Flood prevention prediction method and system

Abstract

The invention relates to a flood prevention prediction method and system. The method comprises the following steps: obtaining rainfall, terrain and hydrological data of a prediction point, and carrying out standardization processing; generating continuous data for the standardized terrain and hydrological data by adopting an interpolation algorithm, and calculating a terrain gradient; constructing a runoff model based on the standardized rainfall, the continuous hydrological data and the topographic gradient, and obtaining a predicted point runoff volume; building a flood routing model by combining the runoff volume and the topographic slope, and predicting flood arrival time and water level; and calculating a flood risk index according to the flood arrival time and the water level. According to the method, by constructing the runoff model and the flood routing model, dynamic simulation of flood occurrence, development and propagation processes is achieved, and compared with a traditional static model, the method can better adapt to complex hydrological conditions and provide more accurate technical support for flood risk assessment.

Description

Technical Field

[0001] The present invention relates to a flood prevention prediction method and system, belonging to the technical field of hydrological science and disaster management. Background Art

[0002] With the intensification of global climate change and the continuous advancement of urbanization, the frequency and intensity of extreme rainfall events have increased significantly, and flood disasters have become one of the most serious natural disasters globally. Floods not only pose a direct threat to human life safety but also cause huge damage to infrastructure, agricultural production, and economic development. Therefore, accurate and timely flood prediction is of crucial significance for disaster prevention and mitigation, emergency response, and resource allocation. Traditional flood prediction methods highly rely on historical hydrological data and meteorological observation data. However, in some areas, especially remote areas or regions with imperfect data records, these data may be difficult to obtain or have large errors. In addition, the timeliness and integrity of data will also affect the accuracy of prediction. Traditional hydrological models are usually based on simplified physical processes and empirical formulas and are difficult to comprehensively consider complex terrain, soil conditions, and the uneven spatial and temporal distribution of rainfall. This results in large deviations in the accuracy of prediction results, especially when facing extreme rainfall events, and the adaptability and reliability of the models are insufficient.

[0003] The patent document with the patent number "CN118015813A" discloses a reservoir flood prevention warning method and device. The problems of this method are as follows: detailed reservoir characteristic data, basin characteristic data, and historical and future rainfall data are required. The data requirements are complex, and the requirements for the integrity and accuracy of data are relatively high. Especially when calculating the basin characteristic coefficient and the confluence parameter, data missing may lead to the failure of the model. The technology implementation is relatively complex, especially involving the calculation of the basin characteristic coefficient, the confluence parameter, and the dam-break warning data, but the scalability is limited. For example, if it is required to be applied to other types of water bodies (such as lakes or rivers), the model structure may need to be readjusted. Summary of the Invention

[0004] To solve the problems existing in the above-mentioned prior art, the present invention proposes a flood prevention prediction method and system.

[0005] The technical solution of the present invention is as follows:

[0006] On the one hand, the present invention provides a flood prevention prediction method, including the following steps:

[0007] Obtain the rainfall data, terrain data, and hydrological data of the prediction point, and perform standardized processing to obtain the standardized rainfall data, terrain data, and hydrological data;

[0008] Interpolate the standardized terrain data and hydrological data using an interpolation algorithm to obtain continuous terrain data and hydrological data, and calculate the terrain slope of the prediction point through the continuous terrain data;

[0009] Construct a runoff model based on the standardized rainfall data, continuous hydrological data, and terrain slope, and obtain the runoff of the prediction point through the runoff model;

[0010] Construct a flood routing model based on the runoff and terrain slope, and obtain the flood arrival time and water level through the flood routing model;

[0011] Calculate the flood risk index based on the flood arrival time and water level.

