A method and device for early flood warning through upstream and downstream linkage

Abstract

This invention relates to the field of flood early warning technology, and discloses a method and device for early flood warning issuance through upstream and downstream linkage. The method involves collecting upstream cross-sectional flow, average rainfall intensity across the basin, and reservoir discharge disturbance flow to calculate the flood source stress coefficient; collecting soil moisture content, downstream backwater level, and river base flow level to calculate the basin absorption resilience coefficient; collecting population density along the river, evacuation route congestion delay multiples, and communication base station outage rates to calculate the emergency response entropy increase coefficient; obtaining the difference between the dike crest elevation and the predicted highest water level, as well as dike seepage pressure monitoring values, and combining the flood source stress coefficient and the basin absorption resilience coefficient to calculate the dike engineering stress matching coefficient; and obtaining a comprehensive flood risk index based on the emergency response entropy increase coefficient and the dike engineering stress matching coefficient through a flood risk assessment model, issuing a warning when the threshold is exceeded. This invention achieves a linked assessment of upstream and downstream water conditions and engineering and social response capabilities, enabling the early and accurate issuance of flood warnings.

Description

Technical Field

[0001] This invention belongs to the field of flood early warning technology, and in particular relates to a method and device for early flood warning issuance through upstream and downstream linkage. Background Technology

[0002] Flood disaster early warning, as a crucial link in the flood control and disaster reduction system, directly impacts the safety of people's lives and property and socio-economic stability through its scientific rigor and timeliness. Current mainstream flood early warning technologies primarily rely on hydrological parameters such as water level and flow rate at single monitoring sections, employing fixed threshold triggering mechanisms to generate alarm signals. However, the actual evolution of floods is essentially a nonlinear dynamic system behavior driven by multiple dimensions of factors, including upstream inflow intensity, basin underlying surface storage capacity, river boundary constraints, the safety status of levee engineering, and social emergency response capabilities. Existing methods, focusing only on local hydrological station data, fail to establish a dynamic correlation model between upstream flood stress and downstream carrying capacity. This results in early warning signals lagging significantly behind the actual development of the risk, or generating false alarms in non-emergency situations, greatly diminishing the practical value and credibility of early warning systems. Specifically, traditional early warning frameworks have significant shortcomings: Early warning is limited to cross-sectional hydrological information, lacking a systematic consideration of the hydraulic linkages between upstream and downstream areas. This fails to capture the transmission effect of upstream water inflow changes on downstream risks, resulting in insufficient timeliness and limited spatial coverage. Furthermore, the risk assessment process does not incorporate the quantitative characterization of the basin's natural absorption capacity, such as the impact of pre-existing soil moisture content on rainwater infiltration efficiency and the dilution effect of river baseflow on floods. This makes it difficult for the system to effectively identify the essential differences between different risk scenarios, such as "strong stress – strong absorption" and "moderate stress – weak absorption." The current model suffers from several problems: the classification of flood risk levels is crude and lacks specificity; there is a lack of real-time dynamic matching mechanisms between the safety margin assessment of levee projects and upstream flood dynamic parameters. For example, indicators such as levee crest freeboard and seepage pressure changes are not coupled with flood stress intensity, making it impossible to accurately quantify the critical risk of levee breach; key variables in social emergency response capabilities, including population density along riverbanks, evacuation route access efficiency, and communication network reliability—core elements directly affecting disaster losses—are almost completely ignored in existing models, while these factors often become the dominant variables determining the vulnerability of disaster-bearing bodies in actual disasters. All these issues combined make it difficult for traditional early warning methods to fully characterize the complexity and dynamism of flood risk, failing to meet the urgent need for precise and forward-looking early warning in modern flood control and disaster reduction. Summary of the Invention

[0003] The purpose of this invention is to provide a method and apparatus for early flood warning issuance through upstream and downstream coordination, aiming to solve the aforementioned problems.

[0004] This invention is implemented as follows: a method for early flood warning issuance through upstream and downstream linkage, comprising: collecting upstream cross-sectional flow, average rainfall intensity across the basin, and reservoir discharge disturbance flow to calculate the flood source stress coefficient; collecting soil moisture content, downstream backwater level, and river base flow level to calculate the basin absorption resilience coefficient; collecting population density along the river, evacuation route congestion delay multiple, and communication base station outage rate to calculate the emergency response entropy increase coefficient; obtaining the difference between the dike crest elevation and the predicted highest water level elevation, and dike seepage pressure monitoring values, and combining the flood source stress coefficient and the basin absorption resilience coefficient to calculate the dike engineering stress matching coefficient; and obtaining a comprehensive flood risk index through a flood risk assessment model based on the emergency response entropy increase coefficient and the dike engineering stress matching coefficient, triggering a flood warning signal when the index exceeds a preset warning threshold.

[0005] A further technical solution involves calculating the flood source stress coefficient as follows: The upstream cross-sectional flow, the average rainfall intensity across the basin, and the reservoir discharge disturbance flow are obtained; the current upstream cross-sectional flow, average rainfall intensity across the basin, and reservoir discharge disturbance flow are compared with the cross-sectional warning flow, the local short-duration rainstorm warning threshold, and the downstream river channel safe discharge reference value, respectively. After limiting the upper limit of the ratio to 1, the upstream cross-sectional flow index, the average rainfall intensity index across the basin, and the reservoir discharge disturbance flow index are obtained; the upstream cross-sectional flow index, the average rainfall intensity index across the basin, and the reservoir discharge disturbance flow index are substituted into the formula... Obtain the flood source stress coefficient ,in, The upstream cross-sectional flow index. The average rainfall intensity index for the watershed area. The reservoir discharge disturbance flow index. , and All are Hongyuan coercion weights.

[0006] A further technical solution involves calculating the watershed absorption resilience coefficient as follows: Obtain the initial soil moisture content, downstream backwater level, and river baseflow level; ratio the difference between the current downstream backwater level and the multi-year average low water level to the difference between the critical water level causing a significant backwater effect and the multi-year average low water level, limiting the ratio to an upper limit of 1 to obtain the downstream backwater level index; ratio the difference between the current river baseflow level and the low water level to the difference between the floodplain water level and the low water level, limiting the ratio to an upper limit of 1 to obtain the river baseflow level index; substitute the initial soil moisture content, downstream backwater level index, and river baseflow level index into the formula... Obtain the watershed absorption resilience coefficient ,in, This refers to the initial soil moisture content. This is the downstream backwater level index. This refers to the baseflow water level index of the river channel.

[0007] A further technical solution involves calculating the emergency response entropy increase coefficient as follows: Obtain the population density along the river, the congestion delay multiple of the evacuation route, and the communication base station outage rate; compare the current population density along the river with the historical highest density, limiting the ratio to a maximum of 1, to obtain the population density index along the river; compare the difference between the current congestion delay multiple of the evacuation route and the baseline travel time multiple, with the difference between the congestion tolerance limit multiple and the baseline travel time multiple, limiting the ratio to a maximum of 1, to obtain the route congestion index; substitute the population density index along the river, the route congestion index, and the communication base station outage rate into the formula... Obtain the entropy increase coefficient of emergency response. ,in, The population density index for the riverside area. This refers to the route congestion index. For communication base station outage rate, , and All are entropy increase weights for emergency response. It is a very small positive number.

[0008] A further technical solution involves calculating the stress matching coefficient of the levee project as follows: Obtain the difference between the levee crest elevation and the predicted highest water level elevation, the levee seepage pressure monitoring value, the flood source stress coefficient, and the watershed absorption resilience coefficient; Ratio the current difference between the levee crest elevation and the predicted highest water level elevation to the levee's design safety superelevation, limiting the ratio to 0-1, to obtain the elevation difference index; Ratio the current levee seepage pressure monitoring value to the seepage pressure threshold at the time of historical emergencies, limiting the ratio to an upper limit of 1, to obtain the levee seepage pressure monitoring value index; Substitute the elevation difference index, the levee seepage pressure monitoring value index, the flood source stress coefficient, and the watershed absorption resilience coefficient into the formula. Obtain the stress matching coefficient of the dike project ,in, The flood source stress coefficient, The watershed absorption resilience coefficient. The elevation difference index, This is the index for monitoring seepage pressure in dikes.

