Hydrometeorological comprehensive monitoring method and system for early warning of water conservancy disasters

Abstract

The invention relates to a hydrometeorological comprehensive monitoring method and system for water conservancy disaster early warning, and the method comprises the steps: firstly collecting the rainfall, soil humidity, temperature, wind speed, coordinates and other hydrometeorological data in a drainage basin, and carrying out the denoising and smoothing preprocessing; secondly, respectively constructing a hydrological model and a meteorological model based on the preprocessed data, and calculating to obtain runoff and evaporation capacity of the drainage basin; then, combining the runoff volume, the evaporation capacity and the coordinate data, establishing a flood routing model, and solving by adopting a finite difference method to obtain the flow and the water level of each measurement point; and finally, water conservancy disaster early warning is issued in real time according to a calculation result. Through systematized data acquisition, model construction and early warning release, all-directional real-time monitoring of water conservancy disasters is realized, and the accuracy and timeliness of disaster early warning are remarkably improved.

Description

Technical Field

[0001] The invention relates to a hydrological and meteorological integrated monitoring method and system for water disaster early warning, belonging to the technical field of hydrological, water resources and meteorological monitoring. Background Art

[0002] In the field of hydrology, water resources and meteorological monitoring technology, the integrated hydrological and meteorological monitoring method for water disaster warning has always been a hot topic of research. With the frequent occurrence of global climate change and extreme weather events, water disasters such as floods and droughts have posed a huge threat to human society and economy. Traditional water disaster warning methods mainly rely on manual observation and continuous monitoring of fixed sites. These methods have many shortcomings, such as delayed observation data, low degree of automation, and limited monitoring range. Specifically, manual observation is not only time-consuming and labor-intensive, but also easily affected by human factors, resulting in inaccurate data. Although continuous monitoring of fixed sites can provide certain data support, due to the limited number of sites, it is often unable to fully cover the basin to be tested, making it difficult to accurately reflect the hydrological and meteorological conditions of the entire basin.

[0003] In addition, traditional detection methods also have the problem of insufficient data processing and analysis capabilities. Due to the large amount and complexity of data, traditional data processing methods often cannot extract useful information in a timely and accurate manner, resulting in poor warning effects. At the same time, traditional analysis methods also lack in-depth research on the interaction between different hydrological and meteorological elements, which limits the accuracy and reliability of the warning model.

[0004] The patent document with the patent number "CN116955450A" has developed a hydrological forecasting method and system that integrates the temporal and spatial processes of watershed runoff. The problem with this method is that although this method contains multiple modules (such as watershed rainfall analysis module, surface water volume calculation module, etc.), it may be slightly insufficient in terms of the integrity and systematicness of model construction. In particular, it is not clear how to integrate these modules into a unified framework to achieve comprehensive water disaster warning. Although functions such as disaster risk assessment and economic loss assessment are also provided, the accuracy and real-time nature of the warning may be affected by the collaborative efficiency between modules and the data processing speed. In particular, the warning capability when processing large-scale data and complex geographical conditions may be subject to certain limitations. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present invention proposes a hydrological and meteorological integrated monitoring method and system for water disaster early warning.

[0006] The technical solution of the present invention is as follows:

[0007] On the one hand, the present invention provides a hydrological and meteorological integrated monitoring method for water disaster early warning, comprising the following steps:

[0008] Collecting comprehensive hydrological and meteorological data of the basin to be measured, the comprehensive hydrological and meteorological data including rainfall, soil moisture, temperature, wind speed and coordinates of measuring points, and preprocessing the comprehensive hydrological and meteorological data, the preprocessing including denoising and smoothing;

[0009] Constructing a hydrological model of the watershed to be measured according to the preprocessed hydrological and meteorological comprehensive data, and obtaining the runoff of the watershed to be measured through the hydrological model;

[0010] Constructing a meteorological model of the watershed to be measured according to the preprocessed hydrological and meteorological comprehensive data, and obtaining the evaporation of the watershed to be measured through the meteorological model;

[0011] A flood evolution model is constructed according to the runoff, evaporation and coordinates of the measuring point, and the flow and water level of the measuring point are obtained by solving the flood evolution model by a finite difference method;

[0012] A water disaster warning for the measuring point is issued based on the flow and water level.

