A risk assessment and analysis method for excessive rainstorms in plain water network areas

Abstract

The present invention belongs to the technical field of flood risk assessment, specifically a method for risk assessment and analysis of excessive rainstorms in plain water network areas, comprising the following steps: a first step: data collection, obtaining comprehensive data of the area to be assessed, the comprehensive data including: topography, hydraulic pipe network, hydrological water collection, station gates, river network and river channels, embankments, past rainfall and humanities information; a second step: module coupling: building a comprehensive data module for the area to be assessed based on the obtained comprehensive data, and constructing a three-dimensional model of the area to be assessed through the comprehensive data module; a third step: model verification; a fourth step: rainfall simulation; a fifth step: result output; by establishing multiple data modules, coupling the multiple data modules into a regional model, and simulating rainfall through a rainfall module, real-time calculation of the occurrence of rainstorms in the assessment area is achieved, which is more in line with the actual situation of the assessment area, thereby achieving the effect of improving the accuracy of risk assessment.

Description

Technical Field

[0001] The present invention belongs to the technical field of urban flood risk status assessment, and specifically provides an excessive rainstorm risk assessment and analysis method in plain water network areas. Background Art

[0002] With the continuous intensification of global climate change and the rapid development of urbanization, extreme rainstorms occur frequently with huge rainfall in a short period of time. Whether in cities or rural areas, if timely dredging and drainage cannot be met, rainstorms will harm the growth of crops, disrupt normal agricultural production and the normal development of other industries, and even endanger human life, health and safety.

[0003] Currently, existing technologies have also proposed some solutions for rainstorm risk prediction. For example, the Chinese patent announcement CN113723824B discloses a rainstorm disaster risk assessment method, which evaluates the disaster-causing capacity of rainstorms by selecting four evaluation indicators: precipitation, precipitation intensity, precipitation distribution, and precipitation duration.

[0004] In the above technical solution, judging the disaster-causing capacity of heavy rain by four indicators, namely precipitation amount, precipitation intensity, precipitation distribution and precipitation duration, is not rigorous and does not take into account the actual topography and water conservancy distribution of the region, so the prediction results are relatively general.

[0005] To this end, the present invention provides a method for risk assessment and analysis of excessive rainstorms in plain water network areas. Summary of the Invention

[0006] In order to make up for the deficiencies of the prior art, at least one technical problem raised in the background technology is solved.

[0007] The technical solution adopted by the present invention to solve the technical problem is: a method for risk assessment and analysis of excessive rainstorms in plain water network areas, comprising the following steps:

[0008] S1. Data Collection: Obtain comprehensive data on the area to be assessed, including topography, hydraulic pipe network, hydrological water collection, station locks, river network and channel, levees, past rainfall, and humanities information;

[0009] S2. Module coupling: Based on the acquired comprehensive data, a comprehensive data module for the area to be assessed is constructed, and a three-dimensional model of the area to be assessed is constructed through the comprehensive data module;

[0010] S3, model verification: Verify the accuracy of the 3D model of the area to be evaluated. If the verification is qualified, proceed to the next step. If the verification is unqualified, it indicates that there are abnormal data in the comprehensive data collected in step S1. After finding and correcting the abnormal data, return to step S2 to optimize the comprehensive data module;

[0011] S4. Simulating rainfall: Access future meteorological data and risk avoidance data of the area to be assessed, and simulate future rainfall on a three-dimensional model of the area to be assessed based on the future meteorological data;

[0012] S5. Result output: By simulating future rainfall, when there is heavy rain, the three-dimensional model of the area to be assessed will simulate whether there is waterlogging based on the drainage functions of different areas, and output the heavy rain risk database of the area to be assessed based on the regional situation of waterlogging in the area to be assessed.

[0013] Furthermore, the specific method for model verification described in step S3 is as follows:

[0014] Step 1: Obtain historical data on past rainstorm disasters in the area to be assessed;

[0015] Step 1: Put the rainfall data when the rainstorm disaster occurs into the three-dimensional model for simulation;

[0016] Step 3: Compare the historical disaster characteristics with the three-dimensional model simulation; if the comparison results are consistent, the verification is qualified; if the comparison results are inconsistent, the verification is unqualified.

[0017] Furthermore, the risk avoidance data in step S4 includes the real-time water level of the catchment area, which is obtained by installing a water level monitoring device inside the catchment area and feeding back the water level data in real time through wireless communication technology.

[0018] Furthermore, the risk avoidance data in step S4 also includes the regional pipe network abnormality rate F i , the specific method of obtaining it is:

[0019] The area to be assessed is divided into n regions, and different regions are numbered as {1, 2, 3...i...n}. The number of past rainstorm disasters in different regions is obtained as H, and the total number of pipeline networks built in the region during past rainstorm disasters is obtained as The total number of abnormal pipe networks in the area during the same rainstorm disaster is set as , then the regional pipe network abnormality rate F in different areas i It can be calculated by the following formula:

[0020] *100%;

[0021] When simulating future rainfall on the three-dimensional model of the area to be assessed, turn off the F i Proportional pipe network.

