Emergency evacuation early warning method for flood detention area based on environmental perception and intelligent analysis

Abstract

The invention discloses a flood detention area emergency evacuation early warning method based on environmental perception and intelligent analysis, and relates to the technical field of flood control and disaster mitigation, and the method comprises the steps: collecting the original data of a flood detention area site, and forming a monitoring array; a monitoring array is divided into a plurality of monitoring units according to geographic grids, a rainfall permeation model is established according to rainfall data and soil humidity data, a surface runoff field is constructed in combination with terrain elevation data, and the confluence direction of the runoff field is calibrated through water level data; based on a rainfall penetration model and a surface runoff field, generating a flood detention area inundation evolution sequence, segmenting into an inundation grade map according to a time step length, planning a flood detention area emergency evacuation path, matching with a road network in a geographic information system, generating an optimal evacuation path, and issuing graded early warning information to an early warning area. And sending a withdrawal instruction to residents in the flood detention area through the emergency broadcasting system. According to the invention, by establishing a partition early warning mechanism and a differential early warning strategy, accurate delivery and efficient transmission of early warning information are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of flood prevention and disaster reduction, and in particular to a flood detention area emergency evacuation warning method based on environmental perception and intelligent analysis. Background Art

[0002] Traditional detention basin management relies primarily on a network of hydrological stations and manual inspections, employing empirical judgment and simple threshold-based early warning systems. In recent years, with the development of the Internet of Things (IoT), geographic information systems (GIS), and artificial intelligence (AI), detention basin monitoring and early warning systems have gradually evolved toward digitalization and intelligence. Domestic and international scholars have conducted extensive research on the safety management of detention basins, proposing methods for extracting flood inundation ranges based on remote sensing data, predicting flood evolution based on hydrodynamic models, and planning emergency evacuation routes based on GIS. However, existing detention basin early warning systems generally suffer from issues such as insufficient monitoring data accuracy, limited early warning indicators, and inaccurate and timely warning information release. Particularly in complex terrain, it is difficult to accurately depict the dynamic evolution of rainfall, runoff, and inundation, resulting in a lack of scientific basis for early warning decisions.

[0003] Currently, the practical application of the flood detention area early warning system faces numerous technical bottlenecks: First, traditional single-point hydrological monitoring fails to fully reflect the hydrological changes in the flood detention area and cannot promptly capture sudden emergencies such as localized rapid waterlogging. Second, existing early warning methods often overlook the impact of key factors such as soil saturation and topographic characteristics on the inundation process, resulting in limited warning accuracy. Third, the release of early warning information lacks consideration of regional differences and fails to adopt differentiated early warning strategies based on the risk level of different areas, which affects the effectiveness of early warnings and evacuation efficiency. Furthermore, existing emergency evacuation route planning methods are relatively simple and do not 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 these issues, this paper proposes a flood detention area emergency evacuation warning method based on environmental perception and intelligent analysis. This method utilizes a multi-source sensor monitoring network to acquire high-precision environmental data, integrating rainfall infiltration models with surface runoff analysis to accurately simulate and predict flooding in the detention area. Furthermore, by incorporating GIS technology for evacuation route optimization and zoning warnings, the method significantly improves the timeliness and relevance of warnings. Summary of the Invention

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

[0006] Therefore, the problem to be solved by the present invention is how to achieve accurate prediction of the flooding process through intelligent analysis means, and then provide timely and reliable zoning warning information and optimal evacuation plans for residents in the flood detention area.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides a flood detention area emergency evacuation warning method based on environmental perception and intelligent analysis, comprising: collecting raw data from the flood detention area site through environmental monitoring sensor nodes and forming a monitoring point matrix, wherein the raw data includes rainfall data, soil moisture data, terrain elevation data, and water level data; dividing the monitoring point matrix into a plurality of monitoring units according to a geographic grid, establishing a rainfall infiltration model based on the rainfall data and the soil moisture data, constructing a surface runoff field in combination with the terrain elevation data, and calibrating the confluence direction of the runoff field through the water level data; generating a flood detention area inundation evolution sequence based on the rainfall infiltration model and the surface runoff field, and dividing it into an inundation level map according to the time step; planning an emergency evacuation path for the flood detention area according to the inundation level map, matching the emergency evacuation path with the road network in the geographic information system, and generating an optimal evacuation route; dividing the flood detention area into early warning areas based on the optimal evacuation route, issuing graded early warning information to the early warning areas, and sending evacuation instructions to residents of the flood detention area through the emergency broadcast system.

