement the steps in a risk assessment method for a flood process according to any of the above solutions.

[0038] The fourth aspect of the embodiments of the present application is a computer-readable storage medium for storing computer-readable programs or instructions, and when the programs or instructions are executed by a processor, the steps in a risk assessment method for a flood process according to any of the above solutions can be implemented.

[0039] The fifth aspect of the embodiments of the present application is a computer program product, including a computer program / instruction, characterized in that when the computer program / instruction is executed by a processor, the steps in a risk assessment method for a flood process according to any of the above solutions are implemented.

[0040] Applying the technical solution provided by the embodiments of the present application, by calculating the weight values of multiple disaster-bearing bodies through the instability threshold of the influencing parameters at each moment, different weight values of multiple disaster-bearing bodies are obtained at each moment, and the risk of the flood process is dynamically evaluated based on the different dynamic weight values at each moment. This makes the risk assessment result more consistent and accurate with the actual scenario. Description of the Drawings

[0041] To more clearly illustrate the technical solution of the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments of the present application. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0042] Figure 1 It is a flowchart of the steps of a method for risk assessment of a flood process provided in an embodiment of the present invention;

[0043] Figure 2 It is a schematic diagram of an urban inundation model provided in an embodiment of the present invention;

[0044] Figure 3 It is a flowchart of the steps for calculating the dynamic weight value of a disaster-bearing body based on the analytic hierarchy process provided in an embodiment of the present invention;

[0045] Figure 4 It is a flowchart of the steps for comprehensive risk calculation by the fuzzy matter-element method provided in an embodiment of the present invention;

[0046] Figure 5 It is a heat map of the relationship surface (a) of the lumped parameter with the inundation depth and flow velocity and LP (b), LV (c), LB (d), where the lumped parameter is the influencing parameter.

[0047] Figure 6 It is a schematic diagram of the spatio-temporal distribution of LP (a), LV (b), and LB (c) provided in an embodiment of the present invention.

[0048] Figure 7 It is a schematic diagram of the spatio-temporal distribution of the density of population (a), traffic (b), and buildings (c) provided in an embodiment of the present invention.

[0049] Figure 8 It is a schematic diagram of the spatio-temporal distribution of the risk of population (a), traffic (b), and buildings (c) provided in an embodiment of the present invention.

[0050] Figure 9 It is the dynamic weight classification (a) and the heat map of the weight value (b) provided in an embodiment of the present invention.

[0051] Figure 10 It is a schematic diagram of the spatio-temporal distribution of the comprehensive risk of the flood process based on dynamic weights provided in the embodiments of the present invention.

[0052] Figure 11 It is a schematic diagram of the spatio-temporal distribution of the comprehensive risk of the flood process based on static weights provided in the embodiments of the present invention.

[0053] Figure 12 It is a schematic diagram of the dynamic weight comprehensive risk (a), static weight comprehensive risk (b), dynamic weight category (c), population risk (d), traffic risk (e), and building risk (f) at 11 o'clock in area D provided in the embodiments of the present invention.

[0054] Figure 13 It is a structural block diagram of a risk assessment device for a flood process provided in the embodiments of the present invention;

[0055] Figure 14 It is a hardware structural block diagram of an electronic device provided in the embodiments of the present invention. Detailed implementation manners

[0056] To make the above objects, features, and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the 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.

[0057] Referring to Figure 1 as shown, a step flow chart of a risk assessment method for a flood process is shown. This method can be applied to urban flood risk assessment and prevention and control, such as Figure 1 as shown, specifically including the following steps:

[0058] Step S101, obtain hydraulic parameters under at least one hydraulic condition;

[0059] In one embodiment, the inundation process is simulated through an urban inundation model to obtain the inundation depth and inundation velocity under at least one hydraulic condition. At least one hydraulic condition represents the hydraulic conditions of different inundation depths and velocities under different rainfall conditions.

[0060] In one embodiment, an urban inundation model is constructed based on the PCSWMM software, such as Figure 2As shown in the figure, the model construction is divided into five parts. First, the one-dimensional model of the river network and underground pipe network is generalized. Then, the sub-watersheds are divided. After that, the two-dimensional overland flow model is generalized according to the DEM and building distribution. Next, the one-dimensional and two-dimensional models are coupled through orifices. Finally, the parameters of the urban inundation model are calibrated. After the model construction is completed, the inundation depth and velocity distribution under rainfall conditions with different return periods are simulated.

[0061] Step S102: Calculate the influence parameters of at least one disaster-bearing body according to the hydraulic parameters of the hydraulic conditions; wherein, one disaster-bearing body corresponds to one influence parameter; the disaster-bearing bodies include: people, vehicles, and buildings.

[0062] In one embodiment, based on the expressions of unit-width discharge energy and momentum, the influence parameters of people, vehicles, and buildings are obtained.

