Early warning method for emergency evacuation in flood detention areas based on environmental perception and intelligent analysis

Abstract

This invention discloses an emergency evacuation early warning method for flood detention areas based on environmental perception and intelligent analysis, relating to the field of flood control and disaster reduction technology. The method includes collecting raw data from the flood detention area to form a monitoring array; dividing the monitoring array into several monitoring units according to a geographic grid; establishing a rainfall infiltration model based on rainfall and soil moisture data; constructing a surface runoff field by combining topographic elevation data; and calibrating the confluence direction of the runoff field using water level data. Based on the rainfall infiltration model and the surface runoff field, a flood inundation evolution sequence of the flood detention area is generated, segmented into an inundation level map according to the time step, and emergency evacuation routes are planned. These routes are then matched with the road network in a geographic information system to generate the optimal evacuation route. Tiered early warning information is issued to the warning area, and evacuation instructions are sent to residents of the flood detention area through an emergency broadcast system. This invention achieves precise delivery and efficient transmission of early warning information by establishing a zoned early warning mechanism and differentiated early warning strategies.

Description

Technical Field

[0001] This invention relates to the field of flood control and disaster reduction technology, and in particular to an emergency evacuation early warning method for flood detention areas based on environmental perception and intelligent analysis. Background Technology

[0002] Traditional flood detention area management relies primarily on hydrological station networks and manual inspections, employing experience-based judgment and simple threshold-based early warning methods. In recent years, with the development of IoT, GIS, and AI technologies, flood detention area monitoring and early warning have gradually evolved towards digitalization and intelligence. Domestic and international scholars have conducted extensive research on flood detention area safety management, proposing methods such as flood inundation range extraction based on remote sensing data, flood evolution prediction based on hydrodynamic models, and emergency evacuation route planning based on GIS. However, existing flood detention area early warning systems generally suffer from insufficient monitoring data accuracy, limited early warning indicators, and untimely and inaccurate early warning information dissemination. Particularly under complex terrain conditions, it is difficult to accurately depict the dynamic evolution of rainfall-runoff-inundation, leading to a lack of scientific basis for early warning decisions.

[0003] Currently, flood detention area early warning systems face numerous technical bottlenecks in practical applications: First, traditional single-point hydrological monitoring struggles to comprehensively reflect changes in the hydrological situation of flood detention areas, failing to promptly detect sudden events such as rapid localized flooding; second, existing early warning methods often neglect the impact of key factors like soil saturation and topographic features on the inundation process, resulting in limited early warning accuracy; third, the dissemination of early warning information lacks consideration for regional differences, failing to adopt differentiated early warning strategies based on the risk levels of different areas, thus affecting the effectiveness of early warnings and evacuation efficiency. Furthermore, existing emergency evacuation route planning methods are relatively simplistic, failing to fully consider the dynamic changes in road capacity and real-time inundation conditions, making it difficult to provide residents with optimal evacuation plans.

[0004] To address the aforementioned problems, this invention proposes an emergency evacuation early warning method for flood detention areas based on environmental perception and intelligent analysis. This method acquires high-precision environmental data by constructing a multi-source sensor monitoring network, integrating rainfall infiltration models and surface runoff field analysis to achieve accurate simulation and prediction of the inundation process in flood detention areas. Simultaneously, by combining GIS technology for evacuation path optimization and zoned early warning, the timeliness and relevance of the early warning are significantly improved. Summary of the Invention

[0005] In view of the problems existing in flood detention area early warning systems in terms of environmental monitoring accuracy, flooding process prediction, and emergency evacuation decision-making, this invention is proposed.

[0006] Therefore, the problem to be solved by this invention is how to achieve accurate prediction of the inundation process through intelligent analysis, so as to provide timely and reliable zoning early warning information and optimal evacuation plans for residents in flood detention areas.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0008] In a first aspect, embodiments of the present invention provide an emergency evacuation early warning method for flood detention areas based on environmental perception and intelligent analysis. The method includes: collecting raw data from the flood detention area using environmental monitoring sensor nodes to form a monitoring array, wherein the raw data includes rainfall data, soil moisture data, topographic elevation data, and water level data; dividing the monitoring array into several monitoring units according to a geographic grid, establishing a rainfall infiltration model based on the rainfall data and soil moisture data, constructing a surface runoff field using the topographic elevation data, and calibrating the confluence direction of the runoff field using the water level data; generating a flood inundation evolution sequence for the flood detention area based on the rainfall infiltration model and the surface runoff field, and dividing it into an inundation level map according to a time step; planning emergency evacuation routes for the flood detention area based on the inundation level map, matching the emergency evacuation routes with the road network in a geographic information system to generate an optimal evacuation route; dividing the flood detention area into early warning zones based on the optimal evacuation routes, issuing tiered early warning information to the early warning zones, and sending evacuation instructions to residents of the flood detention area through an emergency broadcast system.