[0009] As a preferred solution of the flood detention area emergency evacuation warning method based on environmental perception and intelligent analysis described in the present invention, wherein: based on the optimal evacuation route, the flood detention area is divided into warning areas, and graded warning information is issued to the warning areas, and evacuation instructions are sent to residents of the flood detention area through the emergency broadcast system, including: dividing the flood detention area into several warning areas according to the spatial distribution characteristics of the optimal evacuation route, combined with the topography and population distribution; conducting risk assessment on each warning area, grading each area according to the assessment results, and establishing a warning level classification standard; formulating warning information according to the warning level, generating a warning information release plan, wherein the warning information includes the threat level, evacuation time limit, evacuation route and risk avoidance guidance; deploying an emergency broadcast system in each warning area, and establishing a zoning control mechanism to realize the targeted release of warning information; based on the warning level, adopting different release strategies to formulate a evacuation instruction release process, and setting up a warning information release effect evaluation mechanism, and evaluating the warning coverage rate through on-site inspections and information feedback.

[0010] As a preferred solution of the method for emergency evacuation warning of flood detention areas based on environmental perception and intelligent analysis described in the present invention, the method for generating the optimal evacuation route is to analyze the evacuation feasibility of each area in the flood detention area based on the flood danger level of the flood level map using a multi-layer path planning model; call the road network data in the geographic information system to extract the spatial distribution information of the main roads, secondary roads and branches in the flood detention area; perform topological analysis on the spatial distribution information, establish a road network layer, and identify attribute information, wherein the attribute information includes road grade, road surface type and road width; superimpose the flood level map on the road network layer. A network layer is used to screen out road sections affected by flooding and calculate the road traffic risk index; a heuristic path search algorithm is used, with the road traffic risk index, path length and expected travel time as constraints, and with the minimum risk path as the goal, to construct an emergency evacuation path network and generate multiple optional evacuation paths, wherein the emergency evacuation path network includes a main channel and alternative channels; 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 screen out the optimal evacuation route, wherein the optimal evacuation route is divided into several evacuation channels according to the region, and alternative paths are set for each evacuation channel.

[0011] As a preferred embodiment of the method for emergency evacuation and early warning of a detention area based on environmental perception and intelligent analysis described in the present invention, the method for generating the detention area inundation evolution sequence comprises: establishing a set of dynamic equations for the detention area inundation process based on the intensity of the hydrodynamic field distribution and the water balance principle, wherein the dynamic equations include a continuity equation and a momentum equation; discretizing the dynamic equations using the finite volume method to calculate the hydraulic gradients of the grid cells; constructing a water diffusion model for the detention area based on the hydraulic gradients to calculate the dynamic expansion process of the inundation range and the advancing speed of the inundation front; generating a detention area inundation evolution sequence based on the dynamic expansion process and the advancing speed of the inundation front using a time series deduction method, wherein the inundation evolution sequence includes water depth field sequence data, flow velocity field sequence data, and inundation range time series data; segmenting the inundation evolution sequence according to a preset time step to generate a series of inundation state sections; setting inundation hazard levels based on water depth and flow velocity, and classifying the inundation state sections; constructing an inundation level map based on the classification results, and marking the locations of key landforms and evacuation routes on the inundation level map.

[0012] As a preferred solution of the flood detention area emergency evacuation warning method based on environmental perception and intelligent analysis of the present invention, the specific formula of the flood front advancing speed is as follows:

[0013]

[0014] Among them, Vf is the advancing speed of the submerged front, g is the acceleration of gravity, l is the water depth, I is the hydraulic gradient, S0 is the ground slope, k is the roughness coefficient, λ is the water depth influence coefficient, and A t is the flooded area at time t, A t-Δt is the flooded area at the previous moment, P t is the perimeter of the flooded area at time t, and Δt is the time step.

