Flood disaster risk monitoring system and method based on distributed hydrological model, and storage medium

Abstract

The invention provides a flood disaster risk monitoring system and method based on a distributed hydrological model and a storage medium, and relates to the technical field of flood disaster risk monitoring, and the method comprises the steps: dividing a target region into a plurality of sub-region modules, and collecting the meteorological data, soil data and hydrological data of the sub-region modules; and inputting the collected real-time data of each sub-domain into the meteorological evaluation model, the soil evaluation model and the hydrological evaluation model, combining the outputs of the three models, comprehensively generating a distributed hydrological model, obtaining a corresponding flood risk index, comparing the flood risk index with a preset risk threshold, and automatically triggering early warning when the flood risk index exceeds an early warning threshold. According to the invention, the target area is divided into a plurality of sub-domain modules, and meteorological, soil and hydrological data are combined to construct a corresponding evaluation model, so that comprehensive analysis of complex terrains and changeable meteorological conditions is realized. The accuracy of flood risk assessment is improved, and model output can be updated in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of flood disaster risk monitoring, and in particular to a flood disaster risk monitoring system, method and storage medium based on a distributed hydrological model. Background Art

[0002] With the acceleration of global climate change and urbanization, the frequency of extreme weather events has increased significantly, and floods have become one of the major natural disasters faced by various regions. Traditional flood monitoring and early warning systems usually rely on limited meteorological data and a single hydrological model, and often lack consideration of complex terrain and diverse land use. This method cannot fully reflect the rainfall, soil moisture and hydrological dynamics in the basin, resulting in inaccurate assessment of flood risks. In addition, the existing system has the problem of insufficient timeliness in data collection and cannot integrate data from different sources in real time, which in turn affects the timeliness and accuracy of early warnings. Therefore, it is particularly urgent to adopt a more integrated and dynamic analysis method to cope with the increasingly severe flood risks.

[0003] Existing technologies usually lack the ability to comprehensively analyze the combination of meteorological, soil and hydrological data, which makes it impossible to comprehensively assess flood risks and easily ignore the mutual influence of certain key factors. In order to effectively respond to these challenges, there is an urgent need for a comprehensive distributed hydrological model that can divide the target area into several sub-domain modules based on detailed terrain data and basic layers, and collect meteorological, soil and hydrological data for each sub-domain module. By establishing a corresponding evaluation model, real-time monitoring and analysis of each sub-domain can be achieved, which can significantly improve the ability to predict flood risks. At the same time, combined with the threshold comparison module, when the flood risk index exceeds the preset threshold, the system can automatically trigger an early warning and notify relevant departments, thereby prompting timely emergency measures. This integrated management strategy helps to improve disaster response capabilities and reduce losses caused by floods.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The object of the present invention is to provide a flood disaster risk monitoring system, method and storage medium based on a distributed hydrological model to solve the problems raised in the above background technology.

[0006] A flood disaster risk monitoring system based on a distributed hydrological model, specifically comprising:

[0007] A watershed division module is used to obtain terrain data and basic layer data of a target area, and divide the target area into a plurality of sub-domain modules according to the basic layer data and the terrain data;

[0008] The data collection module is used to collect the meteorological data, soil data and hydrological data of each sub-domain module after division; and based on the collected meteorological data, soil data and hydrological data, generate the meteorological evaluation index, soil evaluation index and hydrological evaluation index of each sub-domain respectively;

[0009] The data analysis module generates flood risk indexes corresponding to each sub-domain based on meteorological assessment index, soil assessment index and hydrological assessment index;

[0010] The threshold comparison module is used to compare the flood risk index of each sub-domain with the preset risk threshold. When the warning threshold is exceeded, the system automatically triggers a warning and notifies relevant departments to take emergency measures.