[0012] As a preferred embodiment, the method for standardizing the rainfall data is as follows:

[0013]

[0014] Among them, R n (t) represents the rainfall data at time t after standardization, R(t) represents the rainfall data at time t before standardization, N represents the preset maximum number, i, j, g represent digit indices, t represents a continuous time variable, t i , t j , t g represent the i-th discrete point, j-th discrete point, and g-th discrete point selected from the continuous time variable t, R(t i ) represents the rainfall data at time t i before standardization, R(t j ) represents the rainfall data at time t j before standardization, R(t g ) represents the rainfall data at time t g before standardization;

[0015] The method for standardizing the terrain data is as follows:

[0016]

[0017] Among them, D n (x,y) represents the terrain data of any prediction point (x,y) after standardization, D(x,y) represents the terrain data of any prediction point (x,y) before standardization, D(x i ,y i ) represents the terrain data of the i-th prediction point (x i ,y i ) before standardization, D(x j ,y j ) represents the terrain data of the j-th prediction point (xj , y j )'s terrain data, D(x g , y g ) represents the terrain data of the g-th prediction point (x g , y g ) before standardization;

[0018] The method for standardizing the hydrological data is as follows:

[0019]

[0020] Among them, H n (x, y, t) represents the hydrological data of any prediction point (x, y) at time t after standardization, H(x, y, t) represents the hydrological data of any prediction point (x, y) at time t before standardization, H(x i , y i , t i ) represents the hydrological data of the i-th prediction point (x i , y i ) at t i time, H(x j , y j , t j ) represents the hydrological data of the j-th prediction point (x j , y j ) at t j time, H(x g , y g , t g ) represents the hydrological data of the g-th prediction point (x g , y g ) at t g time.

[0021] As a preferred embodiment, the method for obtaining continuous terrain data is:

[0022]

[0023] The method for obtaining continuous hydrological data is:

[0024]

[0025]

[0026] Among them, D i (x, y) represents the terrain data of the i-th continuous arbitrary prediction point (x, y), D n (x i , y i ) represents the i-th prediction point (xi , y i ) topographic data, W i (x, y) represents the i-th inverse distance weight, H i (x, y, t) represents the hydrological data of the i-th consecutive arbitrary prediction point (x, y) at time t, H n (x i , y i , t) represents the hydrological data of the i-th prediction point (x i , y i ) at time t after normalization; x i represents the abscissa of the i-th prediction point (x i , y i ) and y i represents the ordinate of the i-th prediction point (x i , y i ); x represents the abscissa of an arbitrary prediction point (x, y), and y represents the ordinate of an arbitrary prediction point (x, y);

[0027] The calculation method of the terrain slope is as follows:

[0028]

[0029] Among them, represents the square of the partial derivative of the topographic data D i (x, y) of the i-th consecutive arbitrary prediction point (x, y) with respect to the abscissa x of an arbitrary prediction point (x, y), represents the square of the partial derivative of the topographic data D i (x, y) of the i-th consecutive arbitrary prediction point (x, y) with respect to the ordinate y of an arbitrary prediction point (x, y), and S(x, y) represents the terrain slope of an arbitrary prediction point (x, y).

[0030] As a preferred embodiment, the runoff model is expressed as:

[0031] Q(x, y, t) = α(x, y)R n (t) + β(x, y)H i (x, y, t) + γ(x, y)S(x, y);

[0032]

[0033]

[0034] Among them, Q(x, y, t) represents the runoff at an arbitrary prediction point (x, y) at time t, α(x, y) represents the rainfall coefficient, β(x, y) represents the hydrological coefficient, γ(x, y) represents the terrain coefficient, and R n (ti ) represents the rainfall data at time t after standardization, H i i i (x i ,y i ,t i ) represents the i-th consecutive i-th predicted point (x i ,y i ) at time t, and S(x i ,y i ,y i ) represents the i-th predicted point (x i ,y i ) of the terrain slope.

[0035] As a preferred embodiment, the flood routing model is expressed as:

[0036]

[0037] v(x,y) = k(x,y)S(x,y);

[0038]

[0039] T(x,y) = min t {t|W(x,y,t) ≥ W threshold};

[0040] Among them, represents the partial derivative of the water level W(x,y,t) at any predicted point (x,y) at time t with respect to the continuous time variable t, represents the gradient operator, represents the divergence, v(x,y) represents the water flow velocity at any predicted point (x,y), k(x,y) represents the hydraulic conductivity at any predicted point (x,y), T(x,y) represents the flood arrival time at any predicted point (x,y), W threshold represents the preset water level threshold, k i represents the preset i-th hydraulic conductivity parameter related to soil or terrain conditions.