[0009] As a preferred embodiment of the flood detention area emergency evacuation early warning method based on environmental perception and intelligent analysis described in this invention, the method includes: dividing the flood detention area into early warning zones based on the optimal evacuation route, issuing graded early warning information to the early warning zones, and sending evacuation instructions to residents of the flood detention area through an emergency broadcast system. This includes: dividing the flood detention area into several early warning zones based on the spatial distribution characteristics of the optimal evacuation route, combined with topography and population distribution; conducting risk assessments on each early warning zone, classifying each zone according to the assessment results, and establishing early warning level classification standards; developing early warning information for each early warning level, generating an early warning information dissemination plan, wherein the early warning information includes threat level, evacuation time limit, evacuation route, and evacuation guidance; deploying an emergency broadcast system in each early warning zone and establishing a zone control mechanism to achieve targeted dissemination of early warning information; and developing evacuation instruction issuance processes based on different dissemination strategies according to the early warning level, and setting up an early warning information dissemination effect evaluation mechanism, evaluating the early warning coverage rate through on-site inspections and information feedback.

[0010] As a preferred embodiment of the emergency evacuation early warning method for flood detention areas based on environmental perception and intelligent analysis described in this invention, the optimal evacuation route is generated as follows: based on the inundation hazard level of the inundation level map, a multi-layer path planning model is used to analyze the evacuation feasibility of each area within the flood detention area; road network data from a geographic information system is called to extract the spatial distribution information of main roads, secondary roads, and branch roads within the flood detention area; topological analysis is performed on the spatial distribution information to establish a road network layer and identify attribute information, wherein the attribute information includes road grade, pavement type, and road width; the inundation level map is overlaid onto the road network layer. A network layer is used to filter out road segments affected by flooding and calculate the road traffic risk index. A heuristic path search algorithm is employed, using the road traffic risk index, path length, and expected travel time as constraints, with the minimum risk path as the objective, to construct an emergency evacuation path network, generating multiple optional evacuation paths. This network includes a main evacuation route and alternative routes. The multiple optional evacuation paths are then optimized and ranked, and the safety margin, traffic efficiency, and capacity constraints of each path are comprehensively evaluated to select the optimal evacuation route. This optimal evacuation route is further divided into several evacuation channels according to the region, and each evacuation channel has alternative routes.

[0011] As a preferred embodiment of the flood detention area emergency evacuation early warning method based on environmental perception and intelligent analysis described in this invention, the method for generating the flood detention area inundation evolution sequence is as follows: based on the hydrodynamic field distribution intensity, a set of dynamic equations for the flood detention area inundation process is established using the water balance principle, wherein the set of dynamic equations includes a continuity equation and a momentum equation; the set of dynamic equations is discretely solved using the finite volume method to calculate the hydraulic gradient of the grid cells; a flood detention area water diffusion model is constructed based on the hydraulic gradient to calculate the dynamic expansion process of the inundation range and the inundation front advance velocity; based on the dynamic expansion process and the inundation front advance velocity, a time-series extrapolation method is used to generate the flood detention area inundation evolution sequence, wherein the inundation evolution sequence includes water depth field sequence data, flow velocity field sequence data, and inundation range time series data; the inundation evolution sequence is divided according to a preset time step to generate a series of inundation state sections; inundation hazard levels are set according to water depth and flow velocity, and the inundation state sections are classified; an inundation level map is constructed based on the classification results, and key feature locations and evacuation channels are marked on the inundation level map.

[0012] As a preferred embodiment of the flood detention area emergency evacuation early warning method based on environmental perception and intelligent analysis described in this invention, the specific formula for the inundation front advancement velocity is as follows:

[0013] ;

[0014] in, To submerge the leading edge propulsion speed, It is the acceleration due to gravity. Because of the water depth, For hydraulic gradient, For ground slope, Roughness coefficient The water depth influence coefficient, Let be the area submerged at time t. The area submerged in the previous moment. Let be the perimeter of the flooded region at time t. For time step.