[0015] As a preferred solution of the flood detention area emergency evacuation warning method based on environmental perception and intelligent analysis described in the present invention, the method for obtaining the distribution intensity of the hydrodynamic field is to divide the flood detention area into a number of second monitoring units based on the spatial distribution characteristics of the monitoring point array 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 surface morphology is constructed based on the terrain elevation data, and the surface undulation characteristics are characterized by a digital elevation model to extract terrain parameters, wherein the terrain parameters include slope and slope direction; construct a surface runoff field according to the three-dimensional surface morphology, discretize the surface runoff field into a calculation grid, and use the diffusion wave equation to describe the surface water flow movement law; dynamically calibrate the surface runoff field through water level data, and calculate the hydraulic gradient according to the water level difference between adjacent second monitoring units; establish a water exchange relationship between the second monitoring units, calculate the lateral recharge between the second single monitoring units, and use it as the boundary condition of the rainfall infiltration model, wherein the water exchange relationship is the coupling effect of surface runoff and lateral soil seepage, and solve the water balance equation through iterative calculation; based on the rainfall infiltration model and the surface runoff field, adopt a coupled solution method to obtain the hydrodynamic field distribution intensity of the detention area.

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

[0017]

[0018] Where D is the intensity of the hydrodynamic field distribution, N is the number of soil layers, η is the permeability coefficient, K(θ i ) is the unsaturated hydraulic conductivity of the i-th layer of soil, μ is the depth attenuation coefficient, z i is the depth of the i-th soil layer, M is the number of surface calculation grids, h j is the water depth of the jth grid, n is the Manning roughness coefficient, S j is the ground slope of the jth grid, T is the length of the calculation period, t0 is the reference time, and σ is the time scale.

[0019] As a preferred solution of the flood detention area emergency evacuation warning method based on environmental perception and intelligent analysis described in the present invention, the method for generating the monitoring point array is as follows: a plurality of environmental monitoring sensor nodes are deployed within the flood detention area, and the distribution positions of the environmental monitoring sensor nodes are divided into a plurality of first monitoring units; the raw data collected by the environmental monitoring sensor nodes are transmitted to a data aggregation node via a wireless communication module, wherein the data aggregation node timestamps and spatial coordinates the raw data to form a monitoring point array data set; a multiple discrimination algorithm is used to perform data quality inspection on the monitoring point array data set, abnormal data is eliminated, and missing data is interpolated to generate a monitoring point array.

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

[0021] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the flood detention area emergency evacuation warning method based on environmental perception and intelligent analysis as described in the first aspect of the present invention are implemented.

[0022] The beneficial effects of the present 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 of traditional monitoring methods; by establishing a rainfall-infiltration-runoff coupling model, accurate simulation of hydrological processes under complex underlying surface conditions is achieved, and the accuracy of inundation evolution prediction is improved; 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 automatic generation of optimal evacuation routes is achieved, thereby improving the reliability of emergency evacuation plans; by establishing a zoning early warning mechanism and differentiated early warning strategies, accurate delivery and efficient transmission of early warning information are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0024] Figure 1This is a flow chart of the flood detention area emergency evacuation warning method based on environmental perception and intelligent analysis in Example 1. DETAILED DESCRIPTION

[0025] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0026] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0027] Secondly, the term "one embodiment" or "embodiment" 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 various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0028] Example 1

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

[0030] S1: Collecting raw data from the flood detention area through environmental monitoring sensor nodes and forming a monitoring point matrix, wherein the raw data includes rainfall data, soil moisture data, terrain elevation data and water level data.

[0031] Specifically, 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 a number of 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, and the division of monitoring units is determined based on terrain features and hydrological characteristics; within the first monitoring unit, rain sensors, soil moisture sensors and water level sensors perform periodic sampling at preset time intervals; environmental monitoring sensor nodes include rain sensors, soil moisture sensors, water level sensors and elevation measuring devices; rain sensors collect rainfall data in flood retention areas; soil moisture sensors collect soil moisture data of the subsurface soil layer; water level sensors collect water level data in rivers and low-lying areas; elevation measuring devices collect terrain elevation data.

[0033] Furthermore, the raw data collected by the environmental monitoring sensor nodes are transmitted to the data aggregation node through the wireless communication module, wherein the data aggregation node performs time stamp marking and spatial coordinate marking on the raw data to form a monitoring point matrix data set.

[0034] Furthermore, a multiple discrimination algorithm is used to perform data quality inspection on the monitoring point matrix data set, remove abnormal data, and interpolate the missing data to generate the monitoring point matrix.