[0063]

[0064] F = V(gD) -1 / 2 ,

[0065] where LP, LV, and LB are the influence parameters of people, vehicles, and buildings respectively; D is the inundation depth; F is the Froude number; V is the inundation velocity; g is the acceleration due to gravity.

[0066] Step S103: Obtain the risk distribution of each disaster-bearing body at any moment according to the influence parameter of each disaster-bearing body and the distribution density of the disaster-bearing body at any moment.

[0067] In one embodiment, from the above steps, it can be known that the influence parameters of people, vehicles, and buildings at the nth grid at time t are LP(n,t), LV(n,t), and LB(n,t). The risks of population, traffic, and buildings are evaluated by the following formulas respectively.

[0068] RP(n,t) = PD(n,t) × LP(n,t),

[0069] RT(n,t) = RD(n,t) × LV(n,t),

[0070] RB(n,t) = BD(n,t) × LB(n,t),

[0071] where RP(n,t), RT(n,t), and RB(n,t) are the risks of population, traffic, and buildings at the nth grid at time t respectively; PD(n,t), RD(n,t), and BD(n,t) are the population, traffic, and building densities at the nth grid at time t respectively, and the spatial resolution is 12.5m × 12.5m.

[0072] Step S104: At any moment, calculate the weight value of the disaster-bearing body according to the instability threshold of the influencing parameter, and obtain the dynamic weight value of each disaster-bearing body;

[0073] In one embodiment, the instability state and threshold of the human influencing parameter include: population stability state: LP < 0.15 (stable), 0.15 ≤ LP < 0.40 (critically unstable), LP ≥ 0.40 (severely unstable);

[0074] The instability state and threshold of the traffic influencing parameter include: vehicle failure state: LV < 0.10 (normal), 0.10 ≤ LV < 0.4 (partially failed), LV ≥ 0.40 (completely failed);

[0075] The instability threshold of the building influencing parameter: Due to the structural characteristics of the building itself, it is difficult to have an instability situation. Therefore, only the population stability threshold and the vehicle failure threshold need to be determined.

[0076] According to the instability threshold of the population and the traffic instability threshold, conduct dynamic weight classification, and determine the weight value through the Analytic Hierarchy Process (AHP). Step S105: Evaluate according to the dynamic weight values of all disaster-bearing bodies and the risk distribution of all disaster-bearing bodies at any moment, and obtain the flood process moment-by-moment comprehensive risk result including all disaster-bearing bodies.

[0077] In one embodiment, according to the dynamic weight values of humans, traffic, and buildings combined with the fuzzy matter-element method, calculate the flood comprehensive risk value, and highlight it according to the risk distribution of humans, traffic, and buildings at any moment, and obtain the flood process moment-by-moment comprehensive risk result including humans, traffic, and buildings.

[0078] In one embodiment, define the fuzzy matter-element M = (R, C, V), where R is the risk level (low, medium, high); C is the evaluation index (RP, RT, RB); V is the membership function (triangular fuzzy number), and determine the final risk level through the maximum membership degree principle;

[0079] CR(n,t) = WPR S (n,t) × μ RP (n,t) + WTR S (n,t) × μ RT (n,t) + WBR S (n,t) × μ RB (n,t)

[0080] Among them, the flood comprehensive risk at the t-th moment of the n-th grid is CR(n,t); the weights and membership degrees of RP(n,t), RT(n,t), and RB(n,t) are WPR S (n,t), WTR S(n, t), WBR S (n, t) and μ RP (n, t), μ RT (n, t), μ RB (n, t); S is the type of dynamic weight.

[0081] The technical bottlenecks in the field of current urban flood risk assessment are mainly reflected in the following aspects: First, traditional static assessment models ignore the spatio-temporal variation characteristics of hydraulic parameters such as submerged depth and disaster-bearing bodies such as population during the urban flood process, and it is difficult to characterize the complex spatio-temporal coupling effect between the two. In addition, existing methods generally adopt a fixed weight index system, that is, once the flood risk index weight is determined, it remains unchanged throughout the flood process. The above assumption is obviously inconsistent with the actual situation. In flood events, under different hydraulic conditions, the instability degrees of disaster-bearing bodies (such as people, vehicles, and buildings) are different. For example, when the submerged depth is relatively low, the self-evacuation ability of people is significant, and at this time, the weight ratios of traffic and buildings are larger; while when the submerged depth exceeds the human safety threshold, the risk of people being trapped surges, and the weight ratio of population safety needs to be increased. Finally, there are theoretical defects in the physical damage mechanism of urban flood risk assessment. Although current mainstream methods judge the damage degree of disaster-bearing bodies based on submerged depth and flow velocity, most of them are based on statistical principles and are difficult to characterize the essential characteristics of flood destructive power. The damage effect of flood on disaster-bearing bodies is essentially a non-linear interaction process between the kinetic energy of water flow and the structural resistance of disaster-bearing bodies, and it is necessary to synchronously characterize the energy and momentum based on unit-width discharge.