[0015] As a preferred embodiment of the flood detention area emergency evacuation early warning method based on environmental perception and intelligent analysis described in this invention, the method for obtaining the hydrodynamic field distribution intensity is as follows: based on the spatial distribution characteristics of the monitoring point array, an adaptive grid partitioning method is used to divide the flood detention area into several second monitoring units; in the second monitoring unit, based on the rainfall data and the soil moisture data, the soil profile is divided through multi-layer soil structure characterization, a rainfall infiltration model is established, and the vertical infiltration flux is calculated using the Richards equation; a three-dimensional landform is constructed based on topographic elevation data, and the surface undulation characteristics are characterized by a digital elevation model, and topographic parameters are extracted, wherein the topographic parameters include slope and... Slope aspect; a surface runoff field is constructed based on the three-dimensional landform, and the surface runoff field is discretized into a computational grid. The diffusion wave equation is used to describe the surface water flow movement law; the surface runoff field is dynamically calibrated using water level data, and the hydraulic gradient is calculated based on the water level difference between adjacent second monitoring units; the water exchange relationship between the second monitoring units is established, the lateral recharge between the second monitoring units is calculated, and it is used as the boundary condition of the rainfall infiltration model, wherein the water exchange relationship is the coupling effect of surface runoff and soil lateral infiltration. The water balance equation is solved by iterative calculation; based on the rainfall infiltration model and the surface runoff field, the hydrodynamic field distribution intensity of the flood detention area is obtained by a coupled solution method.

[0016] As a preferred embodiment of the flood detention area emergency evacuation early warning method based on environmental perception and intelligent analysis described in this invention, the specific formula for the hydrodynamic field distribution intensity is as follows:

[0017] ;

[0018] in, The intensity of the hydrodynamic field distribution. For soil layers, The permeability coefficient, Let be the unsaturated hydraulic conductivity of the i-th soil layer. This is the depth attenuation coefficient. Let be the depth of the i-th soil layer. Calculate the number of grids for the surface. Let the water depth be the j-th grid. Manning's roughness coefficient Let the ground slope be the j-th grid. To calculate the length of the time period, For reference time, For time scale.

[0019] As a preferred embodiment of the emergency evacuation early warning method for flood detention areas based on environmental perception and intelligent analysis described in this invention, the method for generating the monitoring point array is as follows: A number of environmental monitoring sensor nodes are deployed within the flood detention area, and the distribution locations of the environmental monitoring sensor nodes are divided into several first monitoring units; the raw data collected by the environmental monitoring sensor nodes is transmitted to a data aggregation node via a wireless communication module, wherein the data aggregation node timestamps and spatially marks the raw data to form a monitoring point array dataset; a multi-discrimination algorithm is used to perform data quality checks on the monitoring point array dataset, eliminating abnormal data and interpolating missing data to generate the monitoring point array.

[0020] In a second aspect, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, they implement the steps of the flood detention area emergency evacuation early warning method based on environmental perception and intelligent analysis as described in the first aspect of the present invention.

[0021] Thirdly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of the flood detention area emergency evacuation early warning method based on environmental perception and intelligent analysis as described in the first aspect of the present invention.

[0022] The beneficial effects of this invention are as follows: By constructing a distributed environmental monitoring network, high-precision perception of hydrological elements in flood detention areas is achieved, solving the problem of insufficient spatial resolution in traditional monitoring methods; by establishing a rainfall-infiltration-runoff coupling model, accurate simulation of hydrological processes under complex underlying surface conditions is achieved, improving the accuracy of inundation evolution prediction; by integrating hydrodynamic principles and time-series analysis methods, a physical mechanism-based inundation process prediction model is established, making the prediction of inundation range and intensity more scientific; by intelligently matching inundation prediction results with the GIS road network and considering multiple constraints, the optimal evacuation route is automatically generated, improving the reliability of emergency evacuation plans; and by establishing a zoned early warning mechanism and differentiated early warning strategies, accurate delivery and efficient transmission of early warning information are achieved. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0024] Figure 1 This is a flowchart of the emergency evacuation early warning method for flood detention areas based on environmental perception and intelligent analysis, as described in Example 1. Detailed Implementation

[0025] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0026] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0027] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0028] Example 1

[0029] Reference Figure 1 This is the first embodiment of the present invention, which provides an emergency evacuation early warning method for flood detention areas based on environmental perception and intelligent analysis, including:

[0030] S1: Collect raw data from the flood detention area through environmental monitoring sensor nodes and form a monitoring array, wherein the raw data includes rainfall data, soil moisture data, topographic elevation data and water level data.

[0031] Specifically, several environmental monitoring sensor nodes are deployed within the flood detention area, and the distribution locations of the environmental monitoring sensor nodes are divided into several first monitoring units.

[0032] It should be noted that the environmental monitoring sensor nodes between adjacent first monitoring units establish communication links through wireless communication modules. The division of monitoring units is determined based on terrain features and hydrological characteristics. Within the first monitoring unit, rainfall sensors, soil moisture sensors, and water level sensors perform periodic sampling at preset time intervals. The environmental monitoring sensor nodes include rainfall sensors, soil moisture sensors, water level sensors, and elevation measurement devices. Rainfall sensors collect rainfall data from flood detention areas; soil moisture sensors collect soil moisture data from the subsurface soil layer; water level sensors collect water level data from rivers and low-lying areas; and elevation measurement devices collect topographic elevation data.

