Flood disaster early warning method and system

Abstract

The invention provides a flood disaster early warning method and system, and belongs to the technical field of flood disaster early warning. The method comprises the following steps: for each sponge facility in a target area, predicting a first flood reduction characteristic of the sponge facility at the occurrence time of next rainfall based on historical water storage data and historical drainage data of the sponge facility; correcting the first flood reduction characteristic of each sponge facility on the basis of the environmental state of the occurrence time of next rainfall and the influence relationship of different environmental states on the flood reduction characteristics of various sponge facilities to obtain a second flood reduction characteristic of the sponge facility; and setting an urban rainfall flood model considering the sponge facilities based on the second flood reduction characteristic of each sponge facility, so as to determine a flood disaster early warning result of the target area during next rainfall based on the urban rainfall flood model. According to the invention, the flood disaster early warning result precision can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of flood disaster early warning, and in particular to a flood disaster early warning method and system. Background Art

[0002] To solve this problem, the concept of sponge city construction came into being. Sponge facilities (such as permeable pavement, sunken green space, rain garden, etc.) effectively reduce the pressure of urban rain and flood by absorbing, retaining and discharging rainwater. The urban rain and flood model is an important tool for modern urban flood prevention and disaster reduction. Its core function is to simulate and predict the impact of precipitation events on the urban hydrological system. By integrating meteorological data, topographic features, drainage network information and the dynamic characteristics of sponge facilities, the model can assess the risk of waterlogging in the target area under different rainfall conditions and generate flood disaster warning results.

[0003] However, traditional stormwater management methods are usually implemented based on static models and fail to fully consider the dynamic operating characteristics of sponge facilities and their sensitivity to environmental conditions, resulting in low accuracy of flood disaster warning results that are difficult to meet actual needs. Summary of the invention

[0004] The embodiment of the present invention provides a flood disaster early warning method and system to solve the problem of improving the accuracy of flood disaster early warning results.

[0005] In a first aspect, an embodiment of the present invention provides a flood disaster early warning method, comprising: For each sponge facility in the target area, based on the historical water storage data and historical drainage data of the sponge facility, predict the first flood reduction characteristics of the sponge facility at the time of the next precipitation; Based on the environmental state of the next rainfall occurrence time and the relationship between the flood reduction characteristics of various sponge facilities and the influence of different environmental states, the first flood reduction characteristics of each sponge facility are corrected to obtain the second flood reduction characteristics of the sponge facility; An urban stormwater model taking into account the sponge facility is set based on the second flood reduction characteristic of each sponge facility, so as to determine a flood disaster warning result of the target area at the next rainfall based on the urban stormwater model.

[0006] In a possible implementation, the flood reduction characteristics include water storage capacity and drainage rate; for each sponge facility in the target area, based on the historical water storage data and historical drainage data of the sponge facility, the first flood reduction characteristics of the sponge facility at the time of the next precipitation are predicted, including: The total precipitation, precipitation intensity and duration of the last precipitation, and the occurrence time of the next precipitation are input into the characteristic recognition model corresponding to the category of the first sponge facility, and the change in water storage and drainage rate corresponding to the unit catchment area of ​​the first sponge facility are obtained; wherein the first sponge facility is any sponge facility; Based on the water storage capacity and drainage rate of the first sponge facility in the last rainfall, as well as the change in water storage capacity and the change in drainage rate, the water storage capacity and drainage rate of the first sponge facility in the next rainfall are calculated.

[0007] In a possible implementation, before the total precipitation, precipitation intensity and duration of the last precipitation, and the time of occurrence of the next precipitation are input into the characteristic recognition model corresponding to the category of the first sponge facility, and the water storage capacity change and drainage rate change corresponding to the unit catchment area of ​​the first sponge facility are obtained, the method further includes: Obtain the occurrence time, total precipitation, precipitation intensity, duration, water storage change per unit catchment area and drainage rate change of multiple historical precipitations of multiple sponge facilities of the first category; wherein the first category is any category; The time interval between two adjacent historical precipitations, as well as the total precipitation, precipitation intensity and duration of the previous historical precipitation in the two adjacent historical precipitations are used as training samples, and the change in water storage and drainage rate corresponding to the unit catchment area of ​​the corresponding sponge facility is used as the sample label to construct the training data set of the first category of sponge facilities; The initial neural network model is trained based on the training data set to obtain a characteristic recognition model of the first category of sponge facilities.

[0008] In a possible implementation, the change in water storage and drainage rate corresponding to the unit catchment area of ​​the sponge facility is used as a sample label, including: For each historical precipitation, obtain the water storage capacity change and drainage rate change corresponding to the unit catchment area of ​​multiple sponge facilities of the first category; Based on the catchment area of ​​each sponge facility, the weighted average of the change in water storage capacity and the change in drainage rate per unit catchment area of ​​the sponge facility is taken as the sample label.