[0009] A further technical solution is provided, wherein the flood risk assessment model is as follows:

[0010]

[0011] in, This is a comprehensive flood risk index. For the stress matching degree of the dike project, This is the entropy increase coefficient for emergency response.

[0012] A device for issuing early flood warnings in coordination with upstream and downstream areas, which applies the aforementioned method for issuing early flood warnings in coordination with upstream and downstream areas.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0014] 1. This invention establishes a dynamic matching relationship between the flood source stress coefficient and the watershed absorption resilience coefficient, thereby achieving a quantitative linkage between upstream flood stress and downstream carrying capacity. This enables early identification of risk evolution trends and significantly improves the foresight and timeliness of early warnings.

[0015] 2. This invention incorporates the safety status of dike engineering and the entropy increase coefficient of emergency response into the risk assessment model, comprehensively quantifying flood risk from three dimensions: hydrology, engineering, and society. This effectively overcomes the shortcomings of traditional single-threshold methods, which are prone to false alarms and missed alarms, and improves the accuracy and precision of early warning. Attached Figure Description

[0016] Figure 1 The flowchart illustrates a method for issuing early flood warnings through upstream and downstream coordination, as provided by this invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0019] like Figure 1 As shown, an embodiment of the present invention provides a method for early flood warning issuance through upstream and downstream coordination, comprising:

[0020] The system collects upstream cross-sectional flow, basin-wide average rainfall intensity, and reservoir discharge disturbance flow to calculate the flood source stress coefficient. Upstream cross-sectional flow can be collected through real-time monitoring using flowmeters or water level-discharge curves at key river sections. Basin-wide average rainfall intensity can be obtained through spatial interpolation or weighted averaging of data from multiple rain gauges deployed within the basin. Reservoir discharge disturbance flow can be provided directly by the reservoir management department or obtained by monitoring reservoir outflow data. These raw data are input into the calculation module. Through preset algorithmic logic, such as simple standardization of these parameters, a flood source stress coefficient representing the degree of flood threat is obtained through linear weighting or nonlinear function mapping. For example, when upstream flow, rainfall intensity, and reservoir discharge are all at high levels, the flood source stress coefficient will be calculated as a high value, indicating a greater upstream flood threat.

[0021] The system collects data on early soil moisture content, downstream backwater level, and river baseflow level to calculate the watershed's resilience coefficient. Early soil moisture content can be monitored in real-time using a network of soil moisture sensors or estimated through hydrological model simulation. Downstream backwater level can be monitored by installing level gauges at the downstream estuary or tidal-affected river sections. River baseflow level can be obtained by monitoring the stable water level of the river under conditions of no rainfall or runoff input. This data is input into the calculation module, where, through pre-defined algorithmic logic (e.g., standardizing these parameters and using a product or exponential decay function), a watershed resilience coefficient characterizing the basin's flood absorption capacity is obtained. For example, when soil moisture content, downstream backwater level, and river baseflow level are low, the watershed's absorption capacity is strong, and the watershed resilience coefficient will be calculated as a higher value.

[0022] The system collects data on population density along the river, congestion delay multiples of evacuation routes, and communication base station outage rates to calculate the emergency response entropy increase coefficient. Population density along the river can be dynamically estimated using a Geographic Information System (GIS) combined with census data and real-time population flow data. Congestion delay multiples of evacuation routes can be obtained through traffic monitoring systems, floating car data, or historical traffic pattern analysis. Communication base station outage rates can be obtained from network operation status data provided by telecommunications operators. This data is input into the calculation module, and through preset algorithmic logic—for example, standardizing these parameters—a weighted average or information entropy-based calculation method is used to obtain an emergency response entropy increase coefficient characterizing the vulnerability of the social emergency system. For example, when the riverside area is densely populated, evacuation routes are severely congested, and the communication base station outage rate is high, the emergency response entropy increase coefficient will be calculated as a higher value, indicating that the social emergency system is facing significant pressure.

[0023] The difference between the dike crest elevation and the predicted maximum water level, along with the dike seepage pressure monitoring value, is obtained. Combined with the flood stress coefficient and the watershed absorption resilience coefficient, the dike engineering stress matching coefficient is calculated. The difference between the dike crest elevation and the predicted maximum water level can be calculated using dike design data and real-time hydrological forecast data. The dike seepage pressure monitoring value can be monitored in real time using piezometers deployed inside the dike. These data, along with the previously calculated flood stress coefficient and watershed absorption resilience coefficient, are input into the calculation module. Through preset algorithm logic, for example, a nonlinear function reflecting the dynamic balance of load and resistance can be constructed to obtain a dike engineering stress matching coefficient characterizing the dike's safety status. For example, when the flood stress is strong, the watershed absorption is weak, and the dike crest elevation margin is small while the seepage pressure value is high, the dike engineering stress matching coefficient will be calculated as a higher value, indicating a greater risk to the dike.

[0024] Based on the emergency response entropy increase coefficient and the stress matching degree of the levee project, a comprehensive flood risk index is obtained through a flood risk assessment model. When the index exceeds a preset alarm threshold, a flood warning signal is triggered. The flood risk assessment model can be a multi-factor comprehensive assessment model, such as using fuzzy comprehensive evaluation, analytic hierarchy process (AHP), or machine learning models. This model uses the emergency response entropy increase coefficient and the levee project stress matching coefficient as the main inputs, and outputs a quantified comprehensive flood risk index through internal logical operations. When this index exceeds the preset alarm threshold, the system will automatically trigger a flood warning signal, for example, by issuing warning information to relevant management departments and the public through audible and visual alarms, SMS notifications, broadcast announcements, or network push notifications.

[0025] This multi-indicator comprehensive judgment mechanism integrates multiple factors such as flood source, watershed, engineering and society, and overcomes the shortcomings of traditional early warning methods in terms of timeliness, accuracy and foresight, providing more scientific and refined decision support for flood prevention and disaster reduction.

[0026] This application further proposes a method for calculating the flood source stress coefficient as follows:

[0027] The data collection includes upstream cross-sectional flow, basin-wide average rainfall intensity, and reservoir discharge disturbance flow. Upstream cross-sectional flow can be collected in real-time by hydrological monitoring stations located in the upstream river section, such as through ultrasonic flow meters or radar velocimeters; alternatively, it can be obtained through hydrological model simulation and prediction. Basin-wide average rainfall intensity can be calculated by fusing data from meteorological radar and surface rain gauge networks, or output from numerical weather prediction models. Reservoir discharge disturbance flow can be obtained from real-time discharge data from reservoir management departments, or calculated by monitoring reservoir gate opening and water level.

[0028] The current upstream cross-sectional flow, basin-wide average rainfall intensity, and reservoir discharge disturbance flow are compared with the cross-sectional warning flow, the local short-duration rainstorm warning threshold, and the downstream river channel safe discharge reference value, respectively. After applying a min function to limit the ratio to an upper limit of 1, the upstream cross-sectional flow index, basin-wide average rainfall intensity index, and reservoir discharge disturbance flow index are obtained. The cross-sectional warning flow, the local short-duration rainstorm warning threshold, and the downstream river channel safe discharge reference value are important reference benchmarks in the calculation of the flood source stress coefficient. These reference values ​​can be obtained by consulting flood control plans, hydrological manuals, or historical flood data published by local hydrological departments. These values ​​are usually fixed or dynamically adjusted thresholds determined based on historical experience, hydrological analysis, and flood control standards. Alternatively, these reference values ​​can be re-evaluated or optimized using methods such as hydrological frequency analysis and design flood calculations, combined with basin characteristics and flood control standards; and machine learning models can be used to dynamically adjust these thresholds based on historical data and expert experience to adapt to different scenarios. This step aims to transform raw data with different dimensions and physical meanings into dimensionless relative indicators for comprehensive comparison and calculation. This ratio processing can be performed by the data processing module or computing unit, for example, through division operations in a programming language. After ratio processing, a min function is used to limit the upper limit of the ratio to 1 to obtain the upstream cross-sectional flow index, the basin average rainfall intensity index, and the reservoir discharge disturbance flow index. The purpose of the min function limitation is that when the actual monitored values ​​far exceed the warning or threshold, the ratio may be greater than 1. However, for the assessment of flood source stress, once the warning level is reached or exceeded, the stress level is already very high. Continuing to linearly increase the ratio may lead to the model becoming overly sensitive to extreme situations or causing distortion. Limiting the upper limit to 1 ensures that the various indices are between 0 and 1, making the subsequent comprehensive calculation more stable and reasonable. This limiting operation can also be implemented through conditional judgment and assignment logic in the data processing module or computing unit.