[0035] S2: Divide the monitoring point array into a number of monitoring units according to a geographic grid, establish a rainfall infiltration model based on the rainfall data and the soil moisture data, construct a surface runoff field in combination with the terrain elevation data, and calibrate the confluence direction of the runoff field using the water level data.

[0036] Specifically, 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 a number of second monitoring units.

[0037] It should be noted that the grid size of the second monitoring unit is dynamically adjusted according to the gradient of terrain changes; 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 data 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 Q is calculated using the Richards equation. v , the specific formula is as follows:

[0039]

[0040] Among them, Q v is the vertical infiltration flux, K(θ) is the unsaturated hydraulic conductivity of the i-th layer of soil, h is the water depth, z is the soil depth, μ is the depth attenuation coefficient, θ is the soil moisture content, and θ r is the residual moisture content, θ s is the saturated moisture content.

[0041] Furthermore, a three-dimensional surface morphology is constructed based on the terrain elevation data, and the surface undulation characteristics are characterized by a digital elevation model to extract terrain parameters, including slope and slope aspect. A surface runoff field is constructed based on the three-dimensional surface morphology and discretized into computational grids. The diffusion wave equation is used to describe the movement of surface water flow. The surface runoff field is dynamically calibrated using water level data, and the hydraulic gradient I 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, h i is the water depth of the i-th grid, d i is the distance between the i-th adjacent monitoring points, and β is the distance attenuation coefficient.

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

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

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

[0047]

[0048] Where D is the intensity of the hydrodynamic field distribution, N is the number of soil layers, η is the permeability coefficient, K(θ i ) is the unsaturated hydraulic conductivity of the i-th layer of soil, μ is the depth attenuation coefficient, z i is the depth of the i-th soil layer, M is the number of surface calculation grids, h j is the water depth of the jth grid, n is the Manning roughness coefficient, S j is the ground slope of the jth grid, T is the length of the calculation period, t0 is the reference time, and σ is the time scale.

[0049] It should be noted that if the hydrodynamic field distribution intensity D ≥ 0.8, the area is subject to the dual effects of heavy rainfall and surface runoff, and is judged to be at a level 5 flooding risk, requiring the immediate activation of a level 1 warning. If the hydrodynamic field distribution intensity D is 0.6 ≤ < 0.8, the soil in the area is tending to be saturated and the surface runoff intensity is large, which is a level 4 flooding risk and a level 2 warning. If the hydrodynamic field distribution intensity D is 0.4 ≤ < 0.6, there is obvious surface water accumulation in the area, but it has not yet reached a dangerous level. It is a level 3 flooding risk and requires the activation of a level 3 warning and intensified monitoring. If the hydrodynamic field distribution intensity D is 0.2 ≤ < 0.4, the soil moisture content in the area is increased but the surface runoff is weak, which is a level 2 flooding risk and requires close attention to changes in water conditions. If the hydrodynamic field distribution intensity D is < 0.2, the hydrological conditions in the area are basically normal and it is a level 1 flooding risk, and the regular monitoring frequency can be maintained.

[0050] S3: Based on the rainfall infiltration model and the surface runoff field, a flooding evolution sequence of the detention area is generated, and the sequence is divided into flooding level maps according to the time step.

[0051] Specifically, based on the distribution intensity of the hydrodynamic field, the water balance principle is adopted to establish a group of dynamic equations for the flooding process of the detention area; according to the hydraulic gradient, a water diffusion model of the detention area is constructed to calculate the dynamic expansion process of the flooding range and the advancement speed of the flooding front.

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

[0053] Furthermore, the specific formula for the submerged front advancing speed is as follows:

[0054]

[0055] Among them, V f is the advancing speed of the submerged front, g is the acceleration of gravity, l is the water depth, I is the hydraulic gradient, S0 is the ground slope, k is the roughness coefficient, λ is the water depth influence coefficient, and A t is the flooded area at time t,

[0056] A t-Δt is the flooded area at the previous moment, P t is the perimeter of the flooded area at time t, and Δt is the time step.

[0057] Furthermore, based on the dynamic expansion process and the advancing speed of the flood front, a time series deduction method is used to generate the flooding evolution sequence of the detention area, where the flooding evolution sequence includes the water depth field sequence data, the flow velocity field sequence data and the flooding range time series data;

[0058] Specifically, the inundation evolution sequence is divided according to the preset time step to generate a series of inundation state sections; the inundation hazard level is set according to the water depth and flow velocity, and the inundation state sections are graded and classified; the inundation level map is constructed based on the classification results, and the locations of key landforms and retreat channels are marked on the inundation level map.