[0022] Furthermore, the total number of abnormal pipe networks in the area to be assessed during heavy rain disasters The acquisition process is as follows:

[0023] Step 1: Obtain the total number of abnormal pipe networks in the area to be assessed during the same rainstorm disaster and the disaster season, and build a four-season abnormal pipe network data model for the area to be assessed based on the SARIMA model;

[0024] Step 2: Get the time when the last rainstorm disaster occurred in the area to be assessed. If the time interval between the last rainstorm disaster in the area to be assessed and the predicted rainstorm is less than half a month, then output If it is equal to 0, then if the time interval between the last rainstorm disaster in the area to be assessed and the predicted rainstorm is greater than half a month, then proceed to the next step;

[0025] Step 3: Determine the predicted rainstorm season and input it into the four-season abnormal pipe network data model of the area to be assessed. The four-season abnormal pipe network data model of the area to be assessed will output the total number of abnormal pipe networks when the rainstorm causes disasters in the current season. .

[0026] Furthermore, the specific output method of the rainstorm risk database of the area to be assessed in step S5 is as follows:

[0027] Step 1: Determine whether the cause of waterlogging in area i is due to the catchment area or the pipe network;

[0028] Step 2: Output the severity level N of waterlogging based on whether the waterlogging in area i is caused by the catchment area or the pipe network. i or M i ;

[0029] Step 3: Obtain the GDP proportion G of the waterlogged area i in the area to be assessed i , and the proportion of the population of the waterlogged area i in the area to be assessed P i ;

[0030] Step 4: Integrate the risk data of all flooded areas i, including: G i 、P i and N i or M i , and make a database output.

[0031] Furthermore, the process of determining the cause of waterlogging in waterlogged area i is as follows:

[0032] The total future rainfall in the flooded area i is obtained by using future meteorological data and recorded as J all The current maximum water volume of the catchment area is recorded as V all , calculate J all With V all If the ratio is less than 1, it means that the area is not flooded due to water catchment problems. all With V allThe ratio is greater than 1. According to its specific value, the severity level N of the waterlogging area i is given. i , N i Includes: low risk, medium risk, high risk, or very high risk.

[0033] Furthermore, when J all With V all When the ratio is less than 1, the future rainfall duration of the waterlogged area i is divided into {2, 3...a} equal time periods according to the future meteorological data, and then the future rainfall D in a single time period of the waterlogged area i is calculated. all , then combined with the regional pipeline network abnormality rate F i Calculate the maximum discharge volume L in a single time period of the flooded area i all , calculate D all With L all The ratio of D is greater than 1, indicating that area i will experience waterlogging due to insufficient pipe network capacity during this period. all With L all The severity of waterlogging in waterlogged area i is determined by the ratio of and the duration of waterlogging.

[0034] Further, obtain D all With L all The duration of the period when the ratio is greater than 1 is recorded as T a , get D all With L all The duration of the period when the ratio is greater than 1 is b, and the area of ​​the flooded area i is recorded as S i， The ideal water depth per square meter in the flooded area i is Z i It can be calculated by the following formula:

[0035] *100

[0036] According to the ideal water depth Z i The specific value of gives the severity level M of waterlogging in waterlogged area i. i , M i Includes: low risk, medium risk, high risk, or very high risk.

[0037] Furthermore, the rainstorm risk database described in step S5 also includes rainstorm prevention recommendations, the specific analysis process of which is as follows:

[0038] When the waterlogging severity level N output by waterlogging area i i When the risk is low or medium, determine whether the current water storage capacity of the catchment area i is greater than J all With V all If the difference is greater than , the output suggestion is: empty the catchment area;

[0039] If the current water storage capacity of the catchment area i is greater than J all With V all The difference between the values ​​of , or the waterlogging severity level N output by waterlogged area i i When the risk is high or very high, the output recommendation is: expand the maximum water catchment capacity of the flooded area i;

[0040] When the waterlogging severity level M output by waterlogging area i i When the risk is low or medium, judge F i When it is equal to zero, J all With V all Is the ratio less than 1? If so, output the suggestion: check the abnormality of the pipe network and ensure that the pipe network is unobstructed;

[0041] If F i When it is equal to zero, J all With V all The ratio is still greater than 1, or the waterlogging severity level M output by waterlogging area i i When the risk is high or extremely high, the output suggestion is: expand the number of pipelines in the flooded area i.

[0042] The beneficial effects of the present invention are as follows:

[0043] 1. The present invention provides a method for risk assessment and analysis of excessive rainstorms in plain water network areas. By establishing multiple data modules, coupling the multiple data modules into a regional model, and simulating rainfall through a rainfall module, real-time calculation of rainstorms occurring in the assessment area is achieved, which is more in line with the actual situation in the assessment area, thereby achieving the effect of improving the accuracy of risk assessment.