[0029] Substituting the upstream cross-section flow index, the basin average rainfall intensity index, and the reservoir discharge disturbance flow index into the formula Obtain the flood source stress coefficient The square root part of the formula reflects the combined intensity of multiple stress sources, similar to the magnitude of a vector, and can capture the effect of multiple factors superimposed; while the max function part highlights the most important stress factor, ensuring that even if only one factor reaches an extreme, it can be fully reflected in the flood source stress coefficient. , This indicates that there was no coercion. This indicates that at least one index has reached an extreme and the overall strength is also extremely high, among which, The upstream cross-sectional flow index. The average rainfall intensity index for the watershed area. The reservoir discharge disturbance flow index. , and All are flood stress weights ranging from 0 to 1, and , , and The relative importance of upstream cross-sectional flow, basin-wide average rainfall intensity, and reservoir discharge disturbance flow in flood source stress can be adjusted based on the characteristics of different watersheds, historical flood experience, or expert experience, making the model more adaptable and accurate. Specifically, the flood source stress weights... , and The determination of the weights can be achieved using a combined weighting method: when the basin has more than 30 historical flood data, the entropy weighting method is used first, and objective weights are automatically generated by calculating the information entropy of each indicator (upstream cross-sectional flow, basin average rainfall intensity, and reservoir discharge disturbance flow) in the sample; when the data is insufficient or it is a newly developed basin, the analytic hierarchy process (AHP) is used, and at least 5 hydrological experts are invited to construct a judgment matrix using the 1-9 scale method and complete the consistency test to obtain subjective weights; finally, the two are combined in a linear weighting manner, and the resulting weights are dynamically updated every 3-5 years or after accumulating 20 new flood events.

[0030] This application's solution first acquires multi-dimensional upstream hydrological and meteorological data and corresponding reference thresholds. Then, it standardizes the real-time data into dimensionless indices and applies a reasonable amplitude limit using a min function. Finally, these indices are substituted into a nonlinear formula that considers both comprehensive intensity and extreme factors for calculation. This series of steps ensures that the calculation of the flood source stress coefficient is not only based on comprehensive data but also accurately quantifies the potential threat posed by upstream water conditions to downstream areas. In this way, the flood source stress coefficient can dynamically and accurately reflect the degree of upstream flood threat, providing reliable input for subsequent flood risk assessment. This effectively solves the problems of inaccurate or difficult-to-operate quantification of flood source stress in traditional methods. This precise quantification method enables earlier and more accurate identification of risks from upstream water inflows in the overall flood warning system, buying valuable time for downstream flood control decisions.

[0031] The following is a concrete example to illustrate this. Suppose that in a certain watershed, the upstream cross-section flow warning value is 1000 cubic meters per second, the local short-duration heavy rainfall warning threshold is 50 millimeters per hour, and the downstream river channel safe discharge reference value is 800 cubic meters per second. Currently, the monitored upstream cross-section flow is 1500 cubic meters per second, the average rainfall intensity of the watershed is 60 millimeters per hour, and the reservoir discharge disturbance flow is 400 cubic meters per second.

[0032] First, perform ratio processing:

[0033] The upstream cross-sectional flow ratio = 1500 / 1000 = 1.5

[0034] The ratio of average rainfall intensity over the watershed area = 60 / 50 = 1.2

[0035] The ratio of reservoir discharge disturbance flow to total discharge flow is 400 / 800 = 0.5

[0036] Next, the min function is used to limit the amplitude ratio to an upper limit of 1, and the various indices are obtained:

[0037] Upstream section flow index =min(1.5,1)=1

[0038] Average rainfall intensity index of watershed area =min(1.2,1)=1

[0039] Reservoir Discharge Disturbance Flow Index =min(0.5,1)=0.5

[0040] Assuming that, based on historical experience and expert judgment, the coercive weights of floodwaters are respectively... =0.4 (flow rate) =0.4 (rainfall) =0.2 (flood discharge).

[0041] Substituting these indices and weights into the formula for the flood source stress coefficient:

[0042]

[0043] The flood source stress coefficient is obtained through the above calculations. This indicates that the current upstream flood stress level is extremely high, approaching an extreme state. The entire calculation process can be automatically executed by the flood warning system software module deployed on data centers or edge computing devices, receiving monitoring data in real time and outputting the flood stress coefficient.

[0044] Through the above technical solution, this application provides a systematic and quantitative method for calculating the flood source stress coefficient, solving the problem of inaccurate and incomplete assessment of upstream water conditions in traditional early warning methods. This method can uniformly quantify multi-source heterogeneous upstream hydrological and meteorological data into a flood source stress coefficient between 0 and 1, allowing for a comprehensive consideration of the contributions of different stress factors and effectively avoiding the excessive influence of a single extreme event on the assessment results. This precise quantification capability enables flood risk assessment models to more accurately capture the dynamic changes in upstream flood threats, thereby significantly improving the scientific rigor and foresight of flood early warnings and providing a more reliable basis for flood control and disaster reduction decision-making.

[0045] This application further proposes a method for calculating the watershed absorption resilience coefficient as follows:

[0046] The study focuses on obtaining soil pre-flood moisture content, downstream backwater level, and river baseflow level. Soil pre-flood moisture content refers to the amount of water contained in the soil within the watershed before a flood event. This parameter directly reflects the soil's saturation level and its ability to absorb subsequent rainfall and runoff. It can be obtained through real-time monitoring using a surface soil moisture sensor network or through simulation estimation using a hydrological model combined with historical rainfall and evaporation data. Downstream backwater level refers to the rise in water level caused by the obstruction of upstream flow by downstream river channels or water bodies. This parameter reflects the downstream water body's capacity to withstand upstream flood discharge or runoff. It can be obtained through real-time monitoring of water levels at downstream river or lake monitoring stations or through simulation calculations of water levels under specific boundary conditions using hydrodynamic models. River baseflow level refers to the lowest water level in a river channel maintained by groundwater recharge in the absence of rainfall or runoff input. This parameter reflects the river channel's normal carrying capacity. It can be obtained through long-term observation and averaging at river channel monitoring stations or through simulation using a coupling of groundwater and river hydrological models.

[0047] The downstream backwater level index is obtained by comparing the difference between the current downstream backwater level and the multi-year average low water level with the difference between the critical water level causing a significant backwater effect and the multi-year average low water level, and then using a min function to limit the ratio to an upper limit of 1. The multi-year average low water level refers to the average lowest water level of a river or water body obtained from multi-year hydrological observation data. This parameter serves as a benchmark reference for assessing the downstream backwater level. It can be obtained through statistical analysis of long-term water level data from historical hydrological stations, or by consulting official documents such as hydrological yearbooks and hydrological handbooks. The critical water level causing a significant backwater effect refers to the specific water level at which the downstream water body will exert a significant backwater effect on the upstream water flow, thereby affecting upstream flood discharge or runoff discharge. This parameter is a key threshold for quantifying the intensity of the backwater effect. It can be obtained through historical flood event analysis, hydrodynamic model simulation, or by combining expert experience with on-site surveys. The downstream backwater level index is a quantitative indicator of the downstream backwater level relative to its critical state, reflecting the intensity of the downstream backwater effect.

[0048] The baseflow level index is obtained by comparing the difference between the current baseflow level and the low-water level with the difference between the floodplain level and the low-water level, and then using a min function to limit the ratio to an upper limit of 1. The low-water level refers to the lowest water level a river can reach under conditions of prolonged absence of rainfall or runoff. This parameter serves as a benchmark for assessing the baseflow level. It can be obtained by statistically analyzing long-term water level data from historical hydrological stations and selecting the lowest water level during the low-water season, or by simulating the low-water season scenario using a hydrological model. The floodplain level refers to the water level at which the river channel just fills and begins to overflow the riverbank. This parameter reflects the maximum carrying capacity of the river channel without flooding. It can be obtained by combining river cross-section measurement data with hydraulic calculations, or by using historical flood data and hydrological survey reports. The baseflow level index is a quantitative indicator of the river's baseflow level relative to its maximum carrying capacity, reflecting the degree of utilization of the river's normal carrying capacity.