[0082] The technical solution of the embodiment of the present application is driven by a physical mechanism: quantifying the damage effect of water flow energy and momentum on disaster-bearing bodies through lumped influence parameters, and improving the objectivity of assessment;

[0083] Optimizing through dynamic weights: dynamically adjusting weights based on the lumped influence parameter threshold of disaster-bearing bodies, and improving the accuracy of urban flood risk identification;

[0084] Enhancing decision support for the solution of the present application: coupling the hydraulic characteristics of flood routing with the spatio-temporal variation characteristics of disaster-bearing bodies, outputting a risk spatio-temporal heat map, and guiding the planning of emergency evacuation routes and the layout of flood control facilities.

[0085] In one embodiment, the risk assessment method for the flood process is applied to the flood risk assessment of the southern area of Jinshui District, Zhengzhou City. A DEM (Digital Elevation Model) with a spatial resolution of 12.5 meters was obtained from the Alaska Satellite Facility (https: / / search.asf.alaska.edu / # / ?dataset=ALOS). Subsequently, using the slope tool in ArcGIS, a slope dataset with the same resolution of 12.5 meters was derived from the DEM. Rainfall records were obtained from the Zhengzhou Meteorological Bureau, while data on waterlogging-prone areas, drainage infrastructure, building distribution, and road networks were provided by the Zhengzhou Municipal Administration Bureau. Historical inundation data was compiled through on-site investigations and web crawling. Finally, a population heat map with a spatial resolution of 12.5 meters was obtained from Baidu Maps (https: / / huiyan.baidu.com);

[0086] In one embodiment, as Figure 2 shown, the construction and calibration of the urban inundation model include:

[0087] According to the distribution of roads and buildings, the study area was divided into 292 sub-catchments. Using drainage pipe and river data, the drainage pipe network and rivers in the study area were generalized into 926 inspection wells and 939 pipes. Based on the DEM of the study area and considering the obstruction effect of buildings on water accumulation, the two-dimensional surface consists of 183,298 two-dimensional (2D) nodes, 489,527 2D pipes, and 183,298 2D grids (with an average resolution of 12.5m×12.5m). The drainage pipe network and the surface are connected to each other through 948 orifices. The model accuracy was verified through rainfall events on July 21, 2021 (Event A) and July 22, 2024 (Event B). The results show that all waterlogging-prone points were inundated in Event A, and 87.5% of the waterlogging-prone areas were inundated in Event B, indicating that the model is reliable.

[0088] In one embodiment, referring to Figure 3 shown, a flowchart of the steps for calculating the dynamic weight value of the disaster-bearing body is shown, as Figure 3 shown, and specifically may include the following steps:

[0089] Step S301: Obtain the instability threshold corresponding to each instability state according to at least one instability state of the influence parameters of each disaster-bearing body;

[0090] Step S302: Construct a judgment matrix for the criterion layer of the analytic hierarchy process according to the instability thresholds of all influence parameters;

[0091] Step S303: Calculate the weight value of the disaster-bearing body according to the judgment matrix.

[0092] In one embodiment, calculating the weight value of the disaster-bearing body according to the analytic hierarchy process includes: establishing a hierarchical structure: including an objective layer and a criterion layer.

[0093] 1. Construct the hierarchical structure:

[0094] Objective layer: Comprehensively evaluate the stability of the region.

[0095] Criterion layer: Includes three criteria, namely population stability / failure threshold (LP), vehicle stability / failure threshold (LV), and building failure threshold (LB).

[0096] 2. Construct the judgment matrix;

[0097] Construct the judgment matrix of the criterion layer. Invite experts to score the importance of "population stability / failure threshold", "vehicle stability / failure threshold", and "building failure threshold" pairwise with respect to the objective, and obtain the judgment matrix of the criterion layer.

[0098] 3. Calculate the weights:

[0099] For each element in the criterion layer, conduct pairwise comparisons. Pairwise comparisons are used to determine the weights of two elements by comparing their relative importance.

[0100] According to the results of the pairwise comparisons, calculate the weight of each element by solving the eigenvalue.

[0101] Based on the magnitude of the weights, determine the priority of the scheme or make a final decision.

[0102] In one embodiment, if there are two regions A and B, it is necessary to determine the priorities of the disaster-bearing bodies (population, transportation, and buildings) at three times according to population stability, vehicle stability, and building stability at time T1 and time T2 respectively:

[0103] 1. Establish the hierarchical structure:

[0104] Objective layer: Regional stability assessment;

[0105] Criterion layer: Population stability / failure threshold (LP), vehicle stability / failure threshold (LV), building failure threshold (LB);

[0106] 2. Construct the judgment matrix: Judgment matrix of the criterion layer: In region A, at time T1 and time T2, construct two judgment matrices. According to the population stability / failure threshold, vehicle stability / failure threshold, and building failure threshold at the two times, experts respectively conduct pairwise comparisons on the importance of population, transportation, and buildings at the two times.