[0011] Furthermore, the specific logic for dividing the target area into several sub-domain modules is as follows:

[0012] Obtain basic data, including terrain data, hydrological characteristic data and basic layer data; use DEM data and water flow accumulation algorithm to calculate water flow direction and cumulative flow; extract main river trunks according to flow thresholds, and use river sections with larger flow as main rivers; use water flow path analysis to identify tributaries and bifurcation points on the extracted river trunks; form confluence data by analyzing the flow direction of the river trunks and the extension direction of the tributaries; based on the flow direction data, use GIS software to generate confluence paths and water system networks; combine hydrological characteristic data to identify major rivers and their tributaries; use flow direction data and DEM to identify the boundaries of each sub-basin; generate watershed boundaries through GIS tools, select ridge lines as boundaries, and ensure that the boundaries of each sub-basin are hydrologically reasonable; perform multi-level division according to the water system network and divide the target area into sub-basins of different levels. The division of each sub-basin should take into account its environmental characteristics, which include area, river length and confluence characteristics; compare the division results with the existing hydrological observation data to check whether the water flow path and confluence of the basin are in line with reality; use spatial analysis tools to evaluate the hydrological characteristics of the basin to ensure the rationality of the division; make necessary adjustments to the basin division based on the verification results to ensure that each sub-basin can accurately reflect the actual hydrological characteristics.

[0013] Furthermore, the meteorological data specifically includes: real-time precipitation P, wind speed V, temperature T and humidity S in the subdomain module;

[0014] The soil data specifically includes: soil moisture θ and infiltration rate I in the subdomain module;

[0015] The hydrological data specifically include: surface runoff Q and water level height h within the subdomain module.

[0016] Furthermore, the process of collecting meteorological data, soil data and hydrological data of each divided sub-domain module specifically includes:

[0017] The meteorological data is collected by selecting a meteorological monitoring station in the subdomain for collection. If there is no meteorological station in the subdomain, remote sensing technology is used to obtain data;

[0018] The soil data is collected by selecting a representative soil profile in the sub-domain for collection;

[0019] The hydrological data is collected by selecting a hydrological monitoring station within the sub-domain for collection. If there is no corresponding station, remote sensing technology is used to obtain data;

[0020] Use a rain gauge to collect precipitation P in millimeters; use a temperature sensor to collect temperature T in degrees Celsius; use a wind sensor to collect wind speed V in meters per second; and use a humidity sensor to collect humidity S in percentage.

[0021] The formula for collecting soil moisture θ is:

[0022]

[0023] Where θ is the soil moisture in percentage, V t is the total volume of the sample soil, V w It is the volume of water in the soil, in cubic meters. The volume data of water in the soil is collected as follows:

[0024] Collect a certain volume of soil sample, put the soil sample into an oven and dry it to constant weight, record the mass difference before and after drying, which is the mass of water in the soil sample, recorded as m w , according to the following formula:

[0025]

[0026] Among them, m w is the mass of water, ρ w is the density of water, which is 1000 kg / m 3 ;

[0027] The formula for collecting the infiltration rate I is:

[0028]

[0029] Where I is the soil infiltration rate, Δh is the change in water level during the observation time, which is obtained by monitoring the water level change in the well, Δt is the observation time in seconds, and b is the contact area of ​​the infiltration experimental device in square meters;

[0030] The formula for collecting surface runoff Q is:

[0031]

[0032] Where Q is surface runoff in cubic meters per second, P is precipitation, I0 is initial infiltration in millimeters, and θ is soil moisture;

[0033] Among them, the specific logic for obtaining I0 through infiltration experiment is:

[0034] Collect a certain volume of soil in the subdomain module as a sample, fill the infiltration meter with water, and record the time at the beginning of the experiment. In the initial stage of the experiment, measure the change of water level regularly. At each measuring time point, record the height n of the water level drop in millimeters and calculate the initial infiltration volume:

[0035]

[0036] Among them, n1 is the water level at the beginning of the experiment, n0 is the water level after t time, and t is the time interval in seconds;

[0037] The specific logic for collecting the water level h is as follows:

[0038] The water level H in this sub-area is measured using groundwater monitoring wells gw , the height difference h between the groundwater level and the reference point is obtained, and the formula is as follows:

[0039] h=H0-H gw

[0040] Where h is the height difference between the groundwater level and the reference point, in meters, H gw is the height of the groundwater level in meters, and H0 is the preset reference point height in meters.

[0041] Furthermore, the meteorological assessment index is calculated based on the collected meteorological data, and the formula based on it is as follows:

[0042] A=e α*(P+T+V+S) +C1

[0043] Wherein, A is the meteorological assessment index, P is precipitation, T is temperature, V is wind speed, S is humidity, α is the preset proportional coefficient of (P+T+V+S), and α is greater than zero, and C1 is the constant correction index.