[0041] As a preferred embodiment, the calculation method of the flood risk index is:

[0042]

[0043] Among them, λ 1 、λ 2 、λ 3 represent the preset risk coefficients, R(x,y) represents the flood risk index at any predicted point (x,y), Denote the time-accumulated water level, and \(W(x, y, \tau)\) represents the water level at any prediction point \((x, y)\) within the time range \(\tau\), where \(\tau\in[0, t]\).

[0044] On the other hand, the present invention also provides a flood prevention prediction system, including:

[0045] Data acquisition module: Obtain rainfall data, terrain data, and hydrological data of the prediction point, and perform standardization processing to obtain the standardized rainfall data, terrain data, and hydrological data;

[0046] Data preprocessing module: Use the interpolation algorithm for the standardized terrain data and hydrological data to obtain continuous terrain data and hydrological data, and calculate the terrain slope of the prediction point through the continuous terrain data;

[0047] Runoff model module: Construct a runoff model based on the standardized rainfall data, continuous hydrological data, and terrain slope, and obtain the runoff volume of the prediction point through the runoff model;

[0048] Flood propagation model module: Construct a flood propagation model based on the runoff volume and terrain slope, and obtain the flood arrival time and water level through the flood propagation model;

[0049] Prediction module: Calculate the flood disaster risk index based on the flood arrival time and water level.

[0050] The present invention has the following beneficial effects:

[0051] The present invention comprehensively considers rainfall data, terrain data, and hydrological data, and generates continuous data through standardization processing and interpolation algorithms, enabling the model to more comprehensively reflect the actual hydrological process. This multi-source data fusion method can effectively improve the accuracy of flood prediction. By constructing a runoff model and a flood propagation model, the present invention can dynamically simulate the occurrence, development, and propagation process of floods, and is more adaptable to complex hydrological conditions compared with traditional static models. Standardizing the input data can eliminate the dimensional differences and data range differences between different data sources, making the model more adaptable to data from different regions. Generating continuous terrain and hydrological data through the interpolation algorithm solves the problem of data discretization and improves the applicability of the model under complex terrain conditions. By calculating the flood disaster risk index, the flood risk is quantitatively evaluated, providing an intuitive risk level division for decision-makers and facilitating the formulation of targeted disaster prevention and mitigation measures. Predicting the flood arrival time and water level change through the flood propagation model can provide a more accurate time window for emergency response and resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flowchart of the method implementation of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0053] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0054] It should be understood that the step numbers used herein are only for convenient description and do not limit the execution order of the steps.

[0055] It should be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0056] The terms "comprising" and "including" indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0057] The term " / or" refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0058] Embodiment 1:

[0059] Refer to Figure 1 , the present invention provides a flood disaster prediction method, including the following steps:

[0060] Obtain the rainfall data, terrain data and hydrological data of the prediction point, and perform standardization processing to obtain the standardized rainfall data, terrain data (terrain elevation value) and hydrological data (including time, spatial coordinates and water flow velocity);

[0061] Use the interpolation algorithm for the standardized terrain data and hydrological data to obtain continuous terrain data and hydrological data, and calculate the terrain slope of the prediction point through the continuous terrain data;

[0062] Construct a runoff model according to the standardized rainfall data, continuous hydrological data and terrain slope, and obtain the runoff volume of the prediction point through the runoff model;

[0063] Construct a flood evolution model according to the runoff volume and terrain slope, and obtain the flood arrival time and water level through the flood evolution model;

[0064] Calculate the flood risk index based on the flood arrival time and water level.