[0107] Within area B, at times T1 and T2, two judgment matrices are constructed. According to the population stability / failure threshold, vehicle stability / failure threshold, and building failure threshold at the two times, experts respectively make pairwise comparisons of the importance of the population, transportation, and buildings at the two times.

[0108] 3 Calculate the weights: Weights of the criterion layer: Calculate the weights of the population, transportation, and buildings on the goal at times T1 and T2 in areas A and B respectively.

[0109] Make a decision: Determine the priorities of the population, transportation, and buildings in areas A and B at times T1 and T2 according to the weights respectively.

[0110] In another calculation example, assume that the pairwise comparison results of the experts on the criterion layer are as follows:

[0111] According to the population stability / failure threshold, vehicle stability / failure threshold, and building failure threshold, the relative importance of the population and transportation at time T1 in area A is 3:1, the relative importance of the population and buildings is 2:3, and the relative importance of transportation and buildings is 1:2; then the weights of the criterion layer can be calculated as: weight of the population: 0.3; weight of transportation: 0.2; weight of buildings: 0.5. Similarly, the weights of the criterion layer at time T2 can be calculated as: weight of the population: 0.6; weight of transportation: 0.3; weight of buildings: 0.1.

[0112] The weights of the criterion layer at time T1 in area B can be calculated as: weight of the population: 0.2; weight of transportation: 0.5; weight of buildings: 0.3. Similarly, the weights of the criterion layer at time T2 can be calculated as: weight of the population: 0.1; weight of transportation: 0.6; weight of buildings: 0.3.

[0113] Final decision: In area A, at time T1, the building has the highest priority, followed by the population, and finally transportation; at time T2, the population has the highest priority, followed by transportation, and finally buildings. In area B, at both times T1 and T2, transportation has the highest priority, followed by buildings, and finally the population. In one embodiment, the steps of obtaining the instability threshold may specifically include the following steps:

[0114] The disaster-bearing bodies include: people, transportation, and buildings;

[0115] Based on various instability states corresponding to the influencing parameters of people, obtain the first instability threshold and the second instability threshold of people;

[0116] In one embodiment, the various instability states include: the first instability state, the second instability state, and the third instability state; the first instability state represents a stable state, the second instability state represents a critical instability state, and the third instability state represents a severe instability state;

[0117] The first instability threshold of a person is 0.15, and the second instability threshold is 0.40: LP < 0.15 (stable), 0.15 ≤ LP < 0.40 (critical instability), LP ≥ 0.40 (severe instability).

[0118] In one embodiment, the multiple instability states of the traffic corresponding influence parameters can be the multiple instability states of the vehicle corresponding influence parameters. Based on the multiple instability states of the vehicle corresponding influence parameters, the first instability threshold and the second instability threshold of the vehicle are obtained;

[0119] In one embodiment, the first instability threshold of the vehicle is 0.10, and the second instability threshold is 0.4: LV < 0.10 (normal), 0.10 ≤ LV < 0.4 (partial failure), LV ≥ 0.40 (complete failure).

[0120] In one embodiment, referring to Figure 4 as shown, a step flow chart for comprehensive risk calculation by the fuzzy matter-element method is shown. As Figure 4 shown, it may specifically include the following steps:

[0121] Step S401, calculate the single risk of each disaster-bearing body according to the distribution of disaster-bearing bodies and their influence parameters;

[0122] Step S402, use the single risk of each disaster-bearing body as the evaluation index of the fuzzy matter-element;

[0123] Step S403, construct multiple fuzzy matter-element matrices according to multiple evaluation indexes and multiple moments, and calculate the dynamic weight value of each disaster-bearing body according to the fuzzy matter-element matrix;

[0124] Step S404, perform comprehensive risk calculation according to the single risk of each disaster-bearing body and the dynamic weight value of each disaster-bearing body, and obtain the flood process moment-by-moment comprehensive risk result including all disaster-bearing bodies.

[0125] In one embodiment, the moment-by-moment comprehensive risk value of the flood process is calculated by the fuzzy matter-element method and the dynamic weight values of all disaster-bearing bodies.

[0126] 1: Calculate the single risk of each disaster-bearing body;

[0127] According to the building density and the moment-by-moment population and traffic density, combined with their respective moment-by-moment influence parameters, calculate the moment-by-moment single risks of the population (RP), traffic (RT), and buildings (RB);

[0128] 2: Define the evaluation index and its threshold;

[0129] Evaluation index: the moment-by-moment single risks of the population (RP), traffic (RT), and buildings (RB);

[0130] Each index has two respective thresholds, and each index is divided into three levels.