[0049] Ratio processing refers to dividing two values ​​to obtain a relative proportion or exponent. For example, it compares the current observation with a baseline or threshold to quantify the degree of the current state relative to the baseline state. This processing can use simple division, or, in specific cases, to highlight the trend, it can use forms such as logarithmic ratios. The min function's upper limit of 1 for the ratio means that after ratio processing, if the calculated result is greater than 1, it is forcibly set to 1; if it is less than or equal to 1, the original value is retained. The purpose of this operation is to normalize the ratio result to the range of 0 to 1, ensuring its validity as an exponent and avoiding distortion in subsequent calculations due to extreme values.

[0050] Substitute the soil pre-current moisture content, downstream backwater level index, and river baseflow level index into the formula. Obtain the watershed absorption resilience coefficient Watershed absorption resilience coefficient It is a dimensionless index between 0 and 1, used to comprehensively assess a watershed's capacity to absorb and regulate flood runoff. This indicates that the watershed is in the optimal absorption state, that is, the soil is not saturated, there is no backwater downstream, and the base flow level of the river is low; This indicates that the watershed's absorption capacity has completely failed, meaning the soil is saturated, there is severe backwater downstream, or the river's baseflow level is high. Among these... This refers to the initial soil moisture content. This is the downstream backwater level index. This refers to the baseflow water level index of the river channel. This indicates the unsaturated, available infiltration capacity of the soil. This indicates drainage conditions without a jack downstream. This represents the capacity of the river channel at its low baseflow level. The product of these three terms reflects the overall absorption capacity of the basin, and the formula is smoothed by taking the cube root to balance the influence of each factor. This formula can be calculated using any computing platform or programming language that supports mathematical operations.

[0051] This application proposes a watershed resilience coefficient to quantify the watershed's capacity to bear and regulate flood runoff, thereby addressing the shortcomings of traditional flood warning methods that fail to adequately consider the watershed's underlying surface storage capacity. The coefficient is calculated by first obtaining three core parameters: soil anterior moisture content, downstream backwater level, and river baseflow level. These represent the watershed's infiltration capacity, downstream drainage conditions, and the river's own capacity, respectively. To transform these physical quantities into comparable dimensionless indices, this scheme introduces multiple reference water levels, including the multi-year average low water level, the critical water level causing a significant backwater effect, the low water level, and the flat water level. By comparing the current observed values ​​with these reference values ​​and using a min function to limit the upper limit of the ratio to 1, the downstream backwater level index and the river baseflow level index are standardized and generated. This standardization ensures that the weights and influence ranges of different physical quantities are consistent in subsequent calculations, avoiding evaluation biases caused by dimensional differences. Subsequently, the soil anterior moisture content... Downstream backwater level index and river base flow level index Substituting specific mathematical formulas for comprehensive calculations. This formula cleverly utilizes... , and The product of these factors reflects the overall absorption capacity of the watershed. Among them, This represents the soil's remaining water absorption capacity; the higher the value, the drier the soil and the stronger its water absorption capacity. This represents the degree to which the downstream backing effect is weakened; a larger value indicates smoother downstream drainage. This represents the degree of low water level in the river baseflow; a higher value indicates a larger usable water storage capacity in the river. The product of these three factors is then cubed, ensuring that any deterioration of any factor (i.e., the corresponding exponent approaching 1) will affect the overall water absorption resilience coefficient. A significant decrease, until approaching 0, accurately reflects the overall failure of the basin's absorption capacity. Conversely, when all factors are at their optimal state, A value approaching 1 indicates that the basin possesses extremely strong absorption capacity. In this way, this scheme simplifies complex basin hydrological processes into a single, quantifiable absorption resilience coefficient. This coefficient effectively distinguishes the risk differences under different scenarios such as "strong stress-strong absorption" and "moderate stress-weak absorption." When this basin absorption resilience coefficient is combined with the flood source stress coefficient, a more comprehensive assessment of flood risk can be achieved. For example, even with strong flood source stress, a high basin absorption resilience coefficient may result in relatively low overall risk; conversely, a low basin absorption resilience coefficient may lead to high flood risk even with moderate flood source stress. This comprehensive assessment mechanism provides a more scientific and refined basis for issuing early flood warnings.

[0052] The following is a concrete example. Suppose we are in a specific watershed. To calculate its watershed resilience coefficient, we first need to acquire a series of real-time and historical data. For example, the initial soil moisture content can be collected in real time through a network of multiple soil moisture sensors deployed within the watershed. These sensors can use capacitive or time-domain reflectometry (TDR) principles, transmitting the data to a data center via wireless communication modules. The downstream backwater level and the baseflow level can be obtained in real time through ultrasonic or radar level gauges installed in the downstream river section and main river cross-sections. These level gauges also transmit data to a central processing system. Reference values ​​such as the multi-year average low water level, the critical water level causing a significant backwater effect, the low water level, and the flat water level can be determined in advance through statistical analysis of hydrological observation data from the past few decades of the watershed, combined with hydraulic engineering design data and the experience of hydrological experts, and stored in a database. In the specific calculation, we assume the currently monitored downstream backwater level is A meters, the multi-year average low water level is B meters, and the critical water level causing a significant backwater effect is C meters. The calculation process for the downstream backwater level index is as follows: First, calculate the difference (AB) between the current downstream backwater level and the multi-year average low water level. Then, calculate the difference (CB) between the critical water level causing a significant backwater effect and the multi-year average low water level. Next, calculate the ratio of these two values: (AB) / (CB). Finally, use the min function to limit the upper limit of this ratio to 1. For example, if the calculated result is 1.2, then take 1; if it is 0.8, then take 0.8, thus obtaining the downstream backwater level index. Similarly, assuming the current monitored baseflow level is D meters, the low water level is E meters, and the floodplain level is F meters, the calculation process for the baseflow level index is as follows: First, calculate the difference (DE) between the current baseflow level and the low water level, then calculate the difference (FE) between the floodplain level and the low water level. Next, calculate the ratio of these two values: (DE) / (FE). Finally, use the min function to limit the upper limit of this ratio to 1 to obtain the baseflow level index. Assume the current soil moisture content in the early stages is... (For example, 0.6 indicates a soil moisture content of 60%). Finally, the calculated... , and Substitute into the formula The watershed absorption resilience coefficient can then be obtained. For example, if =0.6, =0.5, =0.3, then The calculated value is 0.519. This calculation process can be completed automatically in an integrated hydrological data processing platform or early warning system.

[0053] Through the above technical solution, this application provides a precise method for quantifying the watershed absorption resilience coefficient, solving the problem of the difficulty in comprehensively assessing the watershed's underlying surface storage capacity in traditional flood warning systems. This method comprehensively considers key factors such as pre-soil moisture content, downstream backwater level, and river baseflow level, standardizing them into dimensionless indices, and then integrating them through a scientific mathematical model to accurately reflect the watershed's capacity to bear and regulate flood runoff. This allows the flood warning system to more precisely differentiate risk differences under different flood stress and absorption scenarios. For example, under the same flood source stress, areas with high watershed absorption resilience have lower risk, while areas with low absorption resilience have higher risk. This quantitative assessment capability significantly improves the accuracy and foresight of flood risk assessment, providing decision-makers with more reliable data, thereby enabling more effective flood control and disaster reduction strategies, avoiding warning delays or false alarms caused by misjudgments based on single hydrological indicators, and ultimately enhancing the scientific rigor and practicality of the entire flood warning system.

[0054] This application further proposes a method for calculating the entropy increase coefficient of emergency response as follows:

[0055] The data collection includes population density along the river, congestion delay multiple of evacuation routes, and communication base station outage rate. Population density along the river refers to the number of people per unit area within a specific riverside region, reflecting the potential evacuation pressure the area may face during floods. This can be obtained through spatial analysis using Geographic Information Systems (GIS) combined with census data, or through real-time estimation using anonymized and aggregated population heatmap data provided by mobile communication operators. The congestion delay multiple of evacuation routes is the ratio of the current actual travel time to the baseline travel time (usually the travel time under unobstructed conditions) on a specific evacuation route, used to quantify the degree of traffic congestion. This data can be obtained through real-time traffic monitoring systems (such as cameras and inductive loop detectors), floating car data provided by vehicle navigation systems, or traffic flow analysis based on mobile phone signaling data. The communication base station outage rate is the proportion of communication base stations that cease service due to disasters (such as power outages or equipment damage) within a specific area, reflecting the vulnerability of the communication system during disasters. This data can be provided in real time by the telecommunications operator's back-end monitoring system, or it can be statistically analyzed through on-site inspections, user feedback, and other methods.