[0044] 2. The method for risk assessment and analysis of excessive rainstorms in plain water network areas described in the present invention analyzes the causes of waterlogging in waterlogged areas, thereby distinguishing different types of waterlogging situations, effectively assisting personnel in further analyzing and preventing waterlogging situations, and reducing losses caused by rainstorm disasters in the areas to be assessed. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The present invention will be further described below with reference to the accompanying drawings.

[0046] Figure 1 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0047] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.

[0048] like Figure 1As shown, the method for risk assessment and analysis of excessive rainstorms in plain water network areas according to an embodiment of the present invention includes the following steps:

[0049] S1. Data Collection: Obtain comprehensive data on the area to be assessed, including topography, hydraulic pipe network, hydrological water collection, station locks, river network and channel, levees, past rainfall, and humanities information;

[0050] S2. Module coupling: Based on the acquired comprehensive data, a comprehensive data module for the area to be assessed is constructed, and a three-dimensional model of the area to be assessed is constructed through the comprehensive data module;

[0051] S3, model verification: Verify the accuracy of the 3D model of the area to be evaluated. If the verification is qualified, proceed to the next step. If the verification is unqualified, it indicates that there are abnormal data in the comprehensive data collected in step S1. After finding and correcting the abnormal data, return to step S2 to optimize the comprehensive data module;

[0052] S4. Simulating rainfall: Access future meteorological data and risk avoidance data of the area to be assessed, and simulate future rainfall on a three-dimensional model of the area to be assessed based on the future meteorological data;

[0053] S5. Result output: By simulating future rainfall, when there is heavy rain, the three-dimensional model of the area to be assessed will simulate whether there is waterlogging based on the drainage functions of different areas, and output the heavy rain risk database of the area to be assessed based on the regional situation of waterlogging in the area to be assessed.

[0054] Based on the acquired comprehensive data, a comprehensive data module is constructed, mainly including: building a terrain module based on topographic and geomorphological data, building a hydraulic module based on hydraulic pipe network data, building a hydrological module based on hydrological and water collection data, building a station lock module based on station lock data, building a river network module based on river network and river channel data, building a levee module based on levee data, and building a humanities module based on humanities information data;

[0055] A terrain module configured to simulate landform features of an area to be assessed; the landform features are formed by a combination of elevation point data;

[0056] A hydraulic module is configured to use pipeline network survey data for the area to be assessed, input node attributes, and pipeline attributes; the input node attributes include pipeline network type, node coordinates, manhole bottom elevation, and manhole ground elevation; pipeline attributes include pipeline shape, length, height and width, roughness, and pipe bottom elevation; establish a spatial topological relationship between the upstream and downstream node numbers of the pipeline, simulate the dynamics of water bodies within the drainage network, and perform hydrodynamic calculations of water flow within the pipeline network based on the one-dimensional Saint-Venant equations;

[0057] A hydrological module configured to feedback water collection characteristics of a watershed in a region to be assessed; the water collection characteristics include watershed area, runoff parameters, and runoff coefficient;

[0058] A station gate module is configured to simulate basic information about the station gates in the area to be assessed. This information includes pump station drainage flow, gate top height, gate bottom height, number of gate openings, gate opening width, and station gate coordinate information. By inputting control and scheduling parameters for the station gate, the module simulates properties such as gate opening and closing water levels and pump station opening and closing process curves.

[0059] A river network module is configured to simulate river parameters in the area to be assessed; basic attributes of the river network, topological connection relationships, and river confluence directions are determined through river centerlines, cross-section lines, and riverbanks, thereby simulating a gridded river system;

[0060] A levee module is configured to simulate levee parameters in the area to be assessed; by simulating complex hydraulic processes such as levee leakage and breach through key parameters such as levee location, crest height, and width;

[0061] a water level module configured to simulate water flow movement and water level changes in a river in an area to be assessed under a rainfall scenario;

[0062] The humanities module is configured to simulate and evaluate the humanities information of a region; the humanities information includes regional population density, regional GDP per capita, and the proportion of cultivated land in the region.

[0063] The specific steps for constructing a three-dimensional model of the area to be assessed using comprehensive data are as follows:

[0064] First, the hydraulic module, hydrological module, and river network module are coupled to construct a one-dimensional analysis, converting rainfall into surface runoff, and then the surface runoff flows into the hydraulic module for hydraulic calculation to form a one-dimensional model;

[0065] Then the one-dimensional model is coupled with the terrain module, embankment module, station gate module, and water level module to form a three-dimensional model;

[0066] In the module coupling process, the established hydraulic module is used to divide the sub-catchment area. The land use type, runoff generation and confluence mode, runoff surface type, and related runoff coefficients are defined in the sub-catchment area attribute table, thereby coupling the hydrological module with the hydraulic module. The coupled model can perform pipe network drainage capacity analysis and node water level analysis: the hydrological module converts rainfall into surface runoff and enters the hydraulic module. The hydraulic module transmits the water flow using hydraulic calculation formulas. If the water flow exceeds the pipe network drainage capacity, it overflows from the node.