[0065] As a preferred implementation, the method for standardizing the rainfall data is as follows:

[0066]

[0067] where R n (t) represents the rainfall data at time t after standardization, R(t) represents the rainfall data at time t before standardization, N represents the preset maximum number, i, j, g represent digit indices, t represents a continuous time variable, t i , t j , t g represent the i-th, j-th, and g-th discrete points selected from the continuous time variable t, R(t i ) represents the rainfall data at time t i before standardization, R(t j ) represents the rainfall data at time t j before standardization, R(t g ) represents the rainfall data at time t g before standardization;

[0068] The method for standardizing the terrain data is as follows:

[0069]

[0070] where D n (x, y) represents the terrain data of any prediction point (x, y) after standardization, D(x, y) represents the terrain data of any prediction point (x, y) before standardization, D(x i , y i ) represents the terrain data of the i-th prediction point (x i , y i ) before standardization, D(x j , y j ) represents the terrain data of the j-th prediction point (x j , y j ) before standardization, D(x g , y g ) represents the terrain data of the g-th prediction point (x g , y g ) before standardization;

[0071] The method for standardizing the hydrological data is as follows:

[0072]

[0073] where Hn (x, y, y) represents the hydrological data of any prediction point (x, y) at time t after standardization, and H(x, y, t) represents the hydrological data of any prediction point (x, y) at time t before standardization, H(x i , y i , t i ) represents the hydrological data of the i-th prediction point (x i , y i ) at t i moment, H(x j , y j , t j ) represents the hydrological data of the j-th prediction point (x j , y j ) at t j moment, H(x g , y g , t g ) represents the hydrological data of the g-th prediction point (x g , y g ) at t g moment.

[0074] As a preferred implementation manner, the method for obtaining continuous terrain data is:

[0075]

[0076] The method for obtaining continuous hydrological data is:

[0077]

[0078]

[0079] Among them, D i (x, y) represents the terrain data of the i-th continuous arbitrary prediction point (x, y), D n (x i , y i ) represents the terrain data of the i-th prediction point (x i , y i ) after standardization, W i (x, y) represents the i-th inverse distance weight, H i (x, y, t) represents the hydrological data of the i-th continuous arbitrary prediction point (x, y) at time t, H n (x i , y i , t) represents the hydrological data of the i-th prediction point (x i , y i ) at time t; x iDenote the abscissa of the \(i\)-th prediction point \((x i , y i ), \(y i \) denotes the ordinate of the \(i\)-th prediction point \((x i , y i ). \(x\) represents the abscissa of any prediction point \((x, y)\), and \(y\) represents the ordinate of any prediction point \((x, y)\);

[0080] The calculation method of the terrain slope is as follows:

[0081]

[0082] Among them, denotes the square of the partial derivative of the terrain data \(D i (x, y)\) with respect to the abscissa \(x\) of any prediction point \((x, y)\), denotes the square of the partial derivative of the terrain data \(D i (x, y)\) with respect to the ordinate \(y\) of any prediction point \((x, y)\). \(S(x, u)\) represents the terrain slope of any prediction point \((x, y)\).

[0083] As a preferred embodiment, the runoff model is expressed as:

[0084] Q(x, y, t) = α(x, y)R n (t) + β(x, y)H i (x, y, y) + γ(x, y)S(x, y);

[0085]

[0086] Among them, \(Q(x, y, t)\) represents the runoff at time \(t\) of any prediction point \((x, y)\), \(α(x, y)\) represents the rainfall coefficient, \(β(x, y)\) represents the hydrological coefficient, \(γ(x, y)\) represents the terrain coefficient, \(R n (t i ) represents the rainfall data at time \(t i \) after standardization, \(H i (x i , y i , t i ) represents the hydrological data of the \(i\)-th consecutive \(i\)-th prediction point \((x i , y i ) at time \(t i . \(S(x i , y i ) represents the terrain slope of the \(i\)-th prediction point \((x i , y i ).

[0087] As a preferred embodiment, the flood routing model is expressed as:

[0088]

[0089] v(x,y) = k(x,y)S(x,y);

[0090]

[0091] T(x,y) = min t {t|W(x,y,t) ≥ W threshold};

[0092] Wherein, represents the partial derivative of the water level W(x,y,t) at any prediction point (x,y) with respect to the continuous time variable t, represents the gradient operator, represents the divergence, v(x,y) represents the water flow velocity at any prediction point (x,y), k(x,y) represents the hydraulic conductivity at any prediction point (x,y), T(x,y) represents the flood arrival time at any prediction point (x,y), W threshold represents the preset water level threshold, k i represents the preset i-th hydraulic conductivity parameter related to soil or terrain conditions;

[0093] The divergence can be expanded as:

[0094]

[0095] Wherein, v x (x,y), v y (x,y) represent two components of the water flow velocity v(x,y), v(x,y) = (v x (x,y), v y (x,y)).