[0013] As a preferred embodiment, the pretreatment method is:

[0014] P ct (t) = P raw (t)+α·ΔP(t)+β·Smooth(P raw (t));

[0015] Among them, P ct (t) represents the comprehensive hydrological and meteorological data at time t after preprocessing, P raw (t) represents the comprehensive hydrological and meteorological data at time t before preprocessing, α represents the preset noise correction coefficient, ΔP(t) represents the change of the comprehensive hydrological and meteorological data, β represents the preset smoothing coefficient, Smooth(P raw (t)) represents the smoothed P raw (t), t represents time;

[0016] The calculation method of the change ΔP(t) is:

[0017] ΔP(t)=P raw (t)-P raw (t-Δt);

[0018] Wherein, Δt represents a preset time variable.

[0019] As a preferred implementation, the hydrological model is expressed as:

[0020]

[0021] I a =0.2ln(S+1);

[0022] Where Q represents the runoff, I a represents the loss value of rainfall, P represents rainfall, S represents soil water absorption, CN represents the preset soil curve number, ε represents the preset soil influence weight, SM represents soil moisture, γ represents the preset temperature influence weight, T represents temperature, θ represents the preset wind speed influence weight, and W represents wind speed.

[0023] As a preferred implementation, the meteorological model is expressed as:

[0024]

[0025] R n =R ns +R nl ;

[0026]

[0027] e a =e s ×RH;

[0028]

[0029] Among them, E T represents the evaporation rate, p a represents the air density, c p is the specific heat capacity of air, e s Indicates the preset saturated water vapor pressure, e a represents the actual water vapor pressure, φ represents the slope of the saturated water vapor pressure curve, R n represents the net radiation, G soil heat flux, r a represents the resistance of air to water evaporation, τ represents the preset latent heat of vaporization of water, r s represents the preset vegetation resistance to water evaporation, σ represents the preset time first-order derivative weight, represents the first-order derivative of temperature with respect to time, μ represents the preset slope factor, and R ns represents the shortwave net radiation, R nl represents the long-wave net radiation, p s represents soil density, c s is the specific heat capacity of soil, represents the time rate of change of soil temperature, RH represents the preset relative humidity, ks represents the preset Karman constant, z represents the preset height, and d represents the preset zero plane displacement.

[0030] As a preferred implementation, the flood evolution model is expressed as:

[0031]

[0032] Where x represents the coordinates of the measuring point, A represents the preset water-passing cross-sectional area of ​​the measuring point, g represents the gravitational acceleration, Q loss represents the preset water loss value, h represents the water level, δ,∈ represents the preset adjustment coefficient, S f represents the friction slope, represents the first-order derivative of runoff with respect to time, Indicates (Q 2 / A) the first derivative of the coordinates of the measuring point, represents the first derivative of the water level with respect to the coordinates of the measuring point, represents the second-order derivative of the runoff with respect to the coordinates of the measuring point, Represents the first-order derivative of the preset water-passing cross-sectional area at the measuring point with respect to time, represents the first derivative of runoff with respect to the coordinates of the measuring point, Represents the second-order derivative of the preset water-passing cross-sectional area of ​​the measuring point with respect to the coordinates of the measuring point;

[0033] The flood evolution model is gridded and discretized by the finite difference method, and the flow Q(x, t) and water level h(x, t) at the measuring point at time t are obtained through iterative solution.

[0034] On the other hand, the present invention also provides a hydrological and meteorological integrated monitoring system for water disaster early warning, comprising:

[0035] Data acquisition module: collects the hydrological and meteorological comprehensive data of the basin to be measured, the hydrological and meteorological comprehensive data includes rainfall, soil moisture, temperature, wind speed and coordinates of the measuring points, and pre-processes the hydrological and meteorological comprehensive data, the pre-processing includes denoising and smoothing;

[0036] Hydrological model building module: constructing a hydrological model of the basin to be measured based on the pre-processed hydrological and meteorological comprehensive data, and obtaining the runoff of the basin to be measured through the hydrological model;

[0037] Meteorological model building module: building a meteorological model of the basin to be measured based on the pre-processed hydrological and meteorological comprehensive data, and obtaining the evaporation of the basin to be measured through the meteorological model;

[0038] Flood evolution model construction module: construct a flood evolution model according to the runoff, evaporation and coordinates of the measuring point, and solve the flood evolution model by finite difference method to obtain the flow and water level of the measuring point;

[0039] Disaster warning module: issues water disaster warnings for the measurement point based on the flow and water level.

[0040] The present invention has the following beneficial effects:

[0041] The hydrological model constructed by the present invention based on the pre-processed data can accurately predict the runoff of the basin to be measured, providing key hydrological information for water disaster warning. The constructed meteorological model can