[0067] The terrain module extracts data on pipeline and runoff surface elevation points from the hydraulic and hydrological modules and couples it with a one-dimensional model. It also selects either a one-dimensional or two-dimensional runoff model based on the different settings of the hydrological module's attribute information. The hydraulic and hydrological modules can simulate two-dimensional flooding. When the incoming water in the pipe network exceeds its discharge capacity, water overflows from the nodes into the terrain module, forming two-dimensional water accumulation based on the terrain, allowing for waterlogging risk analysis.

[0068] The river network module is combined with the hydraulic and hydrological modules through flap gates. The water in the pipe network flows into the river in one direction through the flap gates. After coupling the river network module with the hydraulic and hydrological modules, the water flows into the river in one direction. The rising water level in the river creates a supporting effect on the pipe network connected to the outlet, causing the water level in the pipe network to rise, increasing drainage difficulties. When the water level in the river reaches its highest point, the pipe network cannot continue to drain water into the river.

[0069] The station and lock module is usually represented by several independent or interconnected station and lock structures. The structures usually exist in the model in the form of connections. The topological relationship is established by determining the coordinates of the upstream and downstream connection points. The station and lock module can be connected to the hydraulic module or the river network module, or to the hydraulic module and the river network module at the same time.

[0070] The levee module is combined with the river network module. The water in the river channel inside the levee area and the river channel outside the levee area interact with each other through the levee. When the water level in the river channel inside the levee is higher than that in the river channel outside the levee, the water in the river channel inside the levee overflows and discharges to the river channel outside the levee. When the water level in the river channel outside the levee is higher than that in the river channel inside the levee, the water level in the river channel outside the levee supports the water in the river channel inside the levee, causing the water level in the river channel inside the levee to rise and making discharge difficult.

[0071] The water level module is combined with the embankment module. By inputting the historical measured most unfavorable water level or the designed excessive rainfall water level data, the water level module data is converted into surface runoff and entered into the hydraulic module or river network module.

[0072] When the hydraulic module is coupled with the river network module, the end of the hydraulic module's pipe network is connected to the river channel of the river network module. During the simulation, precipitation flows into the river channel through the end of the pipe network. The rising water level in the river channel will have a supporting effect on the water flow inside the pipe network, affecting the water discharge of the pipe network. When the river channel water level rises to the highest water level, the pipe network outlet can no longer drain water, and overflow occurs at the node. The water flow forms two-dimensional water accumulation on the two-dimensional terrain module, improving the realism of the simulation.

[0073] Furthermore, the specific method for model verification described in step S3 is as follows:

[0074] Step 1: Obtain historical data on past rainstorm disasters in the area to be assessed;

[0075] Step 1: Put the rainfall data when the rainstorm disaster occurs into the three-dimensional model for simulation;

[0076] Step 3: Compare the historical disaster characteristics with the three-dimensional model simulation; if the comparison results are consistent, the verification is qualified; if the comparison results are inconsistent, the verification is unqualified.

[0077] When using the established model for simulation, its accuracy needs to be verified in advance. If there are problems with the model, resulting in the risk of heavy rain in some areas without early warning, it is very likely to cause significant losses. This application uses historical heavy rain disaster data from the area to be assessed, simulates the model, and compares the simulation results with the actual disaster situation to judge the accuracy of the model, thereby improving the accuracy of the model when simulating heavy rain.

[0078] It should be noted that when verifying the regional model, the actual pipeline network construction and catchment area planning need to be considered. When verifying the rainstorm disaster situation in different periods, the pipeline system and catchment area that have not been established in the area at that time should be shielded to ensure the accuracy of the verification.

[0079] Furthermore, the risk avoidance data in step S4 includes the real-time water level of the catchment area, which is obtained by installing a water level monitoring device inside the catchment area and feeding back the water level data in real time through wireless communication technology.

[0080] Before a rainstorm arrives, the factors that cause rainstorm disasters are not just future rainfall. The current existing technology is too one-sided to judge the risk of rainstorm disasters only by rainfall. In fact, there are situations where the rainfall in the area is not large but still causes waterlogging. One of the reasons for this is that the water level in the catchment area of ​​the area is too high. The high water level in the catchment area will result in the catchment area being unable to store too much rainwater when a rainstorm comes, which will lead to the watershed being breached and causing waterlogging. This application monitors the water level in the catchment area in real time, thereby correcting the model, and can calculate the storage situation of the catchment area more in line with reality, which helps to improve the accuracy of rainstorm risk assessment.