[0056] The population density index of the riverside area is obtained by comparing the current population density with the historical highest density and then using a min function to limit the ratio to an upper limit of 1. The historical highest density refers to the highest population density value that has ever occurred in a specific riverside area in historical records, serving as a standardized benchmark for calculating the population density index. This value can be obtained through analysis and statistics of historical census data, urban planning data, or long-term population monitoring data. This step aims to standardize the raw population density data into a dimensionless index, allowing it to vary between 0 and 1, to facilitate subsequent comprehensive calculations. By comparing it with the historical highest density, the current population density can be objectively reflected relative to historical extremes. Using a min function to limit the ratio to an upper limit of 1 ensures that when the current population density exceeds the historical highest density, the index will not increase indefinitely but will stabilize at 1, indicating that the item has reached its maximum stress state, thus avoiding the excessive influence of abnormally high values ​​on the overall assessment. This process can be completed by a data processing module or a computing unit.

[0057] The route congestion index is obtained by comparing the difference between the current congestion delay multiple and the baseline travel time multiple with the difference between the congestion tolerance limit multiple and the baseline travel time multiple, and then using a min function to limit the ratio to a maximum of 1. The baseline travel time multiple refers to the ratio of the travel time of the evacuation route under ideal traffic conditions (no congestion) to a certain standard time unit (e.g., one hour), serving as a reference point for calculating the route congestion index. This value can be obtained through road design parameters from transportation planning departments, historical traffic flow data analysis, or actual road tests. The congestion tolerance limit multiple refers to the upper limit at which the traffic congestion delay multiple on the evacuation route reaches a certain critical value, considered to have seriously affected evacuation efficiency and may even lead to traffic paralysis. This value can be set based on traffic engineering theory, expert experience assessment, or historical disaster evacuation case analysis. This step is used to quantify the congestion level of the evacuation route and standardize it to an index from 0 to 1. By calculating the difference between the current congestion delay and the baseline travel time, the actual delay can be obtained. The ratio of this to the congestion tolerance limit delay (the difference between the congestion tolerance limit multiple and the baseline travel time multiple) reflects the relative degree of congestion from the traffic paralysis threshold. The min function's limit ratio is capped at 1, ensuring that when congestion reaches or exceeds the tolerance limit, the exponent stabilizes at 1, indicating complete traffic system failure and preventing unbounded growth of the calculation results. This process can also be completed by the data processing module or computing unit.

[0058] Substitute the population density index, route congestion index, and communication base station outage rate of the riverside area into the formula. Obtain the entropy increase coefficient of emergency response. , , A value close to 0 indicates that the social emergency response system is fully effective and operates without any obstacles. A value close to 1 indicates that the system is nearly inoperable due to dense population, traffic paralysis, and communication disruptions, and disaster response has fallen into a state of disorder and out of control. The population density index for the riverside area. This refers to the route congestion index. For communication base station outage rate, , and All are emergency response entropy increase weights with values ​​ranging from 0 to 1, and ; For a very small positive number, such as Its purpose is to prevent the denominator from being zero, ensure the stability of the calculation, and avoid mathematical singularities. This step is the core of calculating the emergency response entropy increase coefficient, aiming to use a nonlinear function to transform the standardized population density index... Route congestion index and communication base station outage rate In summary, this formula, employing an exponential function, can simulate the transition of a social emergency system from an ordered ( From near 0) to disordered disability ( The evolution process is close to nonlinear, similar to step 1). This formula yields a continuous value between 0 (inclusive) and 1 (exclusive), intuitively reflecting the entropy increase of the social emergency response system, i.e., its trend from effectiveness to incompetence. Emergency Response Entropy Increase Weighting , and The weights are used to adjust the relative importance of population density, traffic congestion, and communication disruption in emergency response failures, and their sum is 1, ensuring a reasonable allocation of weights. This is the emergency response entropy increase weight. , and The recommended approach for determining the risk level is the historical inversion optimization method: collect data on population density index, route congestion index, communication base station outage rate, and corresponding actual disaster losses (such as casualty rate and number of people trapped) from at least 20 historical flood events within the basin. Perform multiple regression analysis on the three indices with loss as the dependent variable, and normalize the absolute values ​​of the regression coefficients to obtain the weights of each factor. If loss data is lacking, the fuzzy hierarchical analysis method (with triangular fuzzy numbers for expert judgment) can be used instead, or Monte Carlo simulation can be used to traverse a large number of weight combinations, selecting the weight that has the highest correlation between the comprehensive flood risk index and the actual disaster level as the final value. It is also recommended that the weights be calibrated regularly.

[0059] This application's scheme aims to accurately quantify the failure trends of social emergency systems during flood disaster response by constructing a computational mechanism for the entropy increase coefficient of emergency response, thereby dynamically and comprehensively reflecting the vulnerability of disaster-bearing entities in flood warnings. The mechanism first acquires key real-time indicators such as population density along riverbanks, evacuation route congestion delay multiples, and communication base station outage rates through multi-source data collection. Simultaneously, it obtains historical maximum density, baseline travel time multiples, and congestion tolerance upper limit multiples as standardized reference benchmarks. These data form the basis for assessing the state of the social emergency system, ensuring the comprehensiveness and objectivity of the assessment. Based on this, the system standardizes the real-time collected population density along riverbanks and evacuation route congestion delay multiples. Specifically, the population density along riverbanks is converted into a population density index by comparing it with the historical maximum density and applying a min function limit. This index directly reflects the pressure of the current population density relative to historical extremes. Similarly, the evacuation route congestion delay multiple is processed by comparing and ratioing it to the baseline travel time multiple and the congestion tolerance limit multiple, and then constrained by a min function to transform it into a route congestion index. This index accurately characterizes the impact of traffic congestion on evacuation efficiency. These standardization steps ensure that data of different types and dimensions can be unified on a comparable scale, laying the foundation for subsequent comprehensive assessment. Subsequently, the standardized population density index, route congestion index, and communication base station outage rate of the riverside area are substituted into a specific nonlinear mathematical formula to calculate the emergency response entropy increase coefficient. This formula cleverly integrates the impact of three core elements—population, transportation, and communication—on the stability of social emergency response systems. It uses weighting coefficients... , and The setting allows for flexible adjustment of the relative contributions of various factors in the entropy increase process. This exponential function design enables the emergency response entropy increase coefficient to simulate the system's transition from fully efficient (…) From near 0 to almost disabled ( The nonlinear dynamic change process, closer to 1), rather than a simple linear superposition, more realistically reflects the degradation trend of social emergency response capabilities under disaster scenarios. Ultimately, the obtained emergency response entropy increase coefficient... As a crucial input parameter in the flood risk assessment model, it works in conjunction with other factors such as the stress matching coefficient of levee engineering to calculate the comprehensive flood risk index. This approach, which quantifies social emergency response capabilities and integrates them into the overall risk assessment framework, ensures that flood warnings no longer focus solely on hydrological and engineering factors, but comprehensively consider the vulnerability of disaster-bearing entities, thereby providing more forward-looking and instructive early warning information. In this way, the proposed solution effectively addresses the difficulty in quantifying the failure trends of social emergency response systems in traditional early warning methods, improving the accuracy and comprehensiveness of flood risk assessment.

[0060] The following example illustrates this. Suppose that a riverside area, during a certain period, obtains the following data through a real-time monitoring system: the current population density of the riverside area is 1500 people / square kilometer, the evacuation route congestion delay multiple is 2.5, and the communication base station outage rate is 0.1 (i.e., 10% of base stations are out of service). Meanwhile, based on historical data and expert settings, the historical highest population density of this area is 2000 people / square kilometer, the baseline travel time multiple is 1.0, and the congestion tolerance limit multiple is 3.0. First, calculate the population density index of the riverside area. : Secondly, calculate the route congestion index. : Communication base station outage rate The value is directly set to 0.1. Then, assuming a weighting coefficient... =0.4 (population density) =0.4 (Route congestion) =0.2 (communication service outage), and Substitute these indices into the formula for calculating the entropy increase coefficient of emergency response: First, calculate the weighted sum in the numerator: Substitute into the formula: The final calculated emergency response entropy increase coefficient The value is approximately 0.804. This value is close to 1, indicating that in this scenario, the social emergency response system is highly dysfunctional due to the combined effects of dense population, traffic congestion, and communication disruptions, facing a significant risk of disorder and loss of control in disaster response. This coefficient will then be input into the flood risk assessment model as a key component in evaluating the comprehensive flood risk index, thereby triggering the corresponding warning level.