[0059] It should be noted that when the water depth is ≥2.0m and the flow velocity is ≥2.5m / s, the fifth level of flooding risk will be marked as a red warning area; when 1.5m≤water depth<2.0m and 2.0m / s≤flow velocity<2.5m / s, the fourth level of flooding risk will be marked as an orange warning area; when 1.0m≤water depth<1.5m and 1.5m / s≤flow velocity<2.0m / s, the third level of flooding risk will be marked as a yellow warning area; when 0.5m≤water depth<1.0m and 1.0m / s≤flow velocity<1.5m / s, the second level of flooding risk will be marked as a blue warning area; when the water depth<0.5m and the flow velocity<1.0m / s, the first level of flooding risk will be marked as a green area.

[0060] S4: planning an emergency evacuation route for the flood detention area according to the inundation level map, matching the emergency evacuation route with the road network in the geographic information system, and generating an optimal evacuation route.

[0061] Specifically, based on the flood hazard level of the inundation level map, a multi-layer path planning model is used to analyze the evacuation feasibility of each area in the flood detention area; the road network data in the geographic information system is called to extract the spatial distribution information of the main roads, secondary roads and branches in the flood detention area; the spatial distribution information is topologically analyzed, and a road network layer is established to identify the attribute information.

[0062] It should be noted that the multi-layer path planning model includes a safety assessment layer, a capacity layer, and a time consumption layer. The safety assessment layer calculates the path hazard factor based on flooding depth and flow velocity; the capacity layer assesses road carrying capacity; and the time consumption layer estimates the travel time of the evacuation path. Attribute information includes road grade, pavement type, and road width.

[0063] Furthermore, the flooding level map is superimposed on the road network layer to screen out road sections affected by flooding, and the road traffic risk index is calculated. The specific formula is as follows:

[0064]

[0065] Among them, R i is the traffic risk index of the i-th road section, H i is the flooding depth of the i-th road section, v i is the water flow velocity of the i-th road section, H max is the maximum design submerged depth, v max is the designed maximum water flow velocity, L is the length of the road section, W is the width of the road section, T1 is the remaining safe passage time of the road section, ω1 and ω2 are weight coefficients, and ω1+ω2=1, and τ is the time attenuation coefficient.

[0066] Furthermore, a heuristic path search algorithm is adopted, taking the road traffic risk index, path length and expected travel time as constraints, and taking the minimum risk path as the goal to construct an emergency retreat path network and generate multiple optional retreat paths; the multiple optional retreat paths are optimized and sorted, and the safety margin, traffic efficiency and capacity constraints of each path are comprehensively evaluated to screen out the optimal retreat route.

[0067] It should be noted that the emergency evacuation route network includes main channels and alternative channels. The optimal evacuation route is divided into several evacuation channels according to region, and each evacuation channel has an alternative route. Emergency signs are installed 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, and the evacuation route is dynamically adjusted according to the flooding situation. Emergency shelters and temporary resettlement points are included in the evacuation route planning.

[0068] S5: Based on the optimal evacuation route, the flood detention area is divided into early warning areas, and graded early warning information is issued to the early warning areas, and evacuation instructions are sent to residents of the flood detention area through the emergency broadcast system.

[0069] Specifically, based on the spatial distribution characteristics of the optimal evacuation route, combined with the topography and population distribution, the flood retention area will be divided into several early warning areas; a risk assessment will be conducted on each early warning area, and each area will be graded according to the assessment results, and a warning level classification standard will be established.

[0070] It should be noted that if the warning area is located in the red warning area on the inundation level map, the population density is ≥2000 people / square kilometer, the terrain elevation is lower than the average elevation of the surrounding area, the distance to the optimal evacuation route is ≥2 kilometers, and the area contains important infrastructure (such as hospitals, schools, etc.), then this area should be designated as a first-level warning area; if the warning area is located in the orange warning area on the inundation level map, the population density is between 1000 people / square kilometer and 2000 people / square kilometer, the terrain elevation is close to the average elevation of the surrounding area, the distance to the optimal evacuation route is between 1 kilometer and 2 kilometers, and there are concentrated residential areas in the area, then this area should be designated as a first-level warning area. It is a second-level warning area; if the warning area is located in the yellow warning area on the inundation level map, the population density is between 500 and 1,000 people / square kilometer, the terrain elevation is higher than the average elevation of the surrounding area, the distance to the optimal evacuation route is between 0.5 and 1 kilometer, and the area is a dispersed residential area, then this area should be designated as a third-level warning area; if the warning area is located in the blue warning area on the inundation level map, the population density is <500 people / square kilometer, the terrain elevation is significantly higher than the average elevation of the surrounding area, the distance to the optimal evacuation route is <0.5 kilometers, and the area is mainly farmland or vacant land, then this area should be designated as a fourth-level warning area.