[0131] 3: Scoring and membership degree;

[0132] Evaluate these indicators for each risk indicator under its respective threshold to obtain the membership degree distribution.

[0133] 4: Establish a fuzzy matter-element matrix;

[0134] Construct a fuzzy matter-element matrix according to the membership degree. The fuzzy matter-element matrix is a table, where each row represents an evaluation object (a moment in a region), each column represents an evaluation indicator, and the value in each cell represents the membership degree of the object on that indicator.

[0135] In one embodiment, for example, we have three evaluation indicators: population single risk (RP), traffic single risk (RT), and building single risk (RB); and there are two regions n1 and n2; two time points (t1, t2), and the fuzzy matter-element matrix can be as shown in Table 1:

[0136] Table 1

[0137]

[0138] Among them, μ RP (n i , t i ), μ RT (n i , t i ), μ RB (n i , t i ) are membership degree values, usually ranging from [0, 1], indicating the membership degree of the indicator in region n i at time point t i .

[0139] 5. Determine the dynamic weight;

[0140] Combine the AHP method and the impact parameters of the disaster-bearing body to calculate the dynamic weight.

[0141] For example; assume that the weights of region n at time t determined by AHP and the impact parameters of the disaster-bearing body are: population single risk (RP): 0.4; traffic single risk (RT): 0.3; building single risk (RB): 0.3;

[0142] 6: Calculate the comprehensive risk;

[0143] According to the maximum membership degree of each indicator combined with the dynamic weight, the comprehensive risk (CR) can be calculated. The formula is as follows:

[0144] CR(n, t) = WPR S (n, t) × μ RP (n, t) + WTR S (n, t) × μ RT (n, t) + WBR S (n, t) × μ RB (n, t),

[0145] Among them, CR(n, t) is the comprehensive flood risk of area n at time t; RP(n, t), RT(n, t), and RB(n, t) are the single risks of population, traffic, and buildings in area n at time t; WPR(n, t), WTR(n, t), and WBR(n, t) are the dynamic weights of area n at time t; μRP(n, t), μRT(n, t), and μRB(n, t) are the membership degrees of area n at time t.

[0146] For example: Suppose the membership degrees at time point t1 in area n1 are: single risk of population: μ RP (n1, t1) = 0.8; single risk of traffic: μ RT (n1, t1) = 0.6 and single risk of buildings: μ RB (n1, t1) = 0.4; then the comprehensive risk of area n1 at time point t1 is:

[0147] CR(n1, t1) = 0.4 × 0.8 + 0.3 × 0.6 + 0.3 × 0.4 = 0.32 + 0.18 + 0.12 = 0.62;

[0148] Through the GIS visualization technology, the risk distribution at each moment is displayed on the map for intuitive analysis.

[0149] For the technical solution of the embodiment of the present application, the comprehensive risk of the flood process is evaluated based on the dynamic weight coupling fuzzy matter-element method, and compared with the static weight method, it can more accurately identify the risk area.

[0150] In one embodiment, the steps of calculating the influence parameters of the disaster-bearing body may specifically include the following steps:

[0151] According to the hydraulic parameters of the hydraulic conditions and the unit-width flow energy and momentum equations, at least one influence parameter of the disaster-bearing body is calculated, where the hydraulic parameters of the hydraulic conditions include: submergence depth and submergence velocity.

[0152] In one embodiment, the submergence process is simulated through a urban inundation model to obtain the submergence depth and submergence velocity under at least one hydraulic condition.

[0153] In one embodiment, the calculation of the lumped impact parameters includes: inputting the rainfall with a return period of 50 years into the urban inundation model to obtain the inundation water depth and the flow velocity process, and calculating the relationship between the lumped impact parameters of people, vehicles, and buildings and the inundation water depth and flow velocity, as Figure 5 shown, and the spatial distribution of the lumped impact parameters at different times, as Figure 6 shown.

[0154] In one embodiment, the risk assessment of population, transportation, and buildings includes:

[0155] obtaining the densities of buildings and roads from building and road data, as Figure 7 shown in FIGS. 7a and 7b, and obtaining the hourly population density from Baidu population heat data, as Figure 7 shown in FIG. 7c. Combining the respective lumped impact parameters to calculate the hourly risks of population, transportation, and buildings, as Figure 8 shown.

[0156] In one embodiment, the classification and assignment of dynamic weights include:

[0157] dividing the weight types according to the population stability threshold and the vehicle failure threshold, as Figure 9 shown in FIG. 8a, and determining the weight values of each type based on the AHP method, as Figure 9 shown in FIG. 8b.