[0061] Through the above technical solution, this application provides a precise and dynamic method for calculating the entropy increase coefficient of emergency response, effectively solving the problem of difficulty in quantifying the failure trend of social emergency systems in traditional flood warnings. This solution comprehensively considers multiple key social vulnerability indicators, such as population density along riverbanks, evacuation route congestion delay multiples, and communication base station outage rates, and standardizes them into dimensionless indices, avoiding assessment biases caused by different data sources and dimensions. Furthermore, by introducing a nonlinear mathematical model to organically integrate these indices, it can accurately simulate the entropy increase process of social emergency systems from order to disorder, rather than a simple linear superposition, thus more realistically reflecting the degradation trend of social emergency capabilities under disaster scenarios. This quantification method enables flood warning systems to incorporate the vulnerability of disaster-bearing entities into the risk assessment framework, compensating for the shortcomings of traditional warnings that only focus on hydrological and engineering factors, making the calculation of the comprehensive flood risk index more comprehensive and accurate. Therefore, this solution can significantly improve the scientific rigor, foresight, and guiding significance of flood warnings, providing a more reliable basis for flood prevention and disaster reduction decision-making.

[0062] This application further proposes a method for calculating the stress matching coefficient of dike engineering as follows:

[0063] The following parameters are obtained: the difference between the dike crest elevation and the predicted maximum water level, the dike seepage pressure monitoring value, the flood stress coefficient, and the watershed absorption resilience coefficient. The difference between the dike crest elevation and the predicted maximum water level refers to the height difference between the actual elevation of the dike crest and the predicted maximum flood level. This difference can be obtained by using a real-time hydrological monitoring system to obtain the predicted maximum water level, combined with dike engineering design data or on-site measurement data to obtain the dike crest elevation, and then performing calculations; or by using a Geographic Information System (GIS) combined with a Digital Elevation Model (DEM) and hydrodynamic model simulation results for spatial analysis. The dike seepage pressure monitoring value refers to the seepage pressure generated inside or at the bottom of the dike under water pressure. This monitoring value can be collected in real time by deploying sensors such as piezometers and pore water pressure gauges inside the dike; or it can be estimated using groundwater monitoring well water level data combined with hydraulic principles. The flood stress coefficient and the watershed absorption resilience coefficient are obtained through the above calculations.

[0064] The elevation difference index is obtained by comparing the difference between the current dike crest elevation and the predicted highest water level with the design safety freeboard of the dike. The ratio is then limited to 0-1 using max and min functions. The design safety freeboard of the dike is typically derived from the dike engineering design specifications, construction drawings, or as-built data. This step aims to standardize the raw elevation difference data into a dimensionless index, allowing it to be used in comprehensive calculations with other indicators. The ratio processing visually reflects the proportion of the current freeboard relative to the design safety standard. Limiting the ratio using max and min functions, for example, restricting it to between 0 and 1, effectively avoids the influence of extreme values ​​on subsequent calculations, ensuring the rationality and stability of the index. When the ratio is less than 0, it indicates that the predicted water level has exceeded the dike crest, and the index can be set to 1; when the ratio is greater than 1, it indicates sufficient freeboard, and the index can be set to 0.

[0065] The current seepage pressure monitoring value of the dike is compared with the seepage pressure threshold at the time of historical emergencies. A min function is used to limit the ratio to an upper limit of 1 to obtain the dike seepage pressure monitoring value index. The seepage pressure threshold at the time of historical emergencies can be determined by reviewing historical flood control and rescue records, engineering accident reports, or expert experience. The purpose of this step is to transform the real-time seepage pressure monitoring value into a standardized index to quantify the current seepage risk. By comparing it with the historical emergency threshold, the degree of danger of the current seepage pressure level from the occurrence of an emergency can be assessed. Using a min function with an upper limit of 1 ensures that when the current seepage pressure value exceeds the historical emergency threshold, the index is at most 1, indicating that the seepage risk has reached or exceeded the historical danger level, preventing unlimited increase and ensuring the rationality of subsequent calculations.

[0066] Substituting the elevation difference index, the levee seepage pressure monitoring value index, the flood source stress coefficient, and the watershed absorption resilience coefficient into the formula Obtain the stress matching coefficient of the dike project This step is the core of calculating the stress matching coefficient of the levee project, and it uses a nonlinear formula to calculate the flood source stress coefficient. Watershed absorption resilience coefficient Difference index between dike crest elevation and predicted highest water level and the levee seepage pressure monitoring value index Synthesize the results. The numerator of this formula... This can be understood as a comprehensive load term, reflecting the pressure of upstream water flow on the dikes and the risks caused by insufficient watershed absorption capacity; the denominator includes both load and resistance terms. , and These represent the structural safety margin and seepage stability of the dike, respectively. Through this ratio, the coefficient dynamically reflects the degree of matching between the comprehensive load borne by the dike and its own resistance, thus quantitatively characterizing the risk of dike failure. When the load is much smaller than the resistance, A value approaching 0 indicates that the levee is safe; when the load and resistance are equivalent, A value close to 0.5 indicates a critical state; when the load is much greater than the resistance, A value approaching 1 indicates that the levee faces an extremely high risk of collapse. (Very small load or extremely strong resistance). This indicates that the dike is far from reaching its bearing capacity. (Load equals resistance) This indicates a critical equilibrium state, when (The load far exceeds the resistance). A value approaching 1 indicates that the levee is overburdened and at extremely high risk of collapse. The flood source stress coefficient, The watershed absorption resilience coefficient. The elevation difference index, This is the index for monitoring seepage pressure in dikes.

[0067] This application's scheme dynamically assesses the bearing capacity of levees by acquiring key parameters of the levee project and combining upstream hydrodynamic stress and watershed absorption resilience. First, the system collects data on the difference between the levee crest elevation and the predicted maximum water level, levee seepage pressure monitoring values, levee design safety freeboard, and seepage pressure thresholds at historical hazard points. These data represent the levee's structural safety margin, seepage stability, design standards, and historical experience, respectively. Next, by comparing the current levee crest elevation with the predicted maximum water level and the levee design safety freeboard, and limiting the range, an index is obtained for the difference between the levee crest elevation and the predicted maximum water level. This index quantifies the relative safety of the levee's freeboard margin at the current predicted water level. Simultaneously, by comparing the current levee seepage pressure monitoring values ​​with the seepage pressure thresholds at historical hazard points and limiting the range, a levee seepage pressure monitoring value index is obtained. This index reflects the real-time status of the levee's seepage risk. After obtaining these standardized indices, this scheme uses the flood source stress coefficient... and watershed absorption resilience coefficient (These two coefficients quantify the pressure of upstream water flow on downstream areas and the basin's ability to regulate floods, respectively, and have been calculated using the methods described above.) And the two dike self-state indices mentioned above. and Substituting into a specific nonlinear formula, the stress matching coefficient of the dike project is calculated. This formula cleverly incorporates the external flood load (from...) and (jointly decided) and the dike's own resistance (by and (Jointly decided) to achieve dynamic balance, forming a comprehensive risk assessment index. When the external load increases or the dike resistance weakens, the stress matching coefficient of the dike project... A corresponding increase in the value indicates a higher risk of dike breach; conversely, a decrease indicates a lower risk. This calculation method not only considers the static design parameters of the dike but also incorporates dynamic hydrological and meteorological conditions and watershed response capabilities, making the assessment of dike safety status more comprehensive and accurate. Through this mechanism, this scheme can transform the complex flood evolution process and dike response behavior into an intuitive quantitative indicator, providing crucial input for subsequent flood risk assessment.