[0044] Furthermore, the soil evaluation index is calculated based on the collected soil data, and the formula used is as follows:

[0045] B=e β*θ +e -I +C2

[0046] Among them, B is the soil assessment index, θ is the soil moisture, I is the soil infiltration rate, β is the preset proportional coefficient of θ, and β is greater than zero, and C2 is the constant correction index.

[0047] Furthermore, the hydrological assessment index is calculated based on the collected hydrological data, and the formula used is as follows:

[0048] C=e γ*(h+Q) +C3

[0049] Among them, C is the hydrological assessment index, h is the height difference between the groundwater level and the reference point, Q is the surface runoff, γ is the preset proportional coefficient of (h+Q), and γ is greater than zero, and C3 is the constant correction index.

[0050] Furthermore, the meteorological assessment index, soil assessment index and hydrological assessment index are combined to comprehensively generate the flood risk coefficient QS, and the formula based on this is as follows:

[0051]

[0052] Among them, QS is the flood risk coefficient, A is the meteorological assessment index, B is the soil assessment index, C is the hydrological assessment index, and C4 is the constant correction index.

[0053] The comparison of the obtained flood risk index QS with the preset risk threshold QY specifically includes:

[0054] When QS≤QY, it means that the flood risk index of the subdomain module has not exceeded the safety threshold, and continuous monitoring of key data should be maintained to ensure that potential changes can be discovered in a timely manner;

[0055] When QS>QY, it means that the flood risk index of the subdomain module exceeds the safety threshold, the system automatically triggers an early warning, and relevant departments take emergency measures.

[0056] The present invention further provides a flood disaster risk monitoring method based on a distributed hydrological model. The flood disaster risk monitoring method based on a distributed hydrological model is obtained by executing the above-mentioned flood disaster risk monitoring system based on a distributed hydrological model. The specific steps include:

[0057] Step 1: Obtain terrain data and basic layer data of the target area, and divide the target area into several sub-domain modules according to the basic layer data and the terrain data;

[0058] Step 2: Collect meteorological data, soil data and hydrological data for each divided sub-domain module; and based on the collected meteorological data, soil data and hydrological data, generate meteorological evaluation index, soil evaluation index and hydrological evaluation index for each sub-domain respectively;

[0059] Step 3: Generate flood risk index corresponding to each sub-domain based on meteorological assessment index, soil assessment index and hydrological assessment index;

[0060] Step 4: Compare the flood risk index of each sub-domain with the preset risk threshold. When the warning threshold is exceeded, the system automatically triggers a warning and notifies relevant departments to take emergency measures.

[0061] The present invention also includes a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, any module in the system is implemented.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] The present invention can effectively obtain the topographic, meteorological, soil and hydrological data of the target area through systematic watershed division and data collection, thereby realizing refined management. First, the watershed division module ensures that the target area is divided into multiple sub-domains according to the terrain characteristics, which is convenient for the targeted collection and analysis of subsequent data. The introduction of the data acquisition module enables the meteorological, soil and hydrological characteristics of each sub-domain to be fully covered, providing a solid data foundation for the establishment of the evaluation model. Secondly, through the comprehensive application of the data analysis module, a distributed hydrological model can be generated to accurately calculate the flood risk index of each sub-domain. This process not only improves the accuracy of risk assessment, but also provides a scientific basis for decision-making. The setting of the threshold comparison module ensures that when the flood risk index exceeds the warning threshold, the system can quickly trigger the warning and promptly notify the relevant departments to implement emergency measures, thereby effectively reducing the impact of flood disasters on the society and ecological environment, optimizing the hydrological management process, improving the efficiency of disaster prevention and mitigation, and providing important data support for regional sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a schematic diagram of the overall system module of the present invention;

[0065] Figure 2 It is a schematic diagram of the overall method flow of the present invention. DETAILED DESCRIPTION

[0066] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0067] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0068] Example:

[0069] See also Figure 1 ,The present invention provides a flood disaster risk monitoring system of a distributed hydrological model, specifically comprising: a watershed division module, for obtaining terrain data and basic layer data of a target area, and dividing the target area into a plurality of sub-domain modules according to the basic layer data and the terrain data;