[0158] In one embodiment, the dynamic assessment of comprehensive risk includes:

[0159] Given the hourly risks of people, transportation, and buildings in the southern part of Jinshui District and the distribution of their hourly dynamic weights, the comprehensive risk of the hourly flood process can be calculated, as Figure 9 shown. The research results show that at 7 am, there is almost no flood risk. By 8 am, the comprehensive risk increases rapidly, and the medium and high risks are relatively evenly distributed. Starting from 9 am, the medium and high risks gradually concentrate in areas A, B, and C. During the period from 10 am to 12 pm, the area affected by the comprehensive risk within the research area gradually shrinks.

[0160] To verify the superiority of the proposed method, the risk assessment results of this paper are compared and analyzed with the static weight method (the risk weights of population, transportation, and buildings are set to 0.55, 0.28, and 0.17 respectively). Figure 11 FIG. 9 shows the comprehensive risk of the flood evolution process evaluated based on the static weight method. The results show that there are significant differences in the comprehensive risk during the period from 9 am to 12 pm compared with the dynamic weight method. For in-depth comparison, the comprehensive risk in area D at 11 am is selected, as Figure 12 shown, for detailed comparative analysis. Figure 11Illustrates the dynamic weight categories in area D, including categories I, III, and IV. In area I, due to the extremely low damage levels of personnel, vehicles, and buildings, the weights of population risk, traffic risk, and building risk are nearly equal. Conversely, in areas III and IV, the vehicle passing capacity is severely affected, while the impacts on personnel and buildings are relatively small. Therefore, the dynamic weights of traffic risk are relatively high, being 0.68 and 0.59 respectively. The static weight method fails to adjust the weights according to the instability levels of personnel, vehicles, and buildings, resulting in an overestimation of the population risk weight (0.55). This phenomenon causes the comprehensive risk distribution based on static weights to be similar to the population risk distribution, as shown in Figure 12 Figures b and 12d, failing to truly reflect the actual situation. In contrast, the comprehensive risk assessment based on dynamic weights can effectively highlight the impact of floods on traffic, as shown in Figure 12 Figures a and 12e, and thus more accurately identify high-risk areas. In summary, the assessment method using dynamic weights can adjust the weights according to the dynamic characteristics of comprehensive impact parameters, thereby achieving a more accurate risk assessment result that is more consistent with the actual scenario.

[0161] Referring to Figure 13 shown, a structural block diagram of a risk assessment device 1300 for a flood process according to an embodiment of the present invention is shown. As shown in Figure 13 shown, the device may specifically include the following modules:

[0162] A hydraulic parameter acquisition module 1301, configured to acquire hydraulic parameters under at least one hydraulic condition;

[0163] An impact parameter calculation module 1302, configured to calculate impact parameters of at least one disaster-bearing body according to the hydraulic parameters of the hydraulic condition; wherein, one disaster-bearing body corresponds to one impact parameter;

[0164] A risk distribution module 1303, configured to obtain the risk distribution of each of the disaster-bearing bodies at any moment according to the impact parameter of each disaster-bearing body and the distribution density of the disaster-bearing body at any moment;

[0165] A dynamic weight calculation module 1304, configured to calculate the weight value of the disaster-bearing body according to the instability threshold of the impact parameter at any moment, and obtain the dynamic weight value of each disaster-bearing body;

[0166] A risk assessment module 1305, configured to perform an assessment according to the dynamic weight values of all disaster-bearing bodies and the risk distribution of all disaster-bearing bodies at any moment, and obtain a comprehensive risk result for each moment of the flood process including all disaster-bearing bodies.

[0167] In one embodiment, the dynamic weight calculation module 1304 is specifically configured to obtain an instability threshold corresponding to each instability state according to at least one instability state of the impact parameters of each disaster-bearing body.

[0168] Construct a judgment matrix for the criterion layer of the analytic hierarchy process according to the instability thresholds of all impact parameters.

[0169] Calculate the weight value of the disaster-bearing body according to the judgment matrix.

[0170] In one embodiment, the disaster-bearing bodies include: people, transportation, and buildings; the dynamic weight calculation module 1304 is specifically configured to obtain a first instability threshold and a second instability threshold of people based on multiple instability states of the impact parameters corresponding to people.

[0171] Obtain a first instability threshold and a second instability threshold of transportation based on multiple instability states of the impact parameters corresponding to transportation.

[0172] In one embodiment, the risk assessment module 1301 is specifically configured to calculate the single risk of each disaster-bearing body according to the distribution of disaster-bearing bodies and their impact parameters; use the single risk of each disaster-bearing body as an evaluation index of the fuzzy matter element; construct multiple fuzzy matter element matrices according to multiple evaluation indexes and multiple moments, calculate the dynamic weight value of each disaster-bearing body according to the fuzzy matter element matrix; perform comprehensive risk calculation according to the single risk of each disaster-bearing body and the dynamic weight value of each disaster-bearing body, and obtain the flood process moment-by-moment comprehensive risk result including all disaster-bearing bodies.