[0033] Furthermore, the raw data collected by the environmental monitoring sensor nodes is transmitted to the data aggregation node via a wireless communication module. The data aggregation node timestamps and marks the spatial coordinates of the raw data to form a monitoring point matrix dataset.

[0034] Furthermore, a multi-discrimination algorithm is used to perform data quality checks on the monitoring point matrix dataset, remove abnormal data, and interpolate missing data to generate the monitoring point matrix.

[0035] S2: By dividing the monitoring points into several monitoring units according to a geographical grid, and establishing a rainfall infiltration model based on the rainfall data and the soil moisture data, a surface runoff field is constructed by combining the topographic elevation data, and the confluence direction of the runoff field is determined by the water level data.

[0036] Specifically, based on the spatial distribution characteristics of the monitoring points, an adaptive grid partitioning method is used to divide the flood detention area into several second monitoring units.

[0037] It should be noted that the grid size of the second monitoring unit is dynamically adjusted according to the terrain change gradient; each second monitoring unit includes multiple monitoring points, and the distribution density of the monitoring points varies with the complexity of the terrain.

[0038] Furthermore, in the second monitoring unit, based on rainfall and soil moisture data, the soil profile is divided through multi-layer soil structure characterization, a rainfall infiltration model is established, and the vertical infiltration flux is calculated using the Richards equation. The specific formula is as follows:

[0039] ;

[0040] in, This refers to the vertical permeation flux. Let be the unsaturated hydraulic conductivity of the i-th soil layer. Because of the water depth, For soil depth, Here, θ represents the depth attenuation coefficient, and θ represents the soil moisture content. Residual moisture content This represents the saturated moisture content.

[0041] Furthermore, a three-dimensional landform is constructed based on topographic elevation data, and the surface undulation characteristics are characterized using a digital elevation model to extract topographic parameters, including slope and aspect. A surface runoff field is constructed based on the three-dimensional landform and discretized into a computational grid. The diffusion wave equation is used to describe the surface water flow pattern. The surface runoff field is dynamically calibrated using water level data, and the hydraulic gradient is calculated based on the water level difference between adjacent second monitoring units. The specific formula is as follows:

[0042] ;

[0043] Where n is the number of adjacent monitoring units. Let the water depth be the i-th grid. Let be the distance between the i-th adjacent monitoring points. This is the distance attenuation coefficient.

[0044] Specifically, the water exchange relationship between the second monitoring units is established, the lateral recharge between the second monitoring units is calculated, and it is used as the boundary condition of the rainfall infiltration model. The water exchange relationship is the coupling effect of surface runoff and soil lateral infiltration. The water balance equation is solved by iterative calculation.

[0045] Furthermore, based on the rainfall infiltration model and the surface runoff field, a coupled solution method is used to obtain the hydrodynamic field distribution intensity of the flood detention area.

[0046] Furthermore, the specific formula for the intensity of the hydrodynamic field distribution is as follows:

[0047] ;

[0048] in, The intensity of the hydrodynamic field distribution. For soil layers, The permeability coefficient, Let be the unsaturated hydraulic conductivity of the i-th soil layer. This is the depth attenuation coefficient. Let be the depth of the i-th soil layer. Calculate the number of grids for the surface. Let the water depth be the j-th grid. Manning's roughness coefficient Let the ground slope be the j-th grid. To calculate the length of the time period, For reference time, For time scale.

[0049] It should be noted that if the hydrodynamic field distribution intensity D ≥ 0.8, the area is affected by both heavy rainfall and surface runoff, and is classified as a Level 5 inundation risk, requiring immediate activation of a Level 1 warning; if 0.6 ≤ hydrodynamic field distribution intensity D < 0.8, the soil in the area is approaching saturation and the surface runoff intensity is relatively high, classifying it as a Level 4 inundation risk, requiring activation of a Level 2 warning; if 0.4 ≤ hydrodynamic field distribution intensity D < 0.6, the area shows obvious surface water accumulation, but has not yet reached a dangerous level, classifying it as a Level 3 inundation risk, requiring activation of a Level 3 warning and increased monitoring; if 0.2 ≤ hydrodynamic field distribution intensity D < 0.4, the soil moisture content in the area is high but surface runoff is weak, classifying it as a Level 2 inundation risk, requiring close monitoring of water conditions; if the hydrodynamic field distribution intensity D < 0.2, the hydrological conditions in the area are basically normal, classifying it as a Level 1 inundation risk, allowing for maintenance of the regular monitoring frequency.

[0050] S3: Based on the rainfall infiltration model and the surface runoff field, generate the flood inundation evolution sequence of the flood detention area and divide it into an inundation level map according to the time step.

[0051] Specifically, based on the intensity of the hydrodynamic field distribution, a set of dynamic equations for the inundation process of the flood detention area is established using the principle of water balance; a water diffusion model of the flood detention area is constructed based on the hydraulic gradient to calculate the dynamic expansion process of the inundation range and the advance velocity of the inundation front.