[0071] Furthermore, warning information is formulated according to the warning level, a warning information release plan is generated, an emergency broadcast system is deployed in each warning area, and a zoning control mechanism is established to achieve targeted release of warning information.

[0072] It should be noted that the early warning information includes the threat level, evacuation time limit, evacuation route and risk avoidance instructions; the emergency broadcast system includes fixed broadcast stations, mobile broadcast vehicles and portable sound amplification equipment.

[0073] Furthermore, based on the warning level, different release strategies are adopted to formulate the process of issuing retreat orders, and a warning information release effect evaluation mechanism is set up to evaluate the warning coverage through on-site inspections and information feedback.

[0074] In summary, the present invention achieves high-precision perception of hydrological elements in flood detention areas by constructing a distributed environmental monitoring network, thereby solving the problem of insufficient spatial resolution of traditional monitoring methods; by establishing a rainfall-infiltration-runoff coupling model, accurate simulation of hydrological processes under complex underlying surface conditions is achieved, thereby 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 automatic generation of optimal evacuation routes is achieved, thereby improving the reliability of emergency evacuation plans; by establishing a zoning early warning mechanism and differentiated early warning strategies, accurate delivery and efficient transmission of early warning information are achieved.

[0075] This embodiment also provides a computer device, which is suitable for the case of a flood detention area emergency evacuation warning method 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 computer-executable instructions to implement the flood detention area emergency evacuation warning method based on environmental perception and intelligent analysis proposed in the above embodiment.

[0076] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0077] Example 2

[0078] Referring to Table 1, which is a second embodiment of the present invention, this embodiment provides a flood detention area emergency evacuation warning method 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.

[0079] 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 are solar-powered and equipped with 4G communication modules, with a data acquisition frequency of 10 minutes. Terrain elevation data was acquired using drone aerial surveys, generating a 1-meter resolution digital elevation model. The study area was divided into 300 geographic grid cells, each measuring 500 meters by 1000 meters. The raw data were quality-checked using a multiple-discrimination algorithm, eliminating 185 outlier data points, representing 0.86% of the total data volume. Missing data were repaired using Kriging interpolation.

[0080] Furthermore, when developing the rainfall infiltration model, the soil profile was divided into four layers: the 0-20 cm topsoil layer, the 20-50 cm cultivated layer, the 50-100 cm groundwater layer, and the 100-200 cm bedrock layer. A modified Richards equation was used to calculate vertical infiltration flux, incorporating a depth attenuation factor to correct the permeability coefficient. Furthermore, a three-dimensional surface morphology was constructed based on a 1-meter resolution DEM, and slope and aspect parameters were extracted to generate a surface runoff field.

[0081] Furthermore, an adaptive time step scheme was employed during the model calculation process, with an initial step size of 1 minute, which was dynamically adjusted based on the convergence of the calculation. The diffusion wave equation was used to describe the water flow, and the calculation results showed good model stability, with a quality error within 0.1%. The time resolution of the inundation evolution sequence was 10 minutes, generating a total of 144 time steps of inundation state data. Four levels of inundation hazard were set based on inundation depth and flow velocity, and 327 possible evacuation routes were calculated using GIS road network data. Ultimately, 12 main evacuation channels and 24 alternative channels were selected, forming a complete emergency evacuation route network.

[0082] Specifically, as shown in Table 1, the improved Richards equation used in the present invention calculates the vertical infiltration flux. In terms of flooding range prediction, by analyzing the data of 8 typical monitoring units, it can be seen that the cumulative rainfall and the maximum flooding depth show a significant positive correlation (correlation coefficient R 2 =0.892), and soil moisture content has a significant regulatory effect on the speed of flooding development.