[0173] In one embodiment, the hydraulic parameter acquisition module 1301 is specifically configured to calculate the impact parameters of at least one disaster-bearing body according to the hydraulic parameters of the hydraulic conditions in combination with the unit-width flow energy and momentum equations, where the hydraulic parameters of the hydraulic conditions include: submergence depth and submergence velocity.

[0174] In one embodiment, it further includes: a simulation module for simulating the inundation process through a urban inundation model to obtain the submergence depth and submergence velocity under at least one hydraulic condition.

[0175] The risk assessment device for a flood process provided in the above embodiment can implement the technical solutions described in the above embodiment of the risk assessment method for a flood process. The specific implementation principles of the above modules or units can be referred to the corresponding content in the above embodiment of the risk assessment method for a flood process, which will not be elaborated here.

[0176] Based on the same inventive concept, the present invention also correspondingly provides an electronic device 1400, as Figure 14 shown. The electronic device 1400 includes a processor 1401, a memory 1402, and a display 1403. Figure 14Only some components of the electronic device 1400 are shown, but it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively.

[0177] In some embodiments, the memory 1402 may be an internal storage unit of the electronic device 1400, such as the hard disk or memory of the electronic device 1400. In other embodiments, the memory 1402 may also be an external storage device of the electronic device 1400, such as a plug-in hard disk equipped on the electronic device 1400, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc.

[0178] Furthermore, the memory 1402 may also include both the internal storage unit of the electronic device 1400 and an external storage device. The memory 1402 is used to store the application software installed in the electronic device 1400 and various types of data.

[0179] In some embodiments, the processor 1401 may be a central processing unit (CPU), a microprocessor or other data processing chips, and is used to run the program code stored in the memory 1402 or process data, such as a risk assessment method for a flood process in the present invention.

[0180] In some embodiments, the display 1403 may be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (Organic Light-Emitting Diode) toucher, etc. The display 1403 is used to display the information in the electronic device 1400 and to display a visual user interface. The components 1401 - 1403 of the electronic device 1400 communicate with each other through a system bus.

[0181] In some embodiments of the present invention, when the processor 1401 executes the risk assessment program for the flood process in the memory 1402, the following steps can be implemented:

[0182] Obtain hydraulic parameters under at least one hydraulic condition; calculate the influence parameters of at least one disaster-bearing body according to the hydraulic parameters of the hydraulic condition, where one disaster-bearing body corresponds to one influence parameter; obtain the risk distribution of each disaster-bearing body at any moment according to the influence parameter of each disaster-bearing body and the distribution density of the disaster-bearing body at any moment; calculate the weight value of the disaster-bearing body according to the instability threshold of the influence parameter at any moment to obtain the dynamic weight value of each disaster-bearing body; evaluate according to the dynamic weight values of all disaster-bearing bodies and the risk distribution of all disaster-bearing bodies at any moment to obtain the comprehensive risk result of each moment of the flood process including all disaster-bearing bodies.

[0183] It should be understood that when the processor 1401 executes the risk assessment program of the flood process in the memory 1402, in addition to the above functions, other functions can also be realized. For details, please refer to the description of the corresponding method embodiments above.

[0184] Furthermore, the type of the electronic device 1400 mentioned in the embodiments of the present invention is not specifically limited. The electronic device 1400 can be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, etc. Exemplary embodiments of the portable electronic device include, but are not limited to, portable electronic devices running IOS, android, microsoft or other operating systems. The above portable electronic devices can also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (such as a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 1400 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (such as a touch panel).

[0185] Based on the same inventive concept, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it is configured to execute the risk assessment method of the flood process provided by the above methods. The method includes:

[0186] Obtain hydraulic parameters under at least one hydraulic condition; calculate the influence parameters of at least one disaster-bearing body according to the hydraulic parameters of the hydraulic condition, wherein one disaster-bearing body corresponds to one influence parameter; obtain the risk distribution of each disaster-bearing body at any moment according to the influence parameter of each disaster-bearing body and the distribution density of the disaster-bearing body at any moment; at any moment, calculate the weight value of the disaster-bearing body according to the instability threshold of the influence parameter to obtain the dynamic weight value of each disaster-bearing body; evaluate according to the dynamic weight values of all disaster-bearing bodies and the risk distribution of all disaster-bearing bodies at any moment to obtain the comprehensive risk result of each moment of the flood process including all disaster-bearing bodies.

[0187] Those skilled in the art can understand that all or part of the processes of implementing the above embodiment methods can be completed by instructing relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. Among them, the computer-readable storage medium is a disk, an optical disk, a read-only memory or a random access memory, etc.