[0009] In a possible implementation, based on the environmental state of the next rainfall occurrence time and the relationship between the flood reduction characteristics of various sponge facilities and the influence of different environmental states, the first flood reduction characteristics of each sponge facility are corrected to obtain the second flood reduction characteristics of the sponge facility, and the method further includes: For multiple sponge facilities of the second category, simulate artificial rainfall events of different intensities and durations under multiple environmental conditions, and record the flood reduction characteristics of each sponge facility under each artificial rainfall event; the second category is any category; For each sponge facility, based on the flood reduction characteristics of the sponge facility under each artificial rainfall event under the first environmental state and the flood reduction characteristics of the sponge facility under each artificial rainfall event under the standard environmental state, determine the influence relationship of the flood reduction characteristics of the sponge facility under the first environmental state; wherein the first environmental state is any environmental state; The influence relationship between the flood reduction characteristics of each sponge facility and the first environmental state is integrated to obtain the influence relationship between the flood reduction characteristics of the second category of sponge facilities and the first environmental state.

[0010] In a possible implementation, for each sponge facility, based on the flood reduction characteristics of the sponge facility under each artificial rainfall event under the first environmental state and the flood reduction characteristics of the sponge facility under each artificial rainfall event under the standard environmental state, determining the influence relationship of the flood reduction characteristics of the sponge facility on the first environmental state includes: For each sponge facility, linear regression fitting is performed based on the flood reduction characteristics of the sponge facility under each artificial rainfall event under the first environmental state and the flood reduction characteristics of the sponge facility under each artificial rainfall event under the standard environmental state to obtain the relationship between the flood reduction characteristics of the sponge facility and the influence of the first environmental state.

[0011] In a possible implementation, the environmental conditions include temperature, humidity, wind speed, and wind direction; Based on the environmental conditions at the next rainfall and the relationship between the flood reduction characteristics of various sponge facilities and the influence of different environmental conditions, the first flood reduction characteristics of each sponge facility are modified, including: The second flood reduction characteristic of the second sponge facility is obtained by multiplying the influence relationship of the temperature, humidity, wind speed and wind direction corresponding to the category of the second sponge facility at the time of the next precipitation by the first flood reduction characteristic of the second sponge facility at the time of the next precipitation; wherein the second sponge facility is any sponge facility.

[0012] In a second aspect, an embodiment of the present invention provides a flood disaster early warning system, including: A prediction module, for predicting, for each sponge facility in the target area, the first flood reduction characteristic of the sponge facility at the time of the next precipitation based on the historical water storage data and historical drainage data of the sponge facility; A correction module is used to correct the first flood reduction characteristic of each sponge facility based on the environmental state of the next precipitation occurrence time and the influence of different environmental states on the flood reduction characteristics of various sponge facilities, so as to obtain the second flood reduction characteristic of the sponge facility; The early warning module is used to set an urban rain and flood model taking into account the sponge facilities based on the second flood reduction characteristics of each sponge facility, so as to determine the flood disaster early warning result of the target area at the next rainfall based on the urban rain and flood model.

[0013] In a third aspect, an embodiment of the present invention provides a terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method described in the first aspect or any possible implementation manner of the first aspect are implemented.

[0014] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect or any possible implementation method of the first aspect are implemented.

[0015] The embodiment of the present invention provides a flood disaster warning method and system, which predicts the first flood reduction characteristic of the sponge facility at the time of the next precipitation based on the historical water storage and drainage data of the sponge facility; and then corrects it in combination with the influence relationship of the environmental state to obtain a second flood reduction characteristic that is closer to the actual operating conditions. The introduction of the dynamic flood reduction characteristic enables the urban stormwater model to more accurately reflect the actual performance of the sponge facility, thereby improving the simulation accuracy of the sponge facility in the stormwater model for the rainwater retention, infiltration and discharge process, and improving the accuracy of the flood disaster warning results. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0017] Figure 1 This is a flow chart of a flood disaster early warning method according to an embodiment of the present invention; Figure 2 It is a structural schematic diagram of a flood disaster early warning system provided by an embodiment of the present invention; Figure 3 is a schematic diagram of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0019] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0020] See also Figure 1 , which shows a flow chart of a flood disaster early warning method provided by an embodiment of the present invention, and is described in detail as follows: Step 101, for each sponge facility in the target area, based on the historical water storage data and historical drainage data of the sponge facility, predict the first flood reduction characteristics of the sponge facility at the time of the next rainfall.

[0021] In this embodiment, the following parameters of the sponge facility will change over time and the number of storage and drainage cycles, thus affecting the performance of the facility and the flood reduction effect: Water storage capacity: After long-term use, the porosity of materials (such as soil and fill) inside the facility may decrease due to compaction or clogging, resulting in a decrease in water storage capacity. The reduction of vegetation cover will also reduce the rainwater retention capacity.

[0022] Infiltration rate: Particles in soil or fill may migrate or settle over multiple storage and drainage cycles, causing changes in porosity. Impurity accumulation reduces permeability.