[0052] It should be noted that the dynamic equations include the continuity equation and the momentum equation; the dynamic equations are solved discretly using the finite volume method to calculate the hydraulic gradient of the grid cells.

[0053] Furthermore, the specific formula for the submerged leading edge propulsion velocity is as follows:

[0054] ;

[0055] in, To submerge the leading edge propulsion speed, It is the acceleration due to gravity. Because of the water depth, For hydraulic gradient, For ground slope, Roughness coefficient The water depth influence coefficient, Let be the area submerged at time t. The area submerged in the previous moment. Let be the perimeter of the flooded region at time t. For time step.

[0056] Furthermore, based on the dynamic expansion process and the advance velocity of the inundation front, a time-series extrapolation method is used to generate an inundation evolution sequence of the flood detention area, which includes water depth field sequence data, flow velocity field sequence data and inundation range time series data.

[0057] Specifically, the inundation evolution sequence is divided according to a preset time step to generate a series of inundation state sections; the inundation hazard level is set according to water depth and flow velocity, and the inundation state sections are classified; an inundation level map is constructed based on the classification results, and the locations of key features and evacuation routes are marked on the inundation level map.

[0058] It should be noted that when the water depth is ≥2.0m and the flow velocity is ≥2.5m / s, the Level 5 flood risk is marked as a red warning zone; when the water depth is 1.5m≤2.0m and the flow velocity is 2.0m / s≤2.5m / s, the Level 4 flood risk is marked as an orange warning zone; when the water depth is 1.0m≤1.5m and the flow velocity is 1.5m / s≤2.0m / s, the Level 3 flood risk is marked as a yellow warning zone; when the water depth is 0.5m≤1.0m and the flow velocity is 1.0m / s≤1.5m / s, the Level 2 flood risk is marked as a blue warning zone; and when the water depth is <0.5m and the flow velocity is <1.0m / s, the Level 1 flood risk is marked as a green zone.

[0059] S4: Based on the flood level map, plan the emergency evacuation route for the flood detention area, match the emergency evacuation route with the road network in the geographic information system, and generate the optimal evacuation route.

[0060] Specifically, based on the inundation hazard level of the inundation level map, a multi-level path planning model is used to analyze the evacuation feasibility of each area within the flood detention area; road network data from the geographic information system is called to extract the spatial distribution information of main roads, secondary roads and branch roads within the flood detention area; topological analysis is performed on the spatial distribution information to establish a road network layer and identify attribute information.

[0061] It should be noted that the multi-layered route planning model includes a safety assessment layer, a traffic capacity layer, and a time consumption layer. The safety assessment layer calculates the route hazard coefficient based on the flood depth and flow velocity; the traffic capacity layer assesses the road bearing capacity; and the time consumption layer estimates the travel time of the evacuation route. Attribute information includes road grade, pavement type, and road width.

[0062] Furthermore, the inundation level map is overlaid onto the road network layer to filter out road segments affected by inundation and calculate the road traffic risk index, using the following formula:

[0063] ;

[0064] in, Let be the traffic risk index for the i-th road segment. Let be the flood depth of the i-th road segment. Let be the water flow velocity in the i-th road segment. To design the maximum submersion depth, To design the maximum water flow velocity, For the length of the road segment, For road segment width, This represents the remaining safe passage time for the road segment. and These are the weighting coefficients, and ω_1 + ω_2 = 1. This is the time decay coefficient.

[0065] Furthermore, a heuristic path search algorithm is adopted, using the road traffic risk index, path length, and expected travel time as constraints, and aiming at the path with the least risk, to construct an emergency evacuation path network and generate multiple optional evacuation paths; the multiple optional evacuation paths are optimized and sorted, and the safety margin, traffic efficiency, and capacity constraints of each path are comprehensively evaluated to select the optimal evacuation route.

[0066] It should be noted that the emergency evacuation route network includes main routes and alternative routes; the optimal evacuation route is divided into several evacuation routes according to the area, and each evacuation route has alternative routes. Emergency signage is set up at key nodes of the optimal evacuation route, indicating the evacuation direction and distance information; a real-time monitoring mechanism for the optimal evacuation route is established to dynamically adjust the evacuation route according to the flooding situation, and emergency shelters and temporary resettlement points are included in the evacuation route planning scope.

[0067] S5: Based on the optimal evacuation route, divide the flood detention area into early warning zones, issue graded early warning information to the early warning zones, and send evacuation instructions to the residents of the flood detention area through the emergency broadcast system.

[0068] Specifically, based on the spatial distribution characteristics of the optimal evacuation routes, combined with topography and population distribution, the flood detention area is divided into several early warning zones; risk assessments are conducted for each early warning zone, and each zone is classified according to the assessment results, establishing a standard for classifying early warning levels.