[0188] The above has introduced in detail a risk assessment method, device, equipment, medium and product for a flood process. Specific examples are used in this article to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those skilled in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A risk assessment method for flood processes, characterized in that Including: Obtain hydraulic parameters under at least one hydraulic condition; Calculate the influence parameters of at least one disaster-bearing body according to the hydraulic parameters of the hydraulic condition; wherein, one disaster-bearing body corresponds to one influence parameter; Obtain the risk distribution of each disaster-bearing body at any moment according to the influence parameter of each disaster-bearing body and the distribution density of the disaster-bearing body at any moment; At any moment, calculate the weight value of the disaster-bearing body according to the instability threshold of the influence parameter, and obtain the dynamic weight value of each disaster-bearing body; Evaluate according to the dynamic weight values of all disaster-bearing bodies and the risk distribution of all disaster-bearing bodies at any moment, and obtain the comprehensive risk results of the flood process for each moment including all disaster-bearing bodies.

2. The risk assessment method for a flood process according to claim 1, wherein The calculating the weight value of the disaster-bearing body according to the instability threshold of the influence parameter specifically includes: Obtain the instability threshold corresponding to each instability state according to at least one instability state of the influence parameter of each disaster-bearing body; Construct a judgment matrix for the criterion layer of the analytic hierarchy process according to the instability thresholds of all influence parameters; Calculate the weight value of the disaster-bearing body according to the judgment matrix.

3. The risk assessment method for a flood process according to claim 2, wherein The disaster-bearing bodies include: people, transportation, and buildings; The obtaining the instability threshold corresponding to each instability state according to at least one instability state of the influence parameter of each disaster-bearing body specifically includes: Based on multiple instability states of the influence parameter corresponding to people, obtain the first instability threshold and the second instability threshold of people; Based on multiple instability states of the influence parameter corresponding to transportation, obtain the first instability threshold and the second instability threshold of transportation.

4. A risk assessment method for a flood process according to any one of claims 1-3, characterized in that, The evaluating according to the dynamic weight values of all disaster-bearing bodies and the risk distribution of all disaster-bearing bodies at any moment, and obtaining the comprehensive risk results of the flood process for each moment including all disaster-bearing bodies specifically includes: Calculate the risk information of each disaster-bearing body according to the risk distribution of all disaster-bearing bodies at any moment and the corresponding influence parameters; Take the risk information of each disaster-bearing body as the evaluation index of the fuzzy matter-element; Construct multiple fuzzy matter-element matrices according to multiple evaluation indexes and multiple moments, and calculate the dynamic weight value of each disaster-bearing body according to the fuzzy matter-element matrix; Conduct comprehensive risk calculation according to the risk information of each disaster-bearing body and the dynamic weight value of each disaster-bearing body, and obtain the comprehensive risk results of the flood process for each moment including all disaster-bearing bodies.

5. The risk assessment method for a flood process according to claim 1, wherein The calculating the influence parameters of at least one disaster-bearing body according to the hydraulic parameters of the hydraulic condition specifically includes: Calculate the influence parameters of at least one disaster-bearing body according to the hydraulic parameters of the hydraulic condition in combination with the unit-width flow energy and momentum equations, wherein the hydraulic parameters of the hydraulic condition include: submergence depth and submergence velocity.

6. The risk assessment method for a flood process according to claim 1 or 5, characterized in that Also including: Simulate the inundation process through the urban inundation model to obtain the submergence depth and submergence velocity under at least one hydraulic condition.

7. A risk assessment device for a flood process, characterized in that, Including: A hydraulic parameter acquisition module for obtaining hydraulic parameters under at least one hydraulic condition; An influence parameter calculation module for calculating the influence parameters of at least one disaster-bearing body according to the hydraulic parameters of the hydraulic condition; wherein, one disaster-bearing body corresponds to one influence parameter; A risk distribution module, configured to obtain the risk distribution of each of the disaster-bearing bodies at any moment according to the influence parameters of each of the disaster-bearing bodies and the distribution density of the disaster-bearing bodies at any moment; A dynamic weight calculation module, configured to calculate the weight value of the disaster-bearing body according to the instability threshold of the influence parameter at any moment, so as to obtain the dynamic weight value of each of the disaster-bearing bodies; A risk assessment module, configured to evaluate according to the dynamic weight values of all the disaster-bearing bodies and the risk distribution of all the disaster-bearing bodies at any moment, so as to obtain the comprehensive risk results of each moment of the flood process including all the disaster-bearing bodies.

8. An electronic device, characterized in that, Comprising a memory and a processor, wherein, The memory is configured to store a program; The processor is coupled to the memory and configured to execute the program stored in the memory to implement the steps in the risk assessment method for a flood process according to any one of claims 1 to 6 above.

9. A computer-readable storage medium, characterized in that, For storing a computer-readable program or instruction, and when the program or instruction is executed by a processor, it can implement the steps in the risk assessment method for a flood process according to any one of claims 1 to 6 above.

10. A computer program product, comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by a processor, it implements the steps in the risk assessment method for a flood process according to any one of claims 1-6.

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