[0070] In this embodiment, the target area refers to a disaster-prone area, that is, an area where floods or flash floods often occur in history, so as to carry out wind control and early warning. The basic layer data is basic information such as land use and vegetation coverage to support subsequent analysis. The terrain data is terrain information of the target area obtained by using a digital elevation model (DEM), including slope, slope direction, water flow direction, elevation information, watershed boundaries, water system networks and terrain features;

[0071] The specific logic for dividing the target area into several sub-domain modules is as follows:

[0072] Obtain basic data, including terrain data, hydrological characteristic data and basic layer data; use DEM data and water flow accumulation algorithm to calculate water flow direction and cumulative flow; extract main river trunks according to flow thresholds, and use river sections with larger flow as main rivers; use water flow path analysis to identify tributaries and bifurcation points on the extracted river trunks; form confluence data by analyzing the flow direction of the river trunks and the extension direction of the tributaries; based on the flow direction data, use GIS software to generate confluence paths and water system networks; combine hydrological characteristic data to identify major rivers and their tributaries; use flow direction data and DEM to identify the boundaries of each sub-basin; generate watershed boundaries through GIS tools, select ridge lines as boundaries, and ensure that the boundaries of each sub-basin are hydrologically reasonable; perform multi-level division according to the water system network and divide the target area into sub-basins of different levels. The division of each sub-basin should take into account its environmental characteristics, which include area, river length and confluence characteristics; compare the division results with the existing hydrological observation data to check whether the water flow path and confluence of the basin are in line with reality; use spatial analysis tools to evaluate the hydrological characteristics of the basin to ensure the rationality of the division; make necessary adjustments to the basin division based on the verification results to ensure that each sub-basin can accurately reflect the actual hydrological characteristics.

[0073] The watershed division module in the present invention not only improves the accuracy and reliability of flood risk monitoring, but also provides a solid data foundation for the implementation of the overall plan, promotes scientific decision-making and effective management, and demonstrates obvious technical advantages and application value.

[0074] The data collection module is used to collect the meteorological data, soil data and hydrological data of each sub-domain module after division; and based on the collected meteorological data, soil data and hydrological data, generate the meteorological evaluation index, soil evaluation index and hydrological evaluation index of each sub-domain respectively;

[0075] In this embodiment, the meteorological data specifically includes: real-time precipitation P, wind speed V, temperature T and humidity S of the subdomain module;

[0076] The soil data specifically includes: soil moisture θ and infiltration rate I of the subdomain module;

[0077] The hydrological data specifically include: surface runoff Q and water level h of the subdomain module.

[0078] The process of collecting meteorological data, soil data and hydrological data from each divided sub-domain module specifically includes:

[0079] The meteorological data is collected by selecting a meteorological monitoring station in the subdomain for collection. If there is no meteorological station in the subdomain, remote sensing technology is used to obtain data;

[0080] The soil data is collected by selecting a representative soil profile in the sub-domain for collection;

[0081] The hydrological data is collected by selecting a hydrological monitoring station within the sub-domain for collection. If there is no corresponding station, remote sensing technology is used to obtain data;

[0082] Use a rain gauge to collect precipitation P in millimeters; use a temperature sensor to collect temperature T in degrees Celsius; use a wind sensor to collect wind speed V in meters per second; use a humidity sensor to collect humidity S in percentage; precipitation is the main factor in the formation of floods, and real-time monitoring of precipitation can help estimate potential flood risks; wind speed affects evaporation and rainfall distribution, and understanding wind speed conditions can help evaluate water circulation and distribution; temperature has a direct impact on water status and soil moisture evaporation, and is an important factor in evaluating hydrological processes; humidity affects the soil's water retention capacity and plant evaporation, helping to analyze water cycles.

[0083] The formula for collecting soil moisture θ is:

[0084]

[0085] Where θ is the soil moisture in percentage, V t is the total volume of the sample soil, V w It is the volume of water in the soil, in cubic meters. This formula intuitively reflects the relationship between the water content in the soil and the total volume through a simple proportional relationship. It is easy to understand and calculate. In addition, this formula conforms to the basic physical properties of water and can scientifically reflect the moisture status of the soil, providing a reliable data basis for subsequent hydrological assessments.