[0023] Drain Rate: Drain lines can become clogged over time due to sediment or biofilm formation, causing drainage to slow down. Microbial activity can form biofilms inside a facility, affecting permeability and drainage efficiency.

[0024] Evaporation rate: Aging of surface materials or degradation of vegetation can affect evaporation rates. Prolonged drought or high temperatures can cause soil compaction, further affecting evaporation.

[0025] Material aging degree: During the storage and drainage cycle, water erosion will cause wear and tear on the surface materials of facilities. Pollutants in rainwater may react chemically with facility materials and change their physical properties. The materials of facilities such as permeable pavement and bioretention ponds may change their physical or chemical properties due to long-term exposure to rainwater, sunlight and temperature changes.

[0026] Therefore, the performance aging degree of sponge facilities can be predicted through historical precipitation data, historical water storage data, and historical drainage data to obtain more accurate flood reduction characteristics.

[0027] Step 102, based on the environmental conditions at the time of the next rainfall and the influence of different environmental conditions on the flood reduction characteristics of various sponge facilities, the first flood reduction characteristics of each sponge facility are corrected to obtain the second flood reduction characteristics of the sponge facility.

[0028] In this embodiment, the flood reduction characteristics of the sponge facility are not determined by a single factor, but are affected by the combined effects of multiple environmental conditions (such as temperature, humidity, wind speed, etc.). If the influence of these factors can be accurately quantified and integrated into the model, the model will be able to more comprehensively reflect the actual operating status of the facility, thereby improving the scientificity and reliability of flood disaster warnings.

[0029] Specifically, the parameters of sponge facilities affected by environmental conditions include: Water storage capacity: Temperature and humidity affect the permeability of the soil or material, thus changing the water storage capacity of the sponge facility. Higher water storage capacity means that the facility can absorb more rainwater and reduce runoff discharge.

[0030] Evaporation rate: Temperature, humidity, and wind speed together determine the evaporation rate, which affects the amount of rainwater lost from storage in the facility. A higher evaporation rate will result in some rainwater loss, reducing the facility's actual storage capacity.

[0031] Infiltration rate: Temperature and humidity change the physical properties of soil or fill (such as porosity and wetness), which in turn affects the infiltration rate. Faster infiltration rates help rainwater enter the ground quickly, reducing surface water accumulation.

[0032] Drainage speed: Wind speed and direction may indirectly affect drainage efficiency by changing the surface hydrodynamic characteristics. Appropriate drainage speed can avoid facility overload and delay the arrival of flood peaks.

[0033] Vegetation growth status: Temperature and humidity directly affect the health of vegetation, and the presence of vegetation can enhance the retention and purification of rainwater. Healthy vegetation can increase the retention time and purification effect of rainwater, and improve the overall flood reduction capacity.

[0034] Step 103, setting an urban rain and flood model taking into account the sponge facility based on the second flood reduction characteristic of each sponge facility, so as to determine a flood disaster warning result of the target area at the next rainfall based on the urban rain and flood model.

[0035] In this embodiment, the urban stormwater model is a mathematical or computational model used to simulate and predict the behavior of the urban stormwater system. It usually integrates meteorological data, topographic features, drainage network information, and the impact characteristics of sponge facilities on precipitation. By simulating the rainwater flow process in the target area (including precipitation distribution, surface runoff, pipe network drainage, sponge facility retention and discharge, etc.), the flood control capacity of the entire system is evaluated. The flood disaster warning results may include information such as the distribution of waterlogging points, waterlogging depth, waterlogging duration, and potential disaster-affected areas.

[0036] Assume that a city’s target area contains multiple sunken green spaces and permeable paving facilities. If the flood mitigation properties of these facilities are not accurately assessed, the following may occur: Underestimation of facility capacity: The model predicts that the target area will experience severe flooding during the next rainfall, but in fact the facilities are able to effectively retain rainwater, resulting in a false alarm.

[0037] Overestimation of facility capacity: The model predicts that waterlogging will not occur in the target area, but in reality, waterlogging occurs in the facilities due to blockage or overload operation, resulting in underreporting.

[0038] By introducing high-precision flood reduction characteristic data, the model can more accurately predict the risk of waterlogging in the target area and avoid the occurrence of the above problems.

[0039] The embodiment of the present invention predicts the first flood reduction characteristic of the sponge facility at the time of the next rainfall based on the historical water storage and drainage data of the sponge facility; and then corrects it in combination with the influence relationship of the environmental state to obtain the second flood reduction characteristic that is closer to the actual operating conditions. The introduction of the dynamic flood reduction characteristic enables the urban stormwater model to more accurately reflect the actual performance of the sponge facility, thereby improving the simulation accuracy of the sponge facility in the stormwater model for rainwater retention, infiltration and discharge processes, and improving the accuracy of flood disaster warning results.