[0069] It should be noted that if the warning area is located within a red warning zone on the inundation level map, has a population density ≥ 2000 people / km², a terrain elevation lower than the average elevation of the surrounding area, is ≥ 2 km from the optimal evacuation route, and contains important infrastructure (such as hospitals and schools), then this area should be designated as a Level 1 warning zone. If the warning area is located within an orange warning zone on the inundation level map, has a population density between 1000 and 2000 people / km², a terrain elevation close to the average elevation of the surrounding area, is 1 to 2 km from the optimal evacuation route, and contains concentrated residential areas, then this area should be designated as a Level 2 warning zone. The warning area is designated as a Level II warning area. If the warning area is located within the yellow warning area on the flood level map, has a population density between 500 and 1000 people per square kilometer, has a terrain elevation higher than the average elevation of the surrounding area, is 0.5 to 1 kilometer from the optimal evacuation route, and is a scattered residential area, then this area should be designated as a Level III warning area. If the warning area is located within the blue warning area on the flood level map, has a population density less than 500 people per square kilometer, has a terrain elevation significantly higher than the average elevation of the surrounding area, is less than 0.5 kilometers from the optimal evacuation route, and is mainly farmland or open space, then this area should be designated as a Level IV warning area.

[0070] Furthermore, warning information is formulated based on the warning level, a warning information dissemination plan is generated, an emergency broadcasting system is deployed in each warning area, and a zone control mechanism is established to achieve targeted dissemination of warning information.

[0071] It should be noted that the early warning information includes the threat level, evacuation time limit, evacuation route, and evacuation guidance; the emergency broadcasting system includes fixed broadcasting stations, mobile broadcasting vehicles, and portable amplification equipment.

[0072] Furthermore, based on the warning level, different release strategies are adopted to formulate the evacuation instruction issuance process, and a warning information release effect evaluation mechanism is set up to evaluate the warning coverage rate through on-site inspections and information feedback.

[0073] In summary, this invention achieves high-precision perception of hydrological elements in flood detention areas by constructing a distributed environmental monitoring network, thus solving the problem of insufficient spatial resolution in traditional monitoring methods. By establishing a rainfall-infiltration-runoff coupling model, it enables accurate simulation of hydrological processes under complex underlying surface conditions, improving the accuracy of inundation evolution prediction. By integrating hydrodynamic principles and time-series analysis methods, it establishes a physical mechanism-based inundation process prediction model, making the prediction of inundation range and intensity more scientific. By intelligently matching inundation prediction results with the GIS road network and considering multiple constraints, it achieves automatic generation of optimal evacuation routes, enhancing the reliability of emergency evacuation plans. Finally, by establishing a zoned early warning mechanism and differentiated early warning strategies, it achieves precise delivery and efficient transmission of early warning information.

[0074] This embodiment also provides a computer device applicable to the emergency evacuation early warning method for flood detention areas based on environmental perception and intelligent analysis, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the emergency evacuation early warning method for flood detention areas based on environmental perception and intelligent analysis as proposed in the above embodiment.

[0075] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0076] Example 2

[0077] Referring to Table 1, the second embodiment of the present invention provides an emergency evacuation early warning method for flood detention areas based on environmental perception and intelligent analysis. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0078] Specifically, 120 environmental monitoring sensor nodes were deployed within the study area, including 40 rainfall sensors, 40 soil moisture sensors, and 40 water level sensors. All sensors were solar-powered and equipped with 4G communication modules, with data acquisition frequency of 10 minutes per acquisition. Topographic elevation data was acquired using UAV aerial surveying, generating a 1-meter resolution digital elevation model. The study area was divided into 300 geographic grid units, each measuring 500 meters × 1000 meters. The raw data underwent quality checks using a multi-discrimination algorithm, resulting in the removal of 185 outlier data points, representing 0.86% of the total data. Missing data was repaired using Kriging interpolation.

[0079] Furthermore, when establishing the rainfall infiltration model, the soil profile was divided into four layers: the 0-20cm topsoil layer, the 20-50cm cultivated layer, the 50-100cm groundwater layer, and the 100-200cm bedrock layer. The modified Richards equation was used to calculate the vertical infiltration flux, and a depth attenuation coefficient was introduced to correct the infiltration coefficient. Simultaneously, a three-dimensional landform was constructed based on a 1-meter resolution DEM, and slope and aspect parameters were extracted to generate the surface runoff field.

[0080] Furthermore, an adaptive time step scheme was adopted during the model calculation process, with an initial step size of 1 minute, dynamically adjusted based on the convergence of the calculations. The water flow motion was described using the diffusion wave equation, and the calculation results showed good model stability with a quality error controlled within 0.1%. The time resolution of the inundation evolution sequence was 10 minutes, generating inundation state data for 144 time steps. Four inundation hazard levels were set based on inundation depth and flow velocity, and 327 possible evacuation routes were calculated using GIS road network data. Finally, 12 main evacuation routes and 24 alternative routes were selected, forming a complete emergency evacuation route network.