[0086] The V w The collection method is:

[0087] Collect a certain volume of soil sample, put the soil sample into an oven and dry it to constant weight, record the mass difference before and after drying, which is the mass of water in the soil sample, recorded as m w , according to the following formula:

[0088]

[0089] Among them, m w is the mass of water, ρ w is the density of water, which is 1000 kg / m 3 ;

[0090] The formula for collecting the infiltration rate I is:

[0091]

[0092] Among them, I is the soil infiltration rate, Δh is the change of water level during the observation time, which is obtained by monitoring the water level change in the well, Δt is the observation time in seconds, and b is the contact area of ​​the infiltration experimental device in square meters; the soil moisture content directly affects the soil's water absorption capacity and the generation of floods, and the key lies in the accuracy of the hydrological model; the infiltration rate reflects the soil's ability to penetrate precipitation, determines the amount of surface runoff generated, and is an important parameter for assessing flood risks.

[0093] The formula for collecting surface runoff Q is:

[0094]

[0095] Among them, Q is the surface runoff in cubic meters per second, P is the precipitation, I0 is the initial infiltration in millimeters, and θ is the soil moisture;

[0096] Among them, the specific logic for obtaining I0 through infiltration experiment is:

[0097] Collect a certain volume of soil in the subdomain module as a sample, fill the infiltration meter with water, and record the time at the beginning of the experiment. In the initial stage of the experiment, measure the change of water level regularly. At each measuring time point, record the height n of the water level drop in millimeters and calculate the initial infiltration volume:

[0098]

[0099] Among them, n1 is the water level at the beginning of the experiment, n0 is the water level after t time, and t is the time interval in seconds;

[0100] The specific logic for collecting the water level h is as follows:

[0101] The water level H in this sub-area is measured using groundwater monitoring wells gw , the height difference h between the groundwater level and the reference point is obtained, and the formula is as follows:

[0102] h=H0-H gw

[0103] Where h is the height difference between the groundwater level and the reference point, in meters, H gw is the height of the groundwater level in meters, and H0 is the preset reference point height in meters. Surface runoff directly reflects the flow of water in the basin after precipitation and is the core indicator of flood monitoring; water level changes can directly reflect flood dynamics, and timely water level monitoring is crucial for flood warning.

[0104] The data analysis module generates a flood risk index corresponding to each sub-domain based on the meteorological assessment index, the soil assessment index and the hydrological assessment index;

[0105] In this embodiment, the meteorological assessment index is calculated based on the collected meteorological data, and the formula used is as follows:

[0106] A=e α*(P+T+V+S) +C1

[0107] Among them, A is the meteorological assessment index, P is precipitation, T is temperature, V is wind speed, S is humidity, α is the preset proportional coefficient of (P+T+V+S), and α is greater than zero, and C1 is a constant correction index; when the precipitation P increases, the ground moisture will increase rapidly, resulting in an increase in surface runoff, and the meteorological assessment index A will increase; when the temperature T increases, high temperature may cause the soil to dry, reduce its infiltration capacity, increase surface runoff, and the meteorological assessment index A will increase; when the wind speed V increases, strong winds can enhance evaporation, and under certain meteorological conditions, help water vapor in the air to be transported upward to form clouds, resulting in concentrated precipitation, and the meteorological assessment index A will increase; when the humidity S increases, high humidity usually leads to more precipitation. Excessive humidity will make precipitation more concentrated, forming heavy rain, so the meteorological assessment index A increases; that is, P, T, V, S and the meteorological assessment index A are positively correlated.

[0108] The soil evaluation index is calculated based on the collected soil data, and the formula is as follows:

[0109] B=e β*θ +e -I +C2

[0110] Among them, B is the soil assessment index, θ is soil moisture, I is soil infiltration rate, β is the preset proportional coefficient of θ, and β is greater than zero, and C2 is a constant correction index. High soil moisture content often leads to soil saturation, thereby increasing flood risk, while low moisture content helps to effectively absorb water and reduce the generation of surface runoff, so when θ increases, the soil assessment index B increases; higher infiltration rates can usually effectively reduce the impact of precipitation on flood risk, while lower infiltration rates may aggravate surface runoff and increase the possibility of floods, so when I increases, the soil assessment index B decreases; that is, θ is positively correlated with B, and I is negatively correlated with B.