[0040] In a possible implementation, the flood reduction characteristics include water storage capacity and drainage rate; for each sponge facility in the target area, based on the historical water storage data and historical drainage data of the sponge facility, the first flood reduction characteristics of the sponge facility at the time of the next precipitation are predicted, including: The total precipitation, precipitation intensity and duration of the last precipitation, and the occurrence time of the next precipitation are input into the characteristic recognition model corresponding to the category of the first sponge facility, and the change in water storage and drainage rate corresponding to the unit catchment area of ​​the first sponge facility are obtained; wherein the first sponge facility is any sponge facility; Based on the water storage capacity and drainage rate of the first sponge facility in the last rainfall, as well as the change in water storage capacity and the change in drainage rate, the water storage capacity and drainage rate of the first sponge facility in the next rainfall are calculated.

[0041] In this embodiment, flood reduction characteristics refer to the ability of sponge facilities to absorb, retain and discharge rainwater in stormwater management, usually including water storage capacity (unit: m³) and drainage rate (unit: m³ / s).

[0042] Historical water storage data and historical drainage data refer to the actual operation records of sponge facilities in past precipitation events, including changes in water storage and drainage flow after each precipitation.

[0043] The characteristic identification model is a mathematical model based on machine learning or statistical analysis, which is used to predict the flood reduction characteristics of sponge facilities based on input parameters such as precipitation, precipitation intensity, duration, etc. For different types of sponge facilities (such as permeable pavement, sunken green space, etc.), special models are constructed to reflect their specific operating laws.

[0044] The change in water storage and drainage rate per unit catchment area refers to the amount of water storage and drainage rate that can be increased or decreased by the facility under specific precipitation conditions per square meter of catchment area. Through standardization, the flood reduction capacity of sponge facilities of different sizes can be compared.

[0045] Assuming there is a permeable paving facility (first sponge facility) in the target area, the steps to predict its first flood reduction characteristics during the next rainfall are as follows: (1) Collect historical data Total precipitation from last precipitation: .

[0046] The intensity of the last precipitation: .

[0047] Duration of last precipitation: .

[0048] Water storage after last rainfall: .

[0049] Drainage rate since last rainfall: .

[0050] Catchment area of ​​the facility: .

[0051] (2) Determine when the next precipitation will occur The next precipitation is expected to occur 72 hours after the last precipitation.

[0052] (3) Input feature recognition model Inputting the above parameters into the characteristic identification model of the permeable pavement category, the following results are obtained: Change in water storage per unit catchment area: .

[0053] Change in drainage rate per unit catchment area: .

[0054] (4) Calculate the water storage capacity and drainage rate of the next rainfall According to the formula:

[0055]

[0056] (5) Results analysis It is predicted that during the next rainfall, the water storage capacity of the permeable paving facility will be 100m³ and the drainage rate will be 0.1m³ / s.

[0057] In a possible implementation, before the total precipitation, precipitation intensity and duration of the last precipitation, and the time of occurrence of the next precipitation are input into the characteristic recognition model corresponding to the category of the first sponge facility, and the water storage capacity change and drainage rate change corresponding to the unit catchment area of ​​the first sponge facility are obtained, the method further includes: Obtain the occurrence time, total precipitation, precipitation intensity, duration, water storage change per unit catchment area and drainage rate change of multiple historical precipitations of multiple sponge facilities of the first category; wherein the first category is any category; The time interval between two adjacent historical precipitations, as well as the total precipitation, precipitation intensity and duration of the previous historical precipitation in the two adjacent historical precipitations are used as training samples, and the change in water storage and drainage rate corresponding to the unit catchment area of ​​the corresponding sponge facility is used as the sample label to construct the training data set of the first category of sponge facilities; The initial neural network model is trained based on the training data set to obtain a characteristic recognition model of the first category of sponge facilities.

[0058] In this embodiment, the neural network-based feature recognition model has a strong nonlinear fitting ability and can more accurately capture the complex relationship between precipitation characteristics and facility flood reduction characteristics. The training sample takes into account the time interval between two adjacent precipitations, which can better reflect the dynamic response of sponge facilities under long-term no precipitation or frequent precipitation conditions.

[0059] In a possible implementation, the change in water storage and drainage rate corresponding to the unit catchment area of ​​the sponge facility is used as a sample label, including: For each historical precipitation, obtain the water storage capacity change and drainage rate change corresponding to the unit catchment area of ​​multiple sponge facilities of the first category; Based on the catchment area of ​​each sponge facility, the changes in water storage capacity and drainage rate per unit catchment area of ​​the sponge facility are weighted and averaged as sample labels. This can reflect the overall performance of the entire category of facilities rather than the performance of a single facility.

[0060] In this embodiment, the change in water storage capacity and the change in drainage rate corresponding to the unit catchment area of ​​the plurality of sponge facilities of the first category reflect the actual operating status of each facility under specific precipitation conditions.