[0096] As a preferred embodiment, the calculation method of the flood disaster risk index is:

[0097]

[0098] Wherein, λ 1 , λ 2 , λ 3 represent the preset risk coefficients, R(x,y) represents the flood disaster risk index at any prediction point (x,y) (by setting the flood disaster risk index threshold, the risk is divided into level 1, level 2, and level 3, where level 1 represents safety, level 2 represents general, and level 3 represents danger), Denote the time-accumulated water level, and \(W(x, y, \tau)\) represents the water level at any prediction point \((x, y)\) within the time range \(\tau\), where \(\tau\in[0, t]\) represents the continuous time range from 0 to \(t\).

[0099] Embodiment 2:

[0100] The present invention also provides a flood prevention prediction system, including:

[0101] Data acquisition module: Obtain the rainfall data, terrain data, and hydrological data of the prediction point, and perform standardization processing to obtain the standardized rainfall data, terrain data, and hydrological data;

[0102] Data preprocessing module: Use the interpolation algorithm for the standardized terrain data and hydrological data to obtain continuous terrain data and hydrological data, and calculate the terrain slope of the prediction point through the continuous terrain data;

[0103] Runoff model module: Construct a runoff model based on the standardized rainfall data, continuous hydrological data, and terrain slope, and obtain the runoff volume of the prediction point through the runoff model;

[0104] Flood propagation model module: Construct a flood propagation model based on the runoff volume and terrain slope, and obtain the flood arrival time and water level through the flood propagation model;

[0105] Prediction module: Calculate the flood disaster risk index based on the flood arrival time and water level.

[0106] This system is used to implement the method in Embodiment 1, which will not be elaborated here.

[0107] In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. "And / or" describes the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent the situation of A existing alone, A and B existing simultaneously, and B existing alone. Where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the front and back associated objects. "At least one of the following" and its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0108] Those of ordinary skill in the art will realize that the various units and algorithm steps described in the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0109] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated here.

[0110] In several embodiments provided in this application, if any function 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 technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memories (hereinafter referred to as ROMs), random access memories (hereinafter referred to as RAMs), magnetic disks, or optical discs, etc., which can store program codes.

[0111] The above are only the embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A flood prevention and prediction method, characterized in that: The following steps are involved: Obtaining rainfall data, topographic data and hydrological data of the prediction point, and performing standardization processing to obtain standardized rainfall data, topographic data and hydrological data; The interpolation algorithm is used on the standardized terrain data and hydrological data to obtain continuous terrain data and hydrological data, and the terrain slope of the prediction point is calculated through the continuous terrain data; A runoff model is constructed based on standardized rainfall data, continuous hydrological data and terrain slope, and the runoff volume at the prediction point is obtained through the runoff model; A flood evolution model is constructed according to the runoff and the terrain slope, and the flood arrival time and water level are obtained through the flood evolution model; The flood risk index is calculated based on the flood arrival time and water level.

2. The flood prevention and prediction method according to claim 1, characterized in that: The standardized processing method of the rainfall data is: Among them, R n (t) represents the rainfall data at time t after standardization, R(t) represents the rainfall data at time t before standardization, N represents the preset maximum number, t represents a continuous time variable, and t i ,t j ,t g represents the i-th discrete point, j-th discrete point, and g-th discrete point selected in the continuous time variable t, R(t i ) represents the value before standardization i The rainfall data at the time, R(t j ) represents the value before standardization j The rainfall data at the time, R(t g ) represents the value before standardization g Rainfall data at the moment; The standardized processing method of the terrain data is: Among them, D n (x, y) represents the terrain data of any predicted point (x, y) after normalization, D(x, y) represents the terrain data of any predicted point (x, y) before normalization, and D(x i ,y i ) represents the i-th prediction point (x i ,y i ) terrain data, D(x j ,y j ) represents the jth prediction point (x j ,y j ) terrain data, D(x g ,y g ) represents the g-th prediction point (x g ,y g )’s terrain data; The standardized processing method of the hydrological data is: Among them, H n (x, y, t) represents the hydrological data of any predicted point (x, y) at time t after standardization, H(x, y, t) represents the hydrological data of any predicted point (x, y) at time t before standardization, and H(x i ,y i , t i ) represents the i-th prediction point (x i ,y i ) at t i Hydrological data at the time, H(x j ,y j , t j ) represents the jth prediction point (x j ,y j ) at t j Hydrological data at the time, H(x g ,y g , t g ) represents the g-th prediction point (x g ,y g ) at t g Hydrological data at the moment.