[0111] The hydrological assessment index is calculated based on the collected hydrological data, and the formula is as follows:

[0112] C=e γ*(h+Q) +C3

[0113] Among them, C is the hydrological assessment index, h is the height difference between the groundwater level and the reference point, Q is the surface runoff, γ is the preset proportional coefficient of (h+Q), and γ is greater than zero, and C3 is the constant correction index; a higher groundwater level usually makes the soil easy to saturate, increasing the risk of floods, while a lower groundwater level helps to effectively drain water and reduce the risk of floods. Therefore, when the height difference h between the groundwater level and the reference point increases, the hydrological assessment index C increases; when the surface runoff Q increases, it flows rapidly to rivers, lakes or other water bodies, which may cause the water level to rise rapidly, thereby causing floods, and the hydrological assessment index C will increase accordingly; that is, h, Q and the hydrological assessment index C are positively correlated.

[0114] The meteorological assessment index, soil assessment index and hydrological assessment index are combined to obtain the corresponding flood risk coefficient QS, which specifically includes:

[0115]

[0116] Among them, QS is the flood risk coefficient, A is the meteorological assessment index, B is the soil assessment index, C is the hydrological assessment index, and C4 is the constant correction index; when the meteorological assessment index A increases, the flood risk coefficient QS will increase accordingly. Similarly, when the soil assessment index B and the hydrological assessment index C increase, the flood risk coefficient QS increases, which shows that A, B, C and the flood risk coefficient QS are positively correlated.

[0117] By combining the outputs of meteorological, soil and hydrological assessment models, generating a distributed hydrological model and calculating the flood risk index, a comprehensive, accurate and timely assessment of flood risk can be achieved. This strategy not only improves prediction accuracy and decision support capabilities, but also promotes the implementation of the overall flood management plan and provides strong technical support for responding to flood disasters.

[0118] The threshold comparison module is used to compare the obtained flood risk index of the subdomain to be evaluated with the preset risk threshold. When the warning threshold is exceeded, the system automatically triggers a warning and notifies relevant departments to take emergency measures.

[0119] In this embodiment, the comparison of the obtained flood risk index QS with the preset risk threshold QY specifically includes:

[0120] When QS≤QY, it means that the flood risk index of the subdomain module has not exceeded the safety threshold, and continuous monitoring of key data should be maintained to ensure that potential changes can be discovered in a timely manner;

[0121] When QS>QY, it means that the flood risk index of the subdomain module exceeds the safety threshold, the system automatically triggers an early warning, and relevant departments take emergency measures.

[0122] The threshold comparison module compares the flood risk index with the preset risk threshold in an automated way, enabling an efficient and accurate early warning system. Compared with traditional manual monitoring methods, this module not only improves the real-time and accuracy of early warnings, but also helps coordinate emergency responses, improve overall flood management efficiency, and enhance the public's sense of security and response capabilities. The overall solution is therefore better able to respond to flood disasters and mitigate potential losses.

[0123] A flood disaster risk monitoring method based on a distributed hydrological model, the specific steps include:

[0124] Step 1: Obtain terrain data and basic layer data of the target area, and divide the target area into several sub-domain modules according to the basic layer data and the terrain data;

[0125] Step 2: Collect meteorological data, soil data and hydrological data for each divided sub-domain module; and based on the collected meteorological data, soil data and hydrological data, generate meteorological evaluation index, soil evaluation index and hydrological evaluation index for each sub-domain respectively;

[0126] Step 3: Generate flood risk index corresponding to each sub-domain based on meteorological assessment index, soil assessment index and hydrological assessment index;

[0127] Step 4: Compare the flood risk index of each sub-domain with the preset risk threshold. When the warning threshold is exceeded, the system automatically triggers a warning and notifies relevant departments to take emergency measures.

[0128] The present invention also includes a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, any module in the system is implemented.

[0129] The specific values ​​of α, β and γ in the formula are generally determined by technical personnel in this field according to actual conditions. The technical personnel in this field collect multiple groups of sample data, and set corresponding preset proportional coefficients for each group of sample data. The preset proportional coefficients and the collected sample data are substituted into the formula. Through repeated experiments and parameter adjustments, the accuracy of the model output and the rationality of the results are observed, and these factor coefficients are gradually adjusted. The performance and effect of the model under different parameter settings are compared to find the optimal coefficient combination, and the calculated factor coefficients are screened and averaged to obtain the values ​​of α, β and γ.