[0061] The catchment area of ​​each sponge facility determines its importance in the entire category. Therefore, the weighted average method is used to convert the change in water storage and drainage rate corresponding to the unit catchment area into a comprehensive value at the category level. As a sample label, it reflects the overall performance change of the entire category of facilities, rather than the performance of a single facility. The formula is as follows:

[0062]

[0063] in, : The catchment area of ​​the ith facility (unit: m²) : The change in water storage capacity per unit catchment area of ​​the ith facility (unit: m³ / m²).

[0064] The change in drainage rate per unit catchment area of ​​the ith facility (unit: m³ / (s·m²)).

[0065] n: Total number of facilities.

[0066] In a possible implementation, based on the environmental state of the next rainfall occurrence time and the relationship between the flood reduction characteristics of various sponge facilities and the influence of different environmental states, the first flood reduction characteristics of each sponge facility are corrected to obtain the second flood reduction characteristics of the sponge facility, and the method further includes: For multiple sponge facilities of the second category, simulate artificial rainfall events of different intensities and durations under multiple environmental conditions, and record the flood reduction characteristics of each sponge facility under each artificial rainfall event; the second category is any category; For each sponge facility, based on the flood reduction characteristics of the sponge facility under each artificial rainfall event under the first environmental state and the flood reduction characteristics of the sponge facility under each artificial rainfall event under the standard environmental state, determine the influence relationship of the flood reduction characteristics of the sponge facility under the first environmental state; wherein the first environmental state is any environmental state; The influence relationship between the flood reduction characteristics of each sponge facility and the first environmental state is integrated to obtain the influence relationship between the flood reduction characteristics of the second category of sponge facilities and the first environmental state.

[0067] In this embodiment, the environmental state refers to a specific environmental condition (such as temperature, humidity, wind speed, wind direction, etc.), which is used to describe the external environmental characteristics of the facility during operation. It can be a single factor (such as high humidity) or a combination of multiple factors (such as high temperature + low wind speed). The standard environmental state refers to a preset benchmark environmental condition, which is usually used to compare the impact of other environmental states on the flood reduction characteristics of the facility, and is used as a reference condition to help quantify the impact of changes in environmental states on the performance of the facility.

[0068] Artificial rainfall events under different environmental conditions can evaluate the operating performance of sponge facilities under different precipitation conditions. Through experiments and data analysis, the relationship between the flood reduction characteristics of various types of sponge facilities and the influence of different environmental conditions is determined, and it is used to correct the first flood reduction characteristics of the facilities, and finally obtain the second flood reduction characteristics that are closer to the actual operating conditions.

[0069] Assuming that there is a type of sunken green space facility (second category) in the target area, the specific steps to analyze the relationship between its flood reduction characteristics and the impact of different environmental conditions are as follows: (1) Artificial rainfall experiment Set three environment states: Environmental condition 1: temperature T1 = 20∘C, humidity H1 = 60%, wind speed Ws1 = 2 m / s, wind direction Wd1 = southeast wind.

[0070] Environmental condition 2: temperature T2 = 30∘C, humidity H2 = 80%, wind speed Ws2 = 5 m / s, wind direction Wd2 = northwest wind.

[0071] Standard environmental conditions: temperature T0=25∘C, humidity H0=70%, wind speed Ws0=3 m / s, wind direction Wd0=east wind.

[0072] Three types of artificial rainfall events were simulated: Light rain: intensity I1=10 mm / h, duration D1=1 h.

[0073] Moderate rain: intensity I2=20 mm / h, duration D2=2 h.

[0074] Heavy rain: intensity I3=40 mm / h, duration D3=3 h.

[0075] Record the flood reduction characteristics (change in storage volume and change in discharge rate per unit catchment area) of each facility for each rainfall event.

[0076] (2) Determine the impact relationship of a single facility Assuming there are two sunken green space facilities (facility A and facility B), the experimental data are shown in Table 1: Table 1 Experimental data

[0077] Based on the above data, the impact relationship model of facility A is established:

[0078]

[0079] (3) Integrate category influence relationships The impact relationship of facility A and facility B is weighted and averaged to obtain the overall impact relationship of the second type of sunken green space:

[0080]

[0081] In a possible implementation, for each sponge facility, based on the flood reduction characteristics of the sponge facility under each artificial rainfall event under the first environmental state and the flood reduction characteristics of the sponge facility under each artificial rainfall event under the standard environmental state, determining the influence relationship of the flood reduction characteristics of the sponge facility on the first environmental state includes: For each sponge facility, linear regression fitting is performed based on the flood reduction characteristics of the sponge facility under each artificial rainfall event under the first environmental state and the flood reduction characteristics of the sponge facility under each artificial rainfall event under the standard environmental state to obtain the relationship between the flood reduction characteristics of the sponge facility and the influence of the first environmental state.

[0082] In this embodiment, a linear regression method can be used with a small number of artificial rainfall events to determine the specific impact of the first environmental state on the flood reduction characteristics of each sponge facility, and the flood reduction characteristics of the facility under a specific environmental state can be predicted through the fitted functional relationship.