3. The flood prevention and prediction method according to claim 2, characterized in that: The continuous terrain data acquisition method is: The continuous hydrological data acquisition method is: Among them, D i (x, y) represents the terrain data of the i-th continuous arbitrary prediction point (x, y), D n (x i ,y i ) represents the i-th prediction point (x i ,y i ) terrain data, W i (x, y) represents the i-th inverse distance weight, H i (x, y, t) represents the hydrological data of the ith continuous arbitrary prediction point (x, y) at time t, H n (x i ,y i , t) represents the i-th prediction point (x i ,y i ) Hydrological data at time t; x i represents the i-th prediction point (x i ,y i )’s horizontal coordinate, y i represents the i-th prediction point (x i ,y i ), x represents the horizontal coordinate of any predicted point (x, y), and y represents the vertical coordinate of any predicted point (x, y); The calculation method of the terrain slope is: in, Represents the terrain data D of the i-th continuous arbitrary prediction point (x, y) i The square of the partial derivative of (x, y) with respect to the horizontal coordinate x of any prediction point (x, y), Represents the terrain data D of the i-th continuous arbitrary prediction point (x, y) i S(x, y) is the square of the partial derivative of (x, y) with respect to the ordinate y of any prediction point (x, y). S(x, y) represents the terrain slope of any prediction point (x, y).

4. The flood prevention and prediction method according to claim 3, characterized in that: The runoff model is expressed as: Q(x,y,t)=α(x,y)R n (t)+β(x,y)H i (x,y,t)+γ(x,y)S(x,y); Among them, Q(x, y, t) represents the runoff of any predicted point (x, y) at time t, α(x, y) represents the rainfall coefficient, β(x, y) represents the hydrological coefficient, γ(x, y) represents the terrain coefficient, and R n (t i ) indicates the standardized i The rainfall data at the time, H i (x i ,y i , t i ) represents the i-th consecutive prediction point (x i ,y i ) at t i Hydrological data at the time, S(x i ,y i ) represents the i-th prediction point (x i ,y i ) of the terrain slope.

5. The flood prevention and prediction method according to claim 4, characterized in that: The flood evolution model is expressed as: T(x,y)=min t {t|W(x,y,t)≥W threshold }; in, It represents the partial derivative of the water level W(x, y, t) at any prediction point (x, y) at time t with respect to the continuous time variable t. represents the gradient operator, represents the divergence, v(x, y) represents the water velocity at any prediction point (x, y), k(x, y) represents the hydraulic conductivity at any prediction point (x, y), T(x, y) represents the flood arrival time at any prediction point (x, y), W threshold Indicates the preset water level threshold, k i Represents the preset i-th hydraulic conductivity parameter related to soil or terrain conditions.

6. The flood prevention and prediction method according to claim 5, characterized in that: The calculation method of the flood risk index is: Among them, λ1, λ2, and λ3 represent the preset risk coefficients, and R(x, y) represents the flood risk index of any prediction point (x, y). Represents the time-accumulated water level, W(x, y, τ) predicts the water level of any point (x, y) in the time range τ, τ∈[0, t].