[0081] Furthermore, the risk avoidance data in step S4 also includes the regional pipe network abnormality rate F i , the specific method of obtaining it is:

[0082] The area to be assessed is divided into n regions, and different regions are numbered as {1, 2, 3...i...n}. The number of past rainstorm disasters in different regions is obtained as H, and the total number of pipeline networks built in the region during past rainstorm disasters is obtained as The total number of abnormal pipe networks in the area during the same rainstorm disaster is set as , then the regional pipe network abnormality rate F in different areas i It can be calculated by the following formula:

[0083] *100%;

[0084] When simulating future rainfall on the three-dimensional model of the area to be assessed, turn off the F i Proportional pipe network.

[0085] In real life, there will be more or less abnormalities in the pipeline network, such as abnormal blockage and damage. These situations are emergencies and cannot be effectively predicted. When simulating rainstorm risks, existing technologies often assume that the regional pipeline system is normal, which is bound to be inconsistent with the actual situation, resulting in inaccurate assessment results. By calculating the regional pipeline network abnormality rate F in different areas, the regional pipeline network abnormality rate F is calculated. i , according to the regional pipeline network abnormality rate F i To simulate future abnormal conditions, the F i The number of pipe networks in proportion can improve the accuracy of rainfall simulation to a certain extent; it should be noted that the total number of pipe networks during previous rainstorm disasters R i It is necessary for personnel to collect data simultaneously when collecting data in step S1; specifically, after each rainstorm, there must be staff from the water conservancy network department to dredge the network and record it in the book. R can be obtained through relevant records. i Specific value of

[0086] Wherein, t is any positive integer from 1 to H, represents the total number of pipe networks in the area when the tth rainstorm caused the disaster, represents the total number of abnormal pipe networks in the area during the t-th rainstorm disaster; i is any positive integer from 1 to n, F i Indicates the regional pipe network anomaly rate of the i-th region. When simulating rainfall through the three-dimensional model of the area to be evaluated, F should be closed. i The proportional number of pipe networks can be increased to improve the accuracy of the simulation.

[0087] Furthermore, the total number of abnormal pipe networks in the area to be assessed during heavy rain disasters The acquisition process is as follows:

[0088] Step 1: Obtain the total number of abnormal pipe networks in the area to be assessed during the same rainstorm disaster and the disaster season, and build a four-season abnormal pipe network data model for the area to be assessed based on the SARIMA model;

[0089] Step 2: Get the time when the last rainstorm disaster occurred in the area to be assessed. If the time interval between the last rainstorm disaster in the area to be assessed and the predicted rainstorm is less than half a month, then output If it is equal to 0, then if the time interval between the last rainstorm disaster in the area to be assessed and the predicted rainstorm is greater than half a month, then proceed to the next step;

[0090] Step 3: Determine the predicted rainstorm season and input it into the four-season abnormal pipe network data model of the area to be assessed. The four-season abnormal pipe network data model of the area to be assessed will output the total number of abnormal pipe networks when the rainstorm causes disasters in the current season. .

[0091] Specifically, the abnormal pipe network data model for the four seasons in the area to be assessed obtains the number of historical abnormal pipe networks, disaster seasons and disaster times in the area to be assessed during past rainstorm disasters, and divides them by season. Output the results; It should be noted that when the interval between two rainstorms is short, it is assumed that the pipe network system has been completely cleared and prevented from clogging after the last rainstorm, so the output =0;

[0092] When a rainstorm disaster occurs, the abnormality rate of the pipe network in different seasons is also different. This is because heavy rain is often accompanied by strong winds. In spring, the branches and leaves on the trees have not yet fully grown, and in winter, the fallen leaves on the trees have basically fallen off. This makes the road surface in the area to be assessed often clean and leafless, so the probability of foreign objects clogging the pipe network during the drainage process is low; in summer or autumn, there are often more leaves on the trees, especially in autumn, when leaves fall off more easily. At this time, the pipe network is often prone to accumulation of more leaves, causing blockage, resulting in a higher abnormality rate of the pipe network. This application distinguishes the F in different seasons by differentiating the seasons. i Evaluate so that F i The numerical values ​​are more accurate and closer to reality, which improves the simulation effect of the model and is of great significance for accurately issuing rainstorm disaster warnings and making scientific decisions on disaster prevention, mitigation and relief.

[0093] Furthermore, the specific output method of the rainstorm risk database of the area to be assessed in step S5 is as follows:

[0094] Step 1: Determine whether the cause of waterlogging in area i is due to the catchment area or the pipe network;

[0095] Step 2: Output the severity level N of waterlogging based on whether the waterlogging in area i is caused by the catchment area or the pipe network. i or M i ;

[0096] Step 3: Obtain the GDP proportion G of the waterlogged area i in the area to be assessed i , and the proportion of the population of the waterlogged area i in the area to be assessed P i ;

[0097] Step 4: Integrate the risk data of all flooded areas i, including: Gi 、P i and N i or M i , and make a database output.