[0083] Linear regression fitting can clarify the influence of environmental conditions on the flood reduction characteristics of facilities, significantly improving the accuracy of prediction results. Moreover, the fitting results are based on experimental data, avoiding the deviation caused by the assumption of fixed parameters in traditional static models.

[0084] In a possible implementation, the environmental conditions include temperature, humidity, wind speed, and wind direction; Based on the environmental conditions at the next rainfall and the relationship between the flood reduction characteristics of various sponge facilities and the influence of different environmental conditions, the first flood reduction characteristics of each sponge facility are modified, including: The second flood reduction characteristic of the second sponge facility is obtained by multiplying the influence relationship of the temperature, humidity, wind speed and wind direction corresponding to the category of the second sponge facility at the time of the next precipitation by the first flood reduction characteristic of the second sponge facility at the time of the next precipitation; wherein the second sponge facility is any sponge facility.

[0085] In this embodiment, the influence relationship of the flood reduction characteristics of the sponge facility on a single environmental state factor can be determined separately, the experimental process of artificial rainfall events can be simplified, and the comprehensive effect of multiple factors (such as temperature, humidity, wind speed, and wind direction) on the flood reduction characteristics can be considered, which can more comprehensively reflect the actual operating conditions of the sponge facility.

[0086] For example, suppose the flood mitigation characteristics of a facility are related to temperature by:

[0087] The relationship with humidity is:

[0088] Where T and H represent temperature and humidity respectively. , , , is the regression coefficient.

[0089] The influence relationship of each single environmental state factor is integrated to form a complete environmental state impact model. Assume that the comprehensive model is in the form of:

[0090] in and Represent wind speed and wind direction respectively.

[0091] In practical applications, based on the specific environmental conditions (such as temperature, humidity, wind speed, and wind direction) at the next rainfall, the above environmental conditions are multiplied by the first flood reduction characteristic of the facility to obtain the second flood reduction characteristic. The calculation formula is as follows:

[0092]

[0093] in: and They represent the change in water storage capacity and discharge rate of the first flood reduction characteristic. F(environmental state) F (Environmental status) represents the comprehensive influence relationship of the environmental status.

[0094] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0095] The following is a system embodiment of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0096] Figure 2 A schematic diagram of the structure of a flood disaster early warning system provided by an embodiment of the present invention is shown. For the sake of convenience, only the parts related to the embodiment of the present invention are shown, which are described in detail as follows: like Figure 2 As shown, a flood disaster early warning system 2 includes: A prediction module 21 is used to predict, for each sponge facility in the target area, a first flood reduction characteristic of the sponge facility at the time of the next rainfall based on the historical water storage data and historical drainage data of the sponge facility; The correction module 22 is used to correct the first flood reduction characteristic of each sponge facility based on the environmental state of the next precipitation occurrence time and the influence relationship between the flood reduction characteristics of various sponge facilities and different environmental states, so as to obtain the second flood reduction characteristic of the sponge facility; The early warning module 23 is used to set an urban rain and flood model taking into account the sponge facility based on the second flood reduction characteristic of each sponge facility, so as to determine the flood disaster early warning result of the target area at the next rainfall based on the urban rain and flood model.

[0097] In a possible implementation, the flood reduction characteristics include water storage capacity and drainage rate; the prediction module 21 is specifically used for: The total precipitation, precipitation intensity and duration of the last precipitation, and the occurrence time of the next precipitation are input into the characteristic recognition model corresponding to the category of the first sponge facility, and the change in water storage and drainage rate corresponding to the unit catchment area of ​​the first sponge facility are obtained; wherein the first sponge facility is any sponge facility; Based on the water storage capacity and drainage rate of the first sponge facility in the last rainfall, as well as the change in water storage capacity and the change in drainage rate, the water storage capacity and drainage rate of the first sponge facility in the next rainfall are calculated.

[0098] In a possible implementation, the prediction module 21 is further configured to: Before inputting the total precipitation, precipitation intensity and duration of the last precipitation, and the occurrence time of the next precipitation into the characteristic recognition model corresponding to the category of the first sponge facility to obtain the water storage change and drainage rate change corresponding to the unit catchment area of ​​the first sponge facility, the occurrence time, total precipitation, precipitation intensity, duration, water storage change and drainage rate change corresponding to the unit catchment area of ​​multiple sponge facilities of the first category are obtained; wherein the first category is any category; The time interval between two adjacent historical precipitations, as well as the total precipitation, precipitation intensity and duration of the previous historical precipitation in the two adjacent historical precipitations are used as training samples, and the change in water storage and drainage rate corresponding to the unit catchment area of ​​the corresponding sponge facility is used as the sample label to construct the training data set of the first category of sponge facilities; The initial neural network model is trained based on the training data set to obtain a characteristic recognition model of the first category of sponge facilities.

[0099] In a possible implementation, the prediction module 21 is specifically used for: For each historical precipitation, obtain the water storage capacity change and drainage rate change corresponding to the unit catchment area of ​​multiple sponge facilities of the first category; Based on the catchment area of ​​each sponge facility, the weighted average of the change in water storage capacity and the change in drainage rate per unit catchment area of ​​the sponge facility is taken as the sample label.