[0083] Table 1 Test data

[0084]

[0085] Furthermore, data from monitoring unit MU005 shows that when the cumulative rainfall reached 356.8 mm, the area experienced a maximum flooding depth of 2.6 m and a maximum flow rate 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 issuance of early warning information and evacuation route planning, saving approximately 40% of the response time compared to conventional methods.

[0086] Furthermore, in terms of evacuation path planning, the present invention introduces a multi-layer path planning model to make the assessment of the path risk index more comprehensive. Data show that even in the most unfavorable circumstances (such as MU005), the planned evacuation path risk index is still controlled within 0.58, which is far below the warning line of 0.75 of the traditional method. This improvement significantly improves the safety of the evacuation route and can theoretically reduce the risk of casualties during the evacuation process by about 65%. In addition, by comparing the data from different monitoring units, it can be found that the present invention shows obvious advantages in dealing with the prediction of flooding evolution under complex terrain conditions. For example, in the MU003 area with large terrain undulations, the predicted flooding range is consistent with the actual observation value by 91.3%.

[0087] 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 the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A flood detention area emergency evacuation warning method based on environmental perception and intelligent analysis, characterized by: include, The raw data of the flood detention area is collected through the environmental monitoring sensor nodes to form a monitoring point matrix, wherein the raw data includes rainfall data, soil moisture data, terrain elevation data and water level data; The monitoring point array is divided into a plurality of monitoring units according to a geographic grid, and a rainfall infiltration model is established based on the rainfall data and the soil moisture data. A surface runoff field is constructed in combination with the terrain elevation data, and the confluence direction of the runoff field is calibrated using the water level data. Based on the rainfall infiltration model and the surface runoff field, a flooding evolution sequence of the detention area is generated, and the sequence is divided into flooding level maps according to the 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 the geographic information system to generate an optimal evacuation route; Based on the optimal evacuation route, the flood detention area is divided into early warning areas, and graded early warning information is issued to the early warning areas, and evacuation instructions are sent to residents of the flood detention area through the emergency broadcast system.

2. The method for emergency evacuation of a flood detention area based on environmental perception and intelligent analysis according to claim 1, characterized in that: Based on the optimal evacuation route, the detention area is divided into warning zones, and graded warning information is issued to the warning zones. Evacuation instructions are sent to residents of the detention area through the emergency broadcast system, including: Based on the spatial distribution characteristics of the optimal evacuation route, combined with the topography and population distribution, the flood detention area is divided into several warning areas; Conduct risk assessments for each warning area, classify each area based on the assessment results, and establish warning level classification standards; Formulate warning information based on the warning level and generate a warning information release plan, where the warning information includes the threat level, evacuation time limit, evacuation route and risk avoidance guidance; Deploy emergency broadcast systems in each warning area and establish a zoning control mechanism to achieve targeted release of warning information; Based on the warning level, different release strategies are adopted to formulate the retreat order issuance process, and a warning information release effect evaluation mechanism is set up to evaluate the warning coverage through on-site inspections and information feedback.

3. The method for emergency evacuation of a flood detention area based on environmental perception and intelligent analysis according to claim 2, characterized in that: The method for generating the optimal retreat route is: Based on the flooding hazard level of the flooding level map, a multi-layer path planning model is used to analyze the evacuation feasibility of each area in the flood detention area; Calling the road network data in the geographic information system to extract the spatial distribution information of the main roads, secondary roads and branches in the flood detention area; Performing a topological analysis on the spatial distribution information, establishing a road network layer, and identifying attribute information, wherein the attribute information includes road grade, road surface type, and road width; Overlaying the flooding level map onto the road network layer, screening out road sections affected by flooding, and calculating a road traffic risk index; A heuristic path search algorithm is used, with the road travel risk index, path length, and expected travel time as constraints, with the minimum risk path as the goal, to construct an emergency evacuation path network and generate multiple optional evacuation paths, wherein the emergency evacuation path network includes a main channel and alternative channels; The multiple optional retreat paths are optimized and sorted, and the safety margin, traffic efficiency and capacity constraints of each path are comprehensively evaluated to screen out the optimal retreat route, wherein the optimal retreat route is divided into several retreat channels according to the region, and alternative paths are set for each retreat channel.