[0098] Current technologies for assessing rainstorm risk often simply use the total rainfall to determine whether a disaster will occur. However, when a rainstorm actually occurs, if the rainfall per unit time in a certain area exceeds the capacity of the pipe network, waterlogging will inevitably occur in that area.

[0099] This application analyzes the causes of waterlogging in waterlogged area i, thereby distinguishing different types of waterlogging, effectively assisting personnel in further analyzing and preventing waterlogging, and at the same time, through the form of a database, combined with the GDP proportion of waterlogged area i in the area to be assessed, G i , and the proportion of the population of the waterlogged area i in the area to be assessed P i When it is necessary to prevent flooding in area i, personnel can combine relevant G i With P i The data can be used to tilt prevention resources and reduce the losses caused by heavy rain disasters in the assessed areas.

[0100] Furthermore, the process of determining the cause of waterlogging in waterlogged area i is as follows:

[0101] The total future rainfall in the flooded area i is obtained by using future meteorological data and recorded as J all The current maximum water volume of the catchment area is recorded as V all (The current water storage capacity of the catchment area is estimated by the real-time water level of the catchment area, and the current maximum water storage capacity of the catchment area is calculated by subtracting the current water storage capacity of the catchment area from the maximum water collection capacity of the catchment area. all ), calculate J all With V all If the ratio is less than 1, it means that the area is not flooded due to water catchment problems. all With V all The ratio is greater than 1. According to its specific value, the severity level N of the waterlogging area i is given. i , N i Includes: low risk, medium risk, high risk, or very high risk.

[0102] When assessing the risk of heavy rain using future meteorological data, existing technologies generally do not take into account the actual water volume in the catchment area, which can cause errors in the assessment process. That is, there may be a situation where waterlogging occurs in a region when there is no risk, resulting in the region not booking a treatment plan in advance, causing property losses. However, the real-time water level of the catchment area collected in S4 can accurately calculate the actual water volume of the flooded area i. By comparing the future rainfall in the area, the heavy rain risk can be effectively estimated, thereby improving the subsequent assessment of the severity of waterlogging N. i Accuracy of judgment;

[0103] Specifically, when J all With V all When the ratio is greater than 1.0 and less than 1.1, output N i is low risk; when J all With V all When the ratio is greater than 1.1 and less than 1.3, output N i For medium risk; when J all With V all When the ratio is greater than 1.3 and less than 1.5, output N i For high risk; when J all With V all When the ratio is greater than 1.5, output N i For extremely high risks, by quantifying the water catchment volume of the waterlogged area i and providing numerical feedback on the overall situation of the region, regional management can make corresponding rainstorm risk prevention measures based on the actual situation in the region.

[0104] Furthermore, when J all With V all When the ratio is less than 1, the future rainfall duration of the waterlogged area i is divided into {2, 3...a} equal time periods according to the future meteorological data, and then the future rainfall D in a single time period of the waterlogged area i is calculated. all , then combined with the regional pipeline network abnormality rate F i Calculate the maximum discharge volume L in a single time period of the flooded area i all , calculate D all With L all The ratio of D is greater than 1, indicating that area i will experience waterlogging due to insufficient pipe network capacity during this period. all With L all The severity of waterlogging in waterlogged area i is determined by the ratio of and the duration of waterlogging.

[0105] When rainfall intensity in a region is excessive and exceeds the capacity of the local pipeline network, waterlogging is inevitable. This waterlogging will gradually disappear as the rainfall intensity decreases, but the damage caused is irreversible, requiring early disaster prevention. However, existing technologies often only use rainfall intensity as a parameter to calculate risk when assessing rainstorm risk. Whether or not there will be any impact is unclear, and what the impact will be, causing great difficulties in regional rainstorm disaster prevention work.

[0106] In particular, when the pipe network system in a certain area is not well developed, its drainage capacity per unit time is limited. If the rainfall per unit time is too large and exceeds the drainage capacity per unit time of the area, it will cause a certain degree of urban flooding. At the same time, the total rainfall of this rainstorm happens to be within the range that the catchment area of ​​the area can bear. In this case, the existing technology is very likely to give a low-risk or no-risk prompt for this rainstorm. However, in reality, the area does experience flooding due to drainage capacity issues, resulting in property losses. This application distinguishes the causes of flooding through calculation, thereby screening out the causes of D all With L all The resulting waterlogging scenarios were identified and classified and analyzed, which improved the information for regional rainstorm risk assessment, avoided the situation where disasters occurred without early warning due to the intensity of rainstorms, and improved the completeness of rainstorm risk assessment.

[0107] Further, obtain D all With L all The duration of the period when the ratio is greater than 1 is recorded as T a , get D all With L all The duration of the period when the ratio is greater than 1 is b, and the area of ​​the flooded area i is recorded as S i， The ideal water depth per square meter in the flooded area i is Z i It can be calculated by the following formula:

[0108] *100

[0109] According to the ideal water depth Z i The specific value of gives the severity level M of waterlogging in waterlogged area i. i , M i Includes: low risk, medium risk, high risk, or very high risk.