[0100] In a possible implementation, the correction module 22 is specifically used for: Based on the environmental state of the next precipitation occurrence time and the relationship between the flood reduction characteristics of various sponge facilities and the influence of different environmental states, the first flood reduction characteristics of each sponge facility are corrected to obtain the second flood reduction characteristics of the sponge facility. For multiple sponge facilities of the second category, artificial rainfall events of different intensities and durations are simulated under multiple environmental states, and the flood reduction characteristics of each sponge facility under each artificial rainfall event are recorded; wherein the second category is any category; For each sponge facility, based on the flood reduction characteristics of the sponge facility under each artificial rainfall event under the first environmental state and the flood reduction characteristics of the sponge facility under each artificial rainfall event under the standard environmental state, determine the influence relationship of the flood reduction characteristics of the sponge facility under the first environmental state; wherein the first environmental state is any environmental state; The influence relationship between the flood reduction characteristics of each sponge facility and the first environmental state is integrated to obtain the influence relationship between the flood reduction characteristics of the second category of sponge facilities and the first environmental state.

[0101] In a possible implementation, the correction module 22 is specifically used for: For each sponge facility, linear regression fitting is performed based on the flood reduction characteristics of the sponge facility under each artificial rainfall event under the first environmental state and the flood reduction characteristics of the sponge facility under each artificial rainfall event under the standard environmental state to obtain the relationship between the flood reduction characteristics of the sponge facility and the influence of the first environmental state.

[0102] In a possible implementation, the environmental conditions include temperature, humidity, wind speed, and wind direction; The correction module 22 is specifically used for: The second flood reduction characteristic of the second sponge facility is obtained by multiplying the influence relationship of the temperature, humidity, wind speed and wind direction corresponding to the category of the second sponge facility at the time of the next precipitation by the first flood reduction characteristic of the second sponge facility at the time of the next precipitation; wherein the second sponge facility is any sponge facility.

[0103] The embodiment of the present invention predicts the first flood reduction characteristic of the sponge facility at the time of the next rainfall based on the historical water storage and drainage data of the sponge facility; and then corrects it in combination with the influence relationship of the environmental state to obtain the second flood reduction characteristic that is closer to the actual operating conditions. The introduction of the dynamic flood reduction characteristic enables the urban stormwater model to more accurately reflect the actual performance of the sponge facility, thereby improving the simulation accuracy of the sponge facility in the stormwater model for rainwater retention, infiltration and discharge processes, and improving the accuracy of flood disaster warning results.

[0104] Figure 3 is a schematic diagram of a terminal provided by an embodiment of the present invention. Figure 3 As shown, the terminal 3 of this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30. When the processor 30 executes the computer program 32, the steps in each of the above-mentioned flood disaster early warning method embodiments are implemented.

[0105] Exemplarily, the computer program 32 may be divided into one or more modules / units, which are stored in the memory 31 and executed by the processor 30 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program 32 in the terminal 3.

[0106] The terminal 3 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal 3 may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will appreciate that Figure 3It is only an example of terminal 3 and does not constitute a limitation on terminal 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal may also include input and output devices, network access devices, buses, etc.

[0107] The processor 30 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0108] The memory 31 may be an internal storage unit of the terminal 3, such as a hard disk or memory of the terminal 3. The memory 31 may also be an external storage device of the terminal 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the terminal 3. The memory 31 is used to store the computer program and other programs and data required by the terminal. The memory 31 may also be used to temporarily store data that has been output or is to be output.

[0109] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0110] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0111] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0112] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0113] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0114] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0115] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of each of the above-mentioned flood disaster warning method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0116] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A flood disaster early warning method, characterized in that: include: For each sponge facility in the target area, based on the historical water storage data and historical drainage data of the sponge facility, predict the first flood reduction characteristics of the sponge facility at the time of the next precipitation; Based on the environmental state of the next rainfall occurrence time and the relationship between the flood reduction characteristics of various sponge facilities and the influence of different environmental states, the first flood reduction characteristics of each sponge facility are corrected to obtain the second flood reduction characteristics of the sponge facility; An urban rain and flood model taking the sponge facility into consideration is set based on the second flood reduction characteristic of each sponge facility, so as to determine a flood disaster warning result of the target area at the next rainfall based on the urban rain and flood model.

2. A flood disaster early warning method according to claim 1, characterized in that: The flood reduction characteristics include water storage capacity and drainage rate; for each sponge facility in the target area, based on the historical water storage data and historical drainage data of the sponge facility, predicting the first flood reduction characteristics of the sponge facility at the time of the next precipitation includes: The total precipitation, precipitation intensity and duration of the last precipitation, and the occurrence time of the next precipitation are input into the characteristic recognition model corresponding to the category of the first sponge facility, and the change in water storage capacity and drainage rate corresponding to the unit catchment area of ​​the first sponge facility are obtained; wherein the first sponge facility is any sponge facility; Based on the water storage capacity and drainage rate of the first sponge facility in the last rainfall, as well as the water storage capacity change and the drainage rate change, the water storage capacity and drainage rate of the first sponge facility in the next rainfall are calculated.