[0068] As a specific implementation method, the above-mentioned technical means can be implemented with reference to the following example. In practical applications, an integrated monitoring and computing system can be deployed. This system may include: hydrological sensors such as water level gauges, rain gauges, and flow meters, used to collect data in real time such as upstream cross-sectional flow, average rainfall intensity of the watershed, reservoir discharge disturbance flow, downstream backwater level, and river base flow level; seepage gauges are installed inside the dike to monitor the seepage pressure monitoring value of the dike in real time; at the same time, the system database stores basic data and historical data such as the design safety freeboard of the dike, seepage pressure thresholds when historical emergencies occur, cross-sectional warning flow, local short-duration rainstorm warning thresholds, reference values ​​for safe discharge of downstream river channels, multi-year average low water level, critical water level causing significant backwater effect, low water level, and flat water level. When the system receives real-time monitoring data, a central processing unit (e.g., a high-performance industrial computer or cloud server) will first calculate the flood source stress coefficient according to a preset algorithm. and watershed absorption resilience coefficient Subsequently, the processing unit extracts the difference between the current dike crest elevation and the predicted highest water level elevation, and then compares this difference with the design safety freeboard of the dike in the database. For example, if the current elevation difference is 1.5 meters and the design safety freeboard is 2 meters, the ratio is 0.75. If the predicted water level has exceeded the dike crest, the elevation difference is negative, and the ratio is limited to 1 after processing. Simultaneously, the processing unit obtains the current dike seepage pressure monitoring value, for example, 0.8 MPa, and compares it with the historical seepage pressure threshold (for example, 0.7 MPa) stored in the database when a hazard occurred, obtaining a ratio of approximately 1.14, which is then limited to 1 using a min function. Finally, the calculated flood source stress coefficient is... Watershed absorption resilience coefficient Difference index between dike crest elevation and predicted highest water level and the levee seepage pressure monitoring value index By substituting the values ​​into the preset formula for calculating the stress matching coefficient of the dike project, the stress matching coefficient of the dike project can be obtained in real time. For example, if the calculation result is 0.7, it indicates that the dike faces a high risk of breach.

[0069] Through the above technical solution, this application overcomes the shortcomings of traditional methods in assessing dike safety by failing to fully consider the dynamic matching between upstream hydrodynamic stress and the dike's own resistance. This solution introduces an index of the difference between the dike crest elevation and the predicted highest water level, along with an index of dike seepage pressure monitoring values, and combines this with the flood source stress coefficient and the watershed absorption resilience coefficient to construct a comprehensive dike engineering stress matching coefficient. This coefficient can accurately quantify the dike's bearing capacity under different flood scenarios, dynamically reflecting the balance between the comprehensive load borne by the dike and its own resistance, thereby achieving a quantitative characterization of dike breach risk. This enables flood warning systems to identify dike hazards earlier and more accurately, providing a scientific basis for flood control decisions and significantly improving the precision and foresight of flood warnings.

[0070] This application further proposes a flood risk assessment model as follows:

[0071]

[0072] in, This is a comprehensive flood risk index. For the stress matching degree of the dike project, This is the entropy increase coefficient for emergency response.

[0073] This flood risk assessment model is a mathematical model used to quantify the potential impact and probability of flood disasters. Its concept lies in comprehensively considering multiple influencing factors to output a unified risk index. Its implementation can include: a qualitative assessment model based on expert experience, using weight allocation and fuzzy logic to classify risk levels; or a quantitative prediction model based on statistical data and machine learning algorithms, using historical data for training to predict the risk index. The comprehensive flood risk index is the final output of this method, a quantitative indicator used to measure the current level of flood risk. Its domain is typically limited to a specific range to facilitate comparisons under different scenarios and the setting of warning thresholds. This index can be expressed as the probability of risk occurrence, risk level, or risk intensity. The stress matching degree of a levee is an indicator that measures the degree of matching between the structural safety state of a levee and the flood load it bears. Its value typically ranges from 0 to 1, where 0 indicates a high safety margin and 1 indicates the levee is in its ultimate bearing capacity or has failed. This indicator can be calculated based on engineering parameters such as the levee's geometric dimensions, material strength, hydraulic conditions (e.g., water level, flow velocity), and seepage conditions (e.g., seepage pressure). The emergency response entropy increase coefficient is an indicator that measures the efficiency and orderliness of a social emergency system in the face of flood disasters. Its value typically ranges from 0 to 1, where 0 indicates a highly efficient and orderly emergency system, and 1 indicates a system nearing inoperability or in a state of disorder. The calculation of this indicator can comprehensively consider social factors such as population density, traffic congestion, communication capabilities, and accessibility of medical resources. The min function is a mathematical function used to select the minimum value from a set of values. In this scheme, min(1,X) limits the calculated result X to within the maximum value of 1, ensuring that the comprehensive flood risk index does not exceed the preset upper limit. This is the core component of the comprehensive flood risk index calculation. It multiplies the stress matching degree of the levee engineering with the emergency response entropy increase coefficient and then weights the latter. This combination aims to reflect that flood risk depends not only on the safety status of the engineering structure but also on the social emergency response capability. When the emergency response capability declines ( When the stress matching degree of the dike project is increased, even if the stress matching degree is increased... If things remain unchanged, the overall risk will be amplified.

[0074] The flood risk assessment model proposed in this application incorporates the stress matching degree of levee engineering. and emergency response entropy increase coefficient And using specific mathematical formulas To calculate the comprehensive flood risk index The model works by first determining the stress matching degree of the levee project. The safety margin of the dikes under the current flood load was quantified. A high value indicates a significant risk of dike breach. Secondly, the emergency response entropy increase coefficient... It quantifies the degree of dysfunction of social emergency response systems during disasters. When A high value indicates severe impairment of emergency response capabilities such as evacuation, rescue, and communication, which may lead to further deterioration of the disaster consequences. This model couples these two key indicators. Specifically, it uses the stress matching degree of the dike engineering... Compared with the weighted emergency response entropy increase coefficient Multiplication enables a comprehensive assessment of flood risk. Here... This item acts as a risk amplifier: when the emergency response system is functioning normally ( When the risk is close to zero, it is mainly determined by the condition of the dike engineering; however, when the emergency response system malfunctions ( When the flood level increases (i.e., even if the levee structure remains relatively stable), the overall flood risk will be significantly amplified because a decline in societal response capacity will exacerbate the disaster's impact. Finally, the calculation result is limited to less than 1 using a min function. This treatment ensured the overall flood risk index was maintained. By consistently maintaining a reasonable range of 0 to 1, the index avoids unlimited growth, giving it clear physical meaning and comparability. This facilitates comparison with preset warning thresholds, thereby triggering flood warning signals. This design allows the model to dynamically integrate the dimensions of engineering safety and social emergency response. When the levee's load-bearing capacity is strained or the emergency system fails, the risk index increases accordingly, thus reflecting the actual flood risk level more comprehensively and accurately.

[0075] As a specific implementation method, the flood risk assessment model can be deployed in a flood early warning system. This system first obtains the stress matching degree of the levee engineering using the methods described above. and emergency response entropy increase coefficient For example, at a specific moment, the system calculates the stress matching degree of the dike project. =0.6, and the emergency response entropy increase was calculated at the same time. =0.3. At this point, the system substitutes these two values ​​into the flood risk assessment model formula: The resulting comprehensive flood risk index The system compares this calculation result with a preset alarm threshold. For example, if the preset flood warning threshold is 0.75, then since 0.78 > 0.75, the system will trigger a flood warning signal. In another scenario, assuming the stress matching degree of the levee project... However, due to unforeseen circumstances causing communication disruptions, the emergency response entropy increase coefficient... At this point, calculate: If the preset threshold remains at 0.75, then 0.72 < 0.75, and the system may not trigger the highest level of warning, but will issue a graded warning based on the risk index level. In this way, the model can assess flood risk in real time and quantitatively based on the dynamic changes in the status of dike engineering and social emergency response capabilities, and ensure that the risk index is within a reasonable range.