[0110] By calculating D all With L allThe ratio of water level to rainfall level can be used to estimate the maximum water level during the duration of heavy rainfall. The maximum water level can be used to judge the extent of waterlogging, thereby facilitating risk prevention in the area. For example, appropriate water barriers should be arranged in advance at the entrances of underground places such as subway stations and underground parking lots to effectively protect the property in the area and avoid further losses.

[0111] Specifically, when Z i When the value is greater than 10 and less than 20, output M i is low risk; when Z i When the value is greater than 20 and less than 50, output M i For medium risk; when Z i When the value is greater than 50 and less than 100, output M i为 High risk; when Z i When the value is greater than 100, output M i For extremely high risks, by quantifying the depth of water accumulation in the flooded area i, the water accumulation situation in the flooded area i can be effectively predicted so that personnel can deploy relevant measures to avoid property losses.

[0112] Furthermore, the rainstorm risk database described in step S5 also includes rainstorm prevention recommendations, the specific analysis process of which is as follows:

[0113] When the waterlogging severity level N output by waterlogging area i i When the risk is low or medium, determine whether the current water storage capacity of the catchment area i is greater than J all With V all If the difference is greater than , the output suggestion is: empty the catchment area;

[0114] If the current water storage capacity of the catchment area i is greater than J all With V all The difference between the values ​​of , or the waterlogging severity level N output by waterlogged area i i When the risk is high or extremely high, the output suggestion is: expand the maximum water collection capacity of the waterlogged area i; the specific output value is: the current maximum water collection capacity of the waterlogged area i is recorded as V all Multiply by J all With V all The ratio of

[0115] When the waterlogging severity level M output by waterlogging area i i When the risk is low or medium, judge F i When it is equal to zero, J all With V all Is the ratio less than 1? If so, output the suggestion: check the abnormality of the pipe network and ensure that the pipe network is unobstructed;

[0116] If F i When it is equal to zero, Jall With V all The ratio is still greater than 1, or the waterlogging severity level M output by waterlogging area i i When the risk is high or extremely high, the output suggestion is: expand the number of pipe networks in the flooded area i; the specific output value is: the number of pipe networks in the flooded area i multiplied by D all With L all ratio.

[0117] In the existing technology, the assessment of rainstorm risk can often only achieve a simple assessment function, and will not further analyze the risks and causes of rainstorm disasters. It also does not take into account that the causes of rainstorm disasters in different areas of the same region are also different, and thus it is impossible to effectively give specific prevention suggestions; this application first divides the area to be assessed into various regions, classifies and assesses different regions, and also distinguishes and judges the causes of waterlogging in different regions, so as to effectively find out the pain points of rainstorm prevention in different regions, and give different prevention suggestions based on the pain points, which effectively helps the area to be assessed to resist rainstorms in the future, and is of great significance for accurately issuing rainstorm disaster warnings and making scientific disaster prevention, mitigation and relief decisions.