3. A flood disaster early warning method according to claim 2, characterized in that: Before inputting the total precipitation, precipitation intensity and duration of the last precipitation, and the occurrence time of the next precipitation into the characteristic recognition model corresponding to the category of the first sponge facility to obtain the water storage capacity change and drainage rate change corresponding to the unit catchment area of ​​the first sponge facility, the method further includes: Obtain the occurrence time, total precipitation, precipitation intensity, duration, water storage change per unit catchment area and drainage rate change of multiple historical precipitations of multiple sponge facilities of the first category; wherein the first category is any category; The time interval between two adjacent historical precipitations, as well as the total precipitation, precipitation intensity and duration of the previous historical precipitation in the two adjacent historical precipitations are used as training samples, and the change in water storage and drainage rate corresponding to the unit catchment area of ​​the corresponding sponge facility is used as sample labels to construct a training data set for the first category of sponge facilities; The initial neural network model is trained based on the training data set to obtain a characteristic recognition model of the first category of sponge facilities.

4. A flood disaster early warning method according to claim 3, characterized in that: The change in water storage and drainage rate corresponding to the unit catchment area of ​​the sponge facility is used as a sample label, including: For each historical precipitation, obtain the water storage capacity change and drainage rate change corresponding to the unit catchment area of ​​multiple sponge facilities of the first category; Based on the catchment area of ​​each sponge facility, the weighted average of the water storage capacity change and drainage rate change corresponding to the unit catchment area of ​​the sponge facility is taken as the sample label.

5. A flood disaster early warning method according to claim 1, characterized in that: Before the first flood reduction characteristic of each sponge facility is corrected based on the environmental state of the next rainfall occurrence time and the influence of different environmental states on the flood reduction characteristics of various sponge facilities to obtain the second flood reduction characteristic of the sponge facility, the method further includes: For multiple sponge facilities of the second category, artificial rainfall events of different intensities and durations are simulated under multiple environmental conditions, and the flood reduction characteristics of each sponge facility under each artificial rainfall event are recorded; wherein the second category is any category; For each sponge facility, based on the flood reduction characteristics of the sponge facility under each artificial rainfall event under a first environmental state and the flood reduction characteristics of the sponge facility under each artificial rainfall event under a standard environmental state, determine the influence relationship of the flood reduction characteristics of the sponge facility under the first environmental state; wherein the first environmental state is any environmental state; The influence relationship between the flood reduction characteristics of each sponge facility and the first environmental state is integrated to obtain the influence relationship between the flood reduction characteristics of the second category of sponge facilities and the first environmental state.

6. A flood disaster early warning method according to claim 5, characterized in that: The method of determining, for each sponge facility, the influence relationship of the flood reduction characteristics of the sponge facility on the flood reduction characteristics of the sponge facility under the first environmental state based on the flood reduction characteristics of the sponge facility under each artificial rainfall event under the first environmental state and the flood reduction characteristics of the sponge facility under each artificial rainfall event under the standard environmental state, includes: For each sponge facility, linear regression fitting is performed based on the flood reduction characteristics of the sponge facility under each artificial rainfall event under the first environmental state and the flood reduction characteristics of the sponge facility under each artificial rainfall event under the standard environmental state to obtain the relationship between the flood reduction characteristics of the sponge facility and the influence of the first environmental state.

7. A flood disaster early warning method according to claim 1, characterized in that: The environmental conditions include temperature, humidity, wind speed and wind direction; The first flood reduction characteristic of each sponge facility is modified based on the environmental state at the next rainfall and the relationship between the flood reduction characteristics of various sponge facilities and the influence of different environmental states, including: The second flood reduction characteristic of the second sponge facility is obtained by multiplying the influence relationship of the temperature, humidity, wind speed and wind direction corresponding to the category of the second sponge facility at the time of the next precipitation by the first flood reduction characteristic of the second sponge facility at the time of the next precipitation; wherein the second sponge facility is any sponge facility.

8. A flood disaster early warning system, characterized in that: include: A prediction module, for predicting, for each sponge facility in the target area, the first flood reduction characteristic of the sponge facility at the time of the next precipitation based on the historical water storage data and historical drainage data of the sponge facility; A correction module is used to correct the first flood reduction characteristic of each sponge facility based on the environmental state of the next precipitation occurrence time and the influence of different environmental states on the flood reduction characteristics of various sponge facilities, so as to obtain the second flood reduction characteristic of the sponge facility; The early warning module is used to set an urban rain and flood model taking into account the sponge facilities based on the second flood reduction characteristics of each sponge facility, so as to determine the flood disaster early warning result of the target area at the next rainfall based on the urban rain and flood model.

9. A terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 are implemented.

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