[0081] Specifically, as shown in Table 1, the improved Richards equation used in this invention, when calculating vertical infiltration flux, demonstrates a significant positive correlation between cumulative rainfall and maximum inundation depth in predicting inundation extent, based on data analysis from eight typical monitoring units (correlation coefficient R). 2 =0.892), and soil moisture content has a significant regulatory effect on the rate of inundation development.

[0082] Table 1 Experimental Data

[0083]

[0084] Furthermore, data from monitoring unit MU005 shows that when the cumulative rainfall reached 356.8 mm, the area experienced a maximum flood depth of 2.6 m and a maximum flow velocity of 1.7 m / s. It is particularly noteworthy that the early warning response time is significantly shortened by the present invention. Taking MU005 as an example, it only takes 20 minutes to complete the early warning information release and evacuation route planning, saving about 40% of the response time compared to conventional methods.

[0085] Furthermore, in terms of evacuation route planning, this invention introduces a multi-layered route planning model, making the assessment of the route risk index more comprehensive. Data shows that even under the most unfavorable conditions (such as MU005), the planned evacuation route risk index remains below 0.58, far lower than the 0.75 warning line of traditional methods. This improvement significantly enhances the safety of evacuation routes, theoretically reducing the risk of casualties during evacuation by approximately 65%. In addition, comparisons of data from different monitoring units reveal that this invention demonstrates a clear advantage in predicting inundation evolution under complex terrain conditions. For example, in the MU003 area with significant terrain undulations, the predicted inundation range matches the actual observed value by 91.3%.

[0086] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for early warning of emergency evacuation in flood detention areas based on environmental perception and intelligent analysis, characterized in that: include, Raw data from the flood detention area is collected through environmental monitoring sensor nodes and a monitoring array is formed. The raw data includes rainfall data, soil moisture data, topographic elevation data, and water level data. The monitoring points are divided into several monitoring units according to a geographical grid, and a rainfall infiltration model is established based on the rainfall data and the soil moisture data. A surface runoff field is constructed by combining the topographic elevation data, and the confluence direction of the runoff field is determined by the water level data. Based on the rainfall infiltration model and the surface runoff field, an inundation evolution sequence of the flood detention area is generated and divided into an inundation level map according to the time step; Based on the flood level map, plan the emergency evacuation route for the flood detention area, match the emergency evacuation route with the road network in the geographic information system, and generate the optimal evacuation route; Based on the optimal evacuation route, flood detention area warning zones are delineated, and graded warning information is issued to the warning zones. Evacuation instructions are sent to residents of the flood detention area through the emergency broadcast system. The method for generating the flood detention area inundation evolution sequence is as follows: Based on the intensity of the hydrodynamic field distribution, a set of dynamic equations for the inundation process of the flood detention area is established using the principle of water balance. The set of dynamic equations includes a continuity equation and a momentum equation. The set of dynamic equations is solved discretly using the finite volume method to calculate the hydraulic gradient of the grid cell. Based on the hydraulic gradient, a water diffusion model for the flood detention area is constructed to calculate the dynamic expansion process of the inundation range and the advance velocity of the inundation front. Based on the dynamic expansion process and the advance velocity of the inundation front, a time-series extrapolation method is used to generate an inundation evolution sequence for the flood detention area, wherein the inundation evolution sequence includes water depth field sequence data, flow velocity field sequence data and inundation range time series data; The flooding evolution sequence is divided according to a preset time step to generate a series of flooding state sections; The flooding hazard level is set according to water depth and flow velocity, and the flooding state section is classified into different levels. A flooding level map is constructed based on the classification results, and the locations of key features and evacuation routes are marked on the flooding level map. The specific formula for the submerged leading edge propulsion velocity is as follows: ； in, To submerge the leading edge propulsion speed, It is the acceleration due to gravity. Because of the water depth, For hydraulic gradient, For ground slope, Roughness coefficient The water depth influence coefficient, Let be the area submerged at time t. The area submerged in the previous moment. Let be the perimeter of the flooded region at time t. For time step.