7. A flood prevention prediction system, characterized in that: include: Data collection module: obtains rainfall data, terrain data and hydrological data of the forecast point, and performs standardized processing to obtain standardized rainfall data, terrain data and hydrological data; Data preprocessing module: Use interpolation algorithm to obtain continuous terrain data and hydrological data after standardized processing, and calculate the terrain slope of the prediction point through continuous terrain data; Runoff model module: build a runoff model based on standardized rainfall data, continuous hydrological data and terrain slope, and obtain the runoff volume at the prediction point through the runoff model; Flood evolution model module: construct a flood evolution model according to the runoff and terrain slope, and obtain the flood arrival time and water level through the flood evolution model; Prediction module: Calculates the flood risk index according to the flood arrival time and water level.

8. The flood prevention prediction system according to claim 7, characterized in that: The standardized processing method of the rainfall data is: Among them, R n (t) represents the rainfall data at time t after standardization, R(t) represents the rainfall data at time t before standardization, N represents the preset maximum number, i, j, g represent the digit index, t represents the continuous time variable, t i ,t j ,t g represents the i-th discrete point, j-th discrete point, and g-th discrete point selected in the continuous time variable t, R(t i ) represents the value before standardization i The rainfall data at the time, R(t j ) represents the value before standardization j The rainfall data at the time, R(t g ) represents the value before standardization g Rainfall data at the moment; The standardized processing method of the terrain data is: Among them, D n (x, y) represents the terrain data of any predicted point (x, y) after normalization, D(x, y) represents the terrain data of any predicted point (x, y) before normalization, and D(x i ,y i ) represents the i-th prediction point (x i ,y i ) terrain data, D(x j ,y j ) represents the jth prediction point (x j ,y j ) terrain data, D(x g ,y g ) represents the g-th prediction point (x g ,y g )’s terrain data; The standardized processing method of the hydrological data is: Among them, H n (x, y, t) represents the hydrological data of any predicted point (x, y) at time t after standardization, H(x, y, t) represents the hydrological data of any predicted point (x, y) at time t before standardization, and H(x i ,y i , t i ) represents the i-th prediction point (x i ,y i ) at t i Hydrological data at the time, H(x j ,y j , t j ) represents the jth prediction point (x j ,y j ) at t j Hydrological data at the time, H(x g ,y g , t g ) represents the g-th prediction point (x g ,y g ) at t g Hydrological data at the moment.

9. The flood prevention prediction system according to claim 8, characterized in that: The continuous terrain data acquisition method is: The continuous hydrological data acquisition method is: Among them, D i (x, y) represents the terrain data of the i-th continuous arbitrary prediction point (x, y), D n (x i ,y i ) represents the i-th prediction point (x i ,y i ) terrain data, W i (x, y) represents the i-th inverse distance weight, H i (x, y, t) represents the hydrological data of the ith continuous arbitrary prediction point (x, y) at time t, H n (x i ,y i , t) represents the i-th prediction point (x i ,y i ) Hydrological data at time t; x i represents the i-th prediction point (x i ,y i )’s horizontal coordinate, y i represents the i-th prediction point (x i ,y i ), x represents the horizontal coordinate of any predicted point (x, y), and y represents the vertical coordinate of any predicted point (x, y); The calculation method of the terrain slope is: in, Represents the terrain data D of the i-th continuous arbitrary prediction point (x, y) i The square of the partial derivative of (x, y) with respect to the horizontal coordinate x of any prediction point (x, y), Represents the terrain data D of the i-th continuous arbitrary prediction point (x, y) i S(x, y) is the square of the partial derivative of (x, y) with respect to the ordinate y of any prediction point (x, y). S(x, y) represents the terrain slope of any prediction point (x, y).

10. The flood prevention prediction system according to claim 9, characterized in that: The runoff model is expressed as: Q(x,y,t)=α(x,y)R n (t)+β(x,y)H i (x,y,t)+γ(x,y)S(x,y); Among them, Q(x, y, t) represents the runoff of any predicted point (x, y) at time t, α(x, y) represents the rainfall coefficient, β(x, y) represents the hydrological coefficient, γ(x, y) represents the terrain coefficient, and R n (t i ) indicates the standardized i The rainfall data at the time, H i (x i ,y i , t i ) represents the i-th consecutive prediction point (x i ,y i ) at t i Hydrological data at the time, S(x i ,y i ) represents the i-th prediction point (x i ,y i ) of the terrain slope.

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