[0118] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for risk assessment and analysis of excessive rainstorms in plain water network areas, characterized by: The following steps are involved: S1. Data Collection: Obtain comprehensive data on the area to be assessed, including topography, hydraulic pipe network, hydrological water collection, station locks, river network and channel, levees, past rainfall, and humanities information; S2. Module coupling: Based on the acquired comprehensive data, a comprehensive data module for the area to be assessed is constructed, and a three-dimensional model of the area to be assessed is constructed through the comprehensive data module; S3, model verification: Verify the accuracy of the 3D model of the area to be evaluated. If the verification is qualified, proceed to the next step. If the verification is unqualified, it indicates that there are abnormal data in the comprehensive data collected in step S1. After finding and correcting the abnormal data, return to step S2 to optimize the comprehensive data module; S4. Simulating rainfall: Access future meteorological data and risk avoidance data of the area to be assessed, and simulate future rainfall on a three-dimensional model of the area to be assessed based on the future meteorological data; S5. Result output: By simulating future rainfall, when there is a rainstorm, the three-dimensional model of the area to be assessed will simulate whether there is waterlogging based on the drainage function of different areas, and output a rainstorm risk database for the area to be assessed based on the areas with waterlogging. The specific method of model verification described in step S3 is as follows: Step 1: Obtain historical data on past rainstorm disasters in the area to be assessed; Step 2: Put the rainfall data when the rainstorm disaster occurs into the three-dimensional model for simulation; Step 3: Compare the historical disaster characteristics with the 3D model simulation. If the comparison results are consistent, the verification is qualified; if the comparison results are inconsistent, the verification fails. The risk avoidance data in step S4 includes the real-time water level of the catchment area, which is fed back in real time by installing a water level monitoring device inside the catchment area and using wireless communication technology; The risk avoidance data in step S4 also includes the regional pipe network abnormality rate F i , the specific method of obtaining it is: The area to be assessed is divided into n regions, and different regions are numbered, specifically {1, 2, 3...i...n}, and the number of past rainstorm disasters in different regions is obtained as H, and the total number of pipeline networks built in the region during past rainstorm disasters is obtained as R i The total number of abnormal pipe networks in the area during the same rainstorm disaster is set as E i , then the regional pipe network abnormality rate F in different areas i It can be calculated by the following formula: When simulating future rainfall on the three-dimensional model of the area to be assessed, turn off the F i Proportional pipe network; The total number of abnormal pipe networks E in the area to be assessed during a rainstorm disaster i The acquisition process is as follows: Step 1: Obtain the total number of abnormal pipe networks in the area to be assessed during the same rainstorm disaster and the disaster season, and build a four-season abnormal pipe network data model for the area to be assessed based on the SARIMA model; Step 2: Get the time when the last rainstorm disaster occurred in the area to be assessed. If the time interval between the last rainstorm disaster in the area to be assessed and the predicted rainstorm is less than half a month, then output E i If it is equal to 0, then if the time interval between the last rainstorm disaster in the area to be assessed and the predicted rainstorm is greater than half a month, then proceed to the next step; Step 3: Determine the predicted rainstorm season and input it into the abnormal pipe network data model of the four seasons in the area to be assessed. The abnormal pipe network data model of the four seasons in the area to be assessed outputs the total number of abnormal pipe networks E when the rainstorm causes disasters in the current season. i ; The specific output method of the rainstorm risk database of the area to be assessed in step S5 is as follows: Step 1: Determine whether the cause of waterlogging in area i is due to the catchment area or the pipe network; Step 2: Output the severity level N of waterlogging based on whether the waterlogging in area i is caused by the catchment area or the pipe network. i or M i ; Step 3: Obtain the GDP proportion G of the waterlogged area i in the area to be assessed i , and the proportion of the population of the waterlogged area i in the area to be assessed P i ; Step 4: Integrate the risk data of all flooded areas i, including: G i 、P i and N i or M i , and make a database output; The process of determining the cause of waterlogging in waterlogged area i is as follows: The total future rainfall in the flooded area i is obtained by using future meteorological data and recorded as J all The current maximum water volume of the catchment area is recorded as V all , calculate J all With V all If the ratio is less than 1, it means that the area is not flooded due to water catchment problems. all With V all The ratio is greater than 1. According to its specific value, the severity level N of the waterlogging area i is given. i , N i These include: low risk, medium risk, high risk, or very high risk; When J all With V all When the ratio is less than 1, the future rainfall duration of the waterlogged area i is divided into {2, 3...a} equal time periods according to future meteorological data, and then the future rainfall D in a single time period of the waterlogged area i is calculated. all , then combined with the regional pipeline network abnormality rate F i Calculate the maximum discharge volume L in a single time period of the flooded area i all , calculate D all With L all The ratio of D is greater than 1, indicating that area i will experience waterlogging due to insufficient pipe network capacity during this period. all With L all The severity of waterlogging in waterlogged area i is determined by the ratio of and the duration of waterlogging.

2. The method for risk assessment and analysis of excessive rainstorms in plain water network areas according to claim 1 is characterized by: Get D all With L all The duration of the period when the ratio is greater than 1 is recorded as T all , get D all With L all The duration of the period when the ratio is greater than 1 is b, and the area of ​​the flooded area i is recorded as S i， The ideal water depth per square meter in the flooded area i is Z i It can be calculated by the following formula: According to the ideal water depth Z i The specific value of gives the severity level M of waterlogging in waterlogged area i. i , M i Includes: low risk, medium risk, high risk, or very high risk.

3. The method for risk assessment and analysis of excessive rainstorms in plain water network areas according to claim 2 is characterized by: The rainstorm risk database described in step S5 also includes rainstorm prevention recommendations, and the specific analysis process is as follows: When the waterlogging severity level N output by waterlogging area i i When the risk is low or medium, determine whether the current water storage capacity of the catchment area i is greater than J all With V all If the difference is greater than , the output suggestion is: empty the catchment area; If the current water storage capacity of the catchment area i is less than J all With V all The difference between the values ​​of , or the waterlogging severity level N output by waterlogged area i i When the risk is high or very high, the output recommendation is: expand the maximum water catchment capacity of the flooded area i; When the waterlogging severity level M output by waterlogging area i i When the risk is low or medium, judge F i When it is equal to zero, J all With V all Is the ratio less than 1? If so, output the suggestion: check the abnormality of the pipe network and ensure that the pipe network is unobstructed; If F i When it is equal to zero, J all With V all The ratio is still greater than 1, or the waterlogging severity level M output by waterlogging area i i When the risk is high or extremely high, the output suggestion is: expand the number of pipelines in the flooded area i.

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