[0076] This application's solution addresses the problems of inaccurate risk index quantification and inaccurate early warning in traditional methods by specifying the calculation formula for the flood risk assessment model. The model couples the stress matching degree of the levee engineering with the entropy increase coefficient of the emergency response and introduces a risk amplification mechanism, meaning that when the emergency response system fails, the overall flood risk is significantly amplified. Simultaneously, a min function limits the comprehensive flood risk index to the range of 0 to 1, ensuring the rationality and operability of risk quantification, preventing the index from growing indefinitely, and making risk assessment results comparable under different scenarios. This design allows the model to dynamically integrate the levee engineering status and social emergency response capabilities. When the levee's bearing capacity is strained or the emergency system fails, the risk index increases accordingly, thus reflecting the actual flood risk level more comprehensively and accurately, significantly improving the scientific rigor and foresight of flood early warning.

[0077] In some of the solutions described above in this application, flood warning methods were proposed to address the shortcomings of existing flood warning technologies. However, during their implementation, manually executing these methods may lead to low efficiency, response delays, and operational errors, failing to meet the needs of real-time flood warnings. Therefore, this application proposes a device for early flood warning issuance through upstream and downstream linkage, applying the aforementioned method for early flood warning issuance through upstream and downstream linkage.

[0078] In this application, the device refers to a physical entity capable of performing a specific function or task, which typically includes hardware and software components. This device can be a dedicated computing system, such as an industrial control computer, embedded system, or high-performance server, or a general-purpose computing device configured with specific software modules. Its core function is to receive data, process it, execute algorithms, and output results. Applying the aforementioned method means that the device is configured to automatically and continuously execute the steps of the aforementioned upstream and downstream linkage for early flood warning. This is typically achieved by running pre-programmed software programs or firmware on the device's processor, which contain the logic and algorithms for implementing the method. In this way, the device can transform abstract method steps into actual executable operations, thereby achieving automated early warning functionality.

[0079] The device described in this application automates the aforementioned method of issuing early flood warnings through upstream and downstream linkage by integrating hardware and software components. Specifically, the device continuously collects key data from various data sources, including upstream cross-sectional flow, average rainfall intensity across the basin, reservoir discharge disturbance flow, soil pre-flood moisture content, downstream backwater level, river baseflow level, population density along the river, evacuation route congestion delay multiple, communication base station outage rate, difference between levee crest elevation and predicted maximum water level, and levee seepage pressure monitoring values. Subsequently, the device's internal processor automatically processes and calculates these collected data according to preset algorithms and logic, sequentially determining the flood source stress coefficient, basin absorption resilience coefficient, emergency response entropy increase coefficient, and levee engineering stress matching coefficient. Based on this, the device further utilizes a flood risk assessment model to synthesize the emergency response entropy increase coefficient and levee engineering stress matching coefficient to obtain a comprehensive flood risk index. When this index exceeds a preset alarm threshold, the device will automatically trigger and issue a flood warning signal.

[0080] By integrating the aforementioned complex flood risk assessment methods involving multiple factors and stages into an automated device, this application overcomes the inherent drawbacks of traditional manual methods, such as inefficiency, delayed response, and human error. The device enables real-time, continuous monitoring and assessment of flood risks, ensuring the timeliness and accuracy of early warning information, thereby significantly improving the scientific rigor and foresight of flood prevention and disaster reduction efforts. This automated execution mechanism allows the originally complex and time-consuming data collection, calculation, and judgment processes to be completed efficiently, providing decision-makers with valuable response time and effectively supporting upstream and downstream coordinated flood control scheduling and emergency management.

[0081] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for coordinated flood warning issuance between upstream and downstream areas, characterized in that, include: The upstream cross-sectional flow, the average rainfall intensity of the watershed, and the flood discharge disturbance flow of the reservoir are collected to calculate the flood source stress coefficient. The soil moisture content in the early stage, the downstream backwater level and the base flow level of the river channel were collected, and the watershed absorption resilience coefficient was calculated accordingly. Collect data on population density along the river, congestion delay multiples of evacuation routes, and communication base station outage rates, and calculate the emergency response entropy increase coefficient accordingly. The difference between the top elevation of the dike and the predicted highest water level, as well as the monitoring value of the dike seepage pressure, are obtained. Combined with the flood source stress coefficient and the watershed absorption resilience coefficient, the stress matching coefficient of the dike project is calculated. Based on the emergency response entropy increase coefficient and the stress matching degree of the dike project, a comprehensive flood risk index is obtained through a flood risk assessment model. When the index exceeds the preset warning threshold, a flood warning signal is triggered.

2. The method for issuing early flood warnings through upstream and downstream coordination as described in claim 1, characterized in that, The flood source stress coefficient is calculated as follows: Obtain upstream cross-sectional flow, average rainfall intensity over the watershed, and reservoir discharge disturbance flow; The current upstream section flow, average rainfall intensity of the basin, and reservoir discharge disturbance flow are compared with the section warning flow, the local short-duration rainstorm warning threshold, and the reference value of safe discharge of the downstream river channel, respectively. After limiting the upper limit of the ratio to 1, the upstream section flow index, the average rainfall intensity index of the basin, and the reservoir discharge disturbance flow index are obtained. Substituting the upstream cross-section flow index, the basin average rainfall intensity index, and the reservoir discharge disturbance flow index into the formula Obtain the flood source stress coefficient ,in, The upstream cross-sectional flow index. The average rainfall intensity index for the watershed area. The reservoir discharge disturbance flow index. , and All are Hongyuan coercion weights.

3. The method for issuing early flood warnings through upstream and downstream coordination as described in claim 1, characterized in that, The watershed absorption resilience coefficient is calculated as follows: Obtain soil moisture content, downstream backwater level, and river base flow level; The difference between the current downstream backwater level and the multi-year average low water level is compared with the difference between the critical water level that causes a significant backwater effect and the multi-year average low water level. After limiting the upper limit of the ratio to 1, the downstream backwater level index is obtained. The difference between the current baseflow level and the low water level is compared with the difference between the floodplain level and the low water level. After limiting the upper limit of the ratio to 1, the baseflow level index is obtained. Substitute the soil pre-current moisture content, downstream backwater level index, and river baseflow level index into the formula. Obtain the watershed absorption resilience coefficient ,in, This refers to the initial soil moisture content. This is the downstream backwater level index. This refers to the baseflow water level index of the river channel.

4. The method for issuing early flood warnings through upstream and downstream coordination as described in claim 1, characterized in that, The emergency response entropy increase coefficient is calculated as follows: Obtain information on population density along the river, congestion delay multiples of evacuation routes, and communication base station outage rates; The population density index of the riverside area is obtained by comparing the current population density of the riverside area with the highest historical population density and limiting the ratio to 1. The route congestion index is obtained by comparing the difference between the current evacuation route congestion delay multiple and the baseline travel time multiple with the difference between the congestion tolerance limit multiple and the baseline travel time multiple, and then limiting the upper limit of the ratio to 1. Substitute the population density index, route congestion index, and communication base station outage rate of the riverside area into the formula. Obtain the entropy increase coefficient of emergency response ,in, The population density index for the riverside area. This refers to the route congestion index. For communication base station outage rate, , and All are entropy increase weights for emergency response. It is a very small positive number.

5. The method for issuing early flood warnings through upstream and downstream coordination according to claim 2 or 3, characterized in that, The stress matching coefficient of the embankment project is calculated as follows: The difference between the top elevation of the dike and the predicted highest water level, the monitoring value of the dike seepage pressure, the flood source stress coefficient, and the watershed absorption resilience coefficient were obtained. The difference between the current dike crest elevation and the predicted highest water level elevation is compared with the design safety superelevation of the dike. After limiting the ratio to 0-1, the elevation difference index is obtained. The current seepage pressure monitoring value of the dike is compared with the seepage pressure threshold at the time of the historical emergency, and the upper limit of the ratio is limited to 1 to obtain the seepage pressure monitoring value index of the dike. Substituting the elevation difference index, the levee seepage pressure monitoring value index, the flood source stress coefficient, and the watershed absorption resilience coefficient into the formula Obtain the stress matching coefficient of the dike project ,in, The flood source stress coefficient, The watershed absorption resilience coefficient. The elevation difference index, This is the index for monitoring seepage pressure in dikes.

6. The method for issuing early flood warnings through upstream and downstream coordination as described in claim 1, characterized in that, The flood risk assessment model is as follows: in, This is a comprehensive flood risk index. For the stress matching degree of the dike project, This is the entropy increase coefficient for emergency response.

7. A device for early flood warning issuance through upstream and downstream linkage, characterized in that, The method for issuing early flood warnings by upstream and downstream linkage as described in any one of claims 1-6.

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