4. The method for emergency evacuation of a flood detention area based on environmental perception and intelligent analysis according to claim 3, characterized in that: The method for generating the flooding evolution sequence of the detention basin is as follows: Based on the intensity of the hydrodynamic field distribution, a set of dynamic equations for the flooding process of the detention basin is established using the water balance principle. The dynamic equations include a continuity equation and a momentum equation. The dynamic equations are discretely solved using the finite volume method to calculate the hydraulic gradient of the grid cells. A water diffusion model of the detention area is constructed based on the hydraulic gradient to calculate the dynamic expansion process of the flooded area and the advancing speed of the flooded front; Based on the dynamic expansion process and the advancing speed of the flood front, a time series deduction method is used to generate a flooding evolution sequence of the detention area, wherein the flooding evolution sequence includes water depth inter-field sequence data, flow velocity inter-field sequence data and flooding range time series data; Dividing the flooding evolution sequence according to a preset time step to generate a series of flooding state sections; Setting flooding risk levels according to water depth and flow velocity, and classifying the flooded sections; An inundation level map is constructed based on the classification results, and the locations of key landforms and evacuation routes are marked on the inundation level map.

5. The method for emergency evacuation of a flood detention area based on environmental perception and intelligent analysis according to claim 4, characterized in that: The specific formula for the submerged front advancing speed is as follows: Among them, V f is the advancing speed of the submerged front, g is the acceleration of gravity, l is the water depth, I is the hydraulic gradient, S0 is the ground slope, k is the roughness coefficient, λ is the water depth influence coefficient, and A t is the flooded area at time t, A t-Δt is the flooded area at the previous moment, P t is the perimeter of the flooded area at time t, and Δt is the time step.

6. The method for emergency evacuation of a flood detention area based on environmental perception and intelligent analysis according to claim 5, characterized in that: The method for obtaining the distribution intensity of the hydrodynamic field is: Based on the spatial distribution characteristics of the monitoring points, the adaptive grid generation 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 by multi-layer soil structure characterization, a rainfall infiltration model is established, and the vertical infiltration flux is calculated using the Richards equation; Constructing a three-dimensional surface morphology based on terrain elevation data, and describing surface undulations through a digital elevation model to extract terrain parameters, including slope and aspect; constructing a surface runoff field according to the three-dimensional surface morphology, discretizing the surface runoff field into a computational grid, and using a diffusion wave equation to describe the movement of surface water flow; 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; Establishing a water exchange relationship between the second monitoring units, calculating the lateral recharge between the second monitoring units, and using this as a boundary condition for a rainfall infiltration model, wherein the water exchange relationship is the coupling effect of surface runoff and lateral soil seepage, and solving a water balance equation through iterative calculation; Based on the rainfall infiltration model and the surface runoff field, a coupled solution method is adopted to obtain the distribution intensity of the hydrodynamic field in the flood detention area.

7. The method for emergency evacuation of a flood detention area based on environmental perception and intelligent analysis according to claim 6, characterized in that: The specific formula for the hydrodynamic field distribution intensity is as follows: Where D is the intensity of the hydrodynamic field distribution, N is the number of soil layers, η is the permeability coefficient, K(θ i ) is the unsaturated hydraulic conductivity of the i-th layer of soil, μ is the depth attenuation coefficient, z i is the depth of the i-th soil layer, M is the number of surface calculation grids, h j is the water depth of the jth grid, n is the Manning roughness coefficient, S j is the ground slope of the jth grid, T is the length of the calculation period, t0 is the reference time, and σ is the time scale.

8. The method for emergency evacuation of a flood detention area based on environmental perception and intelligent analysis according to claim 6 or 7, characterized in that: The method for generating the monitoring point array is: Deploying a plurality of environmental monitoring sensor nodes within the flood detention area, and dividing the distribution locations of the environmental monitoring sensor nodes into a plurality of first monitoring units; The raw data collected by the environmental monitoring sensor node is transmitted to the data aggregation node through the wireless communication module, wherein the data aggregation node performs time stamp marking and spatial coordinate marking on the raw data to form a monitoring point matrix data set; A multiple discrimination algorithm is used to perform data quality inspection on the monitoring point matrix data set, remove abnormal data, and perform interpolation processing on missing data to generate a monitoring point matrix.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the processor implements the steps of the flood detention area emergency evacuation warning method based on environmental perception and intelligent analysis according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the flood detention area emergency evacuation warning method based on environmental perception and intelligent analysis according to any one of claims 1 to 8 are implemented.

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