[0130] In addition, the size of the preset factor coefficient is to quantify each parameter to obtain a specific value. In order to facilitate subsequent comparison, the size of the coefficient depends on the amount of sample data and the preset proportional coefficient initially set by technical personnel in this field for each set of sample data. It is not unique as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0131] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0132] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0133] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0134] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A flood disaster risk monitoring system based on a distributed hydrological model, characterized in that: Specifically include: A watershed division module is used to obtain terrain data and basic layer data of a target area, and divide the target area into a plurality of sub-domain modules according to the basic layer data and the terrain data; The data collection module is used to collect the meteorological data, soil data and hydrological data of each sub-domain module after division; and based on the collected meteorological data, soil data and hydrological data, generate the meteorological evaluation index, soil evaluation index and hydrological evaluation index of each sub-domain respectively; The data analysis module generates flood risk indexes corresponding to each sub-domain based on meteorological assessment index, soil assessment index and hydrological assessment index; The threshold comparison module is used to compare the flood risk index of each sub-domain with the preset risk threshold. When the warning threshold is exceeded, the system automatically triggers a warning and notifies relevant departments to take emergency measures.

2. A flood disaster risk monitoring system based on a distributed hydrological model according to claim 1, characterized in that: The specific logic for dividing the target area into several sub-domain modules is as follows: Obtain basic data, including terrain data, hydrological characteristic data and basic layer data; use DEM data and water flow accumulation algorithm to calculate water flow direction and cumulative flow; extract main river trunks according to flow thresholds, and use river sections with larger flow as main rivers; use water flow path analysis to identify tributaries and bifurcation points on the extracted river trunks; form confluence data by analyzing the flow direction of the river trunks and the extension direction of the tributaries; based on the flow direction data, use GIS software to generate confluence paths and water network; combine hydrological characteristic data to identify major rivers and their tributaries; use flow direction data and DEM to identify the boundaries of each sub-basin; generate watershed boundaries through GIS tools, select ridge lines as boundaries, and ensure that the boundaries of each sub-basin are hydrologically reasonable; perform multi-level division according to the water network, and divide the target area into sub-basins of different levels. The division of each sub-basin should take into account its environmental characteristics. The environmental characteristics are area, river length and confluence characteristics; the division results are compared with the existing hydrological observation data to check whether the water flow path and confluence of the basin are in line with reality; Spatial analysis tools are used to evaluate the hydrological characteristics of the basin and ensure the rationality of the division; based on the verification results, necessary adjustments are made to the basin division to ensure that each sub-basin can accurately reflect the actual hydrological characteristics.

3. The flood disaster risk monitoring system based on a distributed hydrological model according to claim 1, characterized in that: The meteorological data specifically includes: real-time precipitation P, wind speed V, temperature T and humidity S in the subdomain module; The soil data specifically includes: soil moisture θ and infiltration rate I in the subdomain module; The hydrological data specifically include: surface runoff Q and water level height h within the subdomain module.

4. The flood disaster risk monitoring system based on a distributed hydrological model according to claim 1, characterized in that: The process of collecting meteorological data, soil data and hydrological data from each divided subdomain module specifically includes: The meteorological data is collected by selecting a meteorological monitoring station in the subdomain for collection. If there is no meteorological station in the subdomain, remote sensing technology is used to obtain data; The soil data is collected by selecting a representative soil profile in the sub-domain for collection; The hydrological data is collected by selecting a hydrological monitoring station within the sub-domain for collection. If there is no corresponding station, remote sensing technology is used to obtain data; Use a rain gauge to collect precipitation P in millimeters; use a temperature sensor to collect temperature T in degrees Celsius; use a wind sensor to collect wind speed V in meters per second; and use a humidity sensor to collect humidity S in percentage. The formula for collecting soil moisture θ is: Where θ is the soil moisture in percentage, V t is the total volume of the sample soil, V w It is the volume of water in the soil, in cubic meters. The volume data of water in the soil is collected as follows: Collect a certain volume of soil sample, put the soil sample into an oven and dry it to constant weight, record the mass difference before and after drying, which is the mass of water in the soil sample, recorded as m w , according to the following formula: Among them, m w is the mass of water, ρ w is the density of water, which is 1000 kg / m 3 ; The formula for collecting the infiltration rate I is: Where I is the soil infiltration rate, Δh is the change in water level during the observation time, which is obtained by monitoring the water level change in the well, Δt is the observation time in seconds, and b is the contact area of ​​the infiltration experimental device in square meters; The formula for collecting surface runoff Q is: Where Q is surface runoff in cubic meters per second, P is precipitation, I0 is initial infiltration in millimeters, and θ is soil moisture; Among them, the specific logic for obtaining I0 through infiltration experiment is: Collect a certain volume of soil in the subdomain module as a sample, fill the infiltration meter with water, and record the time at the beginning of the experiment. In the initial stage of the experiment, measure the change of water level regularly. At each measuring time point, record the height n of the water level drop in millimeters and calculate the initial infiltration volume: Among them, n1 is the water level at the beginning of the experiment, n0 is the water level after t time, and t is the time interval in seconds; The specific logic for collecting the water level h is as follows: The water level H in this sub-area is measured using groundwater monitoring wells gw , the height difference h between the groundwater level and the reference point is obtained, and the formula is as follows: h=H0-H gw Where h is the height difference between the groundwater level and the reference point, in meters, H gw is the height of the groundwater level in meters, and H0 is the preset reference point height in meters.