2. The emergency evacuation early warning method for flood detention areas based on environmental perception and intelligent analysis as described in claim 1, characterized in that: Based on the optimal evacuation route, flood detention area warning zones are delineated, and tiered warning information is issued to these zones. Evacuation instructions are sent to residents of the flood detention areas via an emergency broadcast system, including: Based on the spatial distribution characteristics of the optimal evacuation route, combined with topography and population distribution, the flood detention area is divided into several early warning zones; Risk assessments were conducted for each warning area, and each area was classified according to the assessment results to establish a standard for classifying warning levels. Develop early warning information based on the warning level and generate an early warning information release plan, wherein the early warning information includes the threat level, evacuation time limit, evacuation route and risk avoidance guidance; Emergency broadcasting systems are deployed in each warning area, and a zoned control mechanism is established to enable targeted dissemination of warning information; Based on the aforementioned warning levels, different release strategies are adopted to formulate evacuation instruction issuance processes, and a warning information release effectiveness evaluation mechanism is set up to assess the warning coverage rate through on-site inspections and information feedback.

3. The emergency evacuation early warning method for flood detention areas based on environmental perception and intelligent analysis as described in claim 2, characterized in that: The method for generating the optimal retreat route is as follows: Based on the inundation hazard level of the inundation level map, a multi-level path planning model is used to analyze the evacuation feasibility of each area within the flood detention area. By calling road network data from the geographic information system, spatial distribution information of main roads, secondary roads and branch roads within the flood detention area is extracted. Perform topological analysis on the spatial distribution information to establish a road network layer and identify attribute information, wherein the attribute information includes road grade, road surface type and road width; The flooding level map is overlaid onto the road network layer to filter out road segments affected by flooding and calculate the road traffic risk index. A heuristic path search algorithm is adopted, taking the road traffic risk index, path length and expected travel time as constraints, and aiming at the path with the minimum risk, to construct an emergency evacuation path network and generate multiple optional evacuation paths. The emergency evacuation path network includes a main channel and alternative channels. The multiple optional evacuation routes are optimized and sorted, and the safety margin, traffic efficiency and capacity constraints of each route are comprehensively evaluated to select the optimal evacuation route. The optimal evacuation route is divided into several evacuation channels according to the region, and each evacuation channel is equipped with alternative routes.

4. The emergency evacuation early warning method for flood detention areas based on environmental perception and intelligent analysis as described in claim 1, characterized in that: The method for obtaining the intensity of the hydrodynamic field distribution is as follows: Based on the spatial distribution characteristics of the monitoring points, the flood detention area is divided into several second monitoring units using an adaptive grid partitioning method. In the second monitoring unit, based on the rainfall data and the soil moisture data, the soil profile is divided through multi-layer soil structure characterization, a rainfall infiltration model is established, and the vertical infiltration flux is calculated using the Richards equation. A three-dimensional landform is constructed based on topographic elevation data, and the surface undulation features are characterized by digital elevation modeling. Topographic parameters are extracted, including slope and aspect. A surface runoff field is constructed based on the three-dimensional landform, and the surface runoff field is discretized into a computational grid. The diffusion wave equation is used to describe the surface water flow movement law. The surface runoff field is dynamically calibrated using water level data, and the hydraulic gradient is calculated based on the water level difference between adjacent second monitoring units. Establish the water exchange relationship between the second monitoring units, calculate the lateral recharge between the second monitoring units, and use it as the boundary condition of the rainfall infiltration model. The water exchange relationship is the coupling effect of surface runoff and soil lateral infiltration. Solve the water balance equation through iterative calculation. Based on the rainfall infiltration model and the surface runoff field, the hydrodynamic field distribution intensity of the flood detention area is obtained by a coupled solution method.

5. The emergency evacuation early warning method for flood detention areas based on environmental perception and intelligent analysis as described in claim 4, characterized in that: The specific formula for the intensity of the hydrodynamic field distribution is as follows: ； in, The intensity of the hydrodynamic field distribution. For soil layers, The permeability coefficient, Let be the unsaturated hydraulic conductivity of the i-th soil layer. This is the depth attenuation coefficient. Let be the depth of the i-th soil layer. Calculate the number of grids for the surface. Let the water depth be the j-th grid. Manning's roughness coefficient Let the ground slope be the j-th grid. To calculate the length of the time period, For reference time, For time scale.

6. The emergency evacuation early warning method for flood detention areas based on environmental perception and intelligent analysis as described in claim 4 or 5, characterized in that: The method for generating the monitoring point array is as follows: Several environmental monitoring sensor nodes are deployed within the flood detention area, and the distribution locations of the environmental monitoring sensor nodes are divided into several first monitoring units; The raw data collected by the environmental monitoring sensor nodes is transmitted to the data aggregation node via a wireless communication module. The data aggregation node timestamps and marks the spatial coordinates of the raw data to form a monitoring point matrix dataset. A multi-discrimination algorithm is used to perform data quality checks on the monitoring point matrix dataset, remove abnormal data, and interpolate missing data to generate the monitoring point matrix.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the emergency evacuation early warning method for flood detention areas based on environmental perception and intelligent analysis as described in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the emergency evacuation early warning method for flood detention areas based on environmental perception and intelligent analysis as described in any one of claims 1 to 6.

Images