5. A flood disaster risk monitoring system based on a distributed hydrological model according to claim 4, characterized in that: The meteorological assessment index is calculated based on the collected meteorological data, and the formula is as follows: A=e α*(P+T+V+S) +C1 Wherein, A is the meteorological assessment index, P is precipitation, T is temperature, V is wind speed, S is humidity, α is the preset proportional coefficient of (P+T+V+S), and α is greater than zero, and C1 is the constant correction index.

6. A flood disaster risk monitoring system based on a distributed hydrological model according to claim 4, characterized in that: The soil evaluation index is calculated based on the collected soil data, and the formula is as follows: B=e β*θ +e -I +C2 Among them, B is the soil assessment index, θ is the soil moisture, I is the soil infiltration rate, β is the preset proportional coefficient of θ, and β is greater than zero, and C2 is the constant correction index.

7. The flood disaster risk monitoring system based on a distributed hydrological model according to claim 4, characterized in that: The hydrological assessment index is calculated based on the collected hydrological data, and the formula is as follows: C=e γ*(g+h) +C3 Among them, C is the hydrological assessment index, h is the height difference between the groundwater level and the reference point, Q is the surface runoff, γ is the preset proportional coefficient of (h+Q), and γ is greater than zero, and C3 is the constant correction index.

8. The flood disaster risk monitoring system based on a distributed hydrological model according to claim 1, characterized in that: The meteorological assessment index, soil assessment index and hydrological assessment index are combined to comprehensively generate the flood risk coefficient QS, and the formula based on it is as follows: Among them, QS is the flood risk coefficient, A is the meteorological assessment index, B is the soil assessment index, C is the hydrological assessment index, and A4 is the constant correction index; The comparison of the obtained flood risk index QS with the preset risk threshold QY specifically includes: When QS≤QY, it means that the flood risk index of the subdomain module has not exceeded the safety threshold, and continuous monitoring of key data should be maintained to ensure that potential changes can be discovered in a timely manner; When QS>QY, it means that the flood risk index of the subdomain module exceeds the safety threshold, the system automatically triggers an early warning, and relevant departments take emergency measures.

9. A flood disaster risk monitoring method based on a distributed hydrological model, characterized in that: The flood disaster risk monitoring method based on a distributed hydrological model is obtained by using a flood disaster risk monitoring system based on a distributed hydrological model as described in any one of claims 1 to 8, and comprises: Step 1: Obtain terrain data and basic layer data of the target area, and divide the target area into several sub-domain modules according to the basic layer data and the terrain data; Step 2: Collect meteorological data, soil data and hydrological data for each divided sub-domain module; and based on the collected meteorological data, soil data and hydrological data, generate meteorological evaluation index, soil evaluation index and hydrological evaluation index for each sub-domain respectively; Step 3: Generate flood risk index corresponding to each sub-domain based on meteorological assessment index, soil assessment index and hydrological assessment index; Step 4: Compare the flood risk index of each sub-domain with the preset risk threshold. When the warning threshold is exceeded, the system automatically triggers a warning and notifies relevant departments to take emergency measures.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the flood disaster risk monitoring method based on the distributed hydrological model described in claim 9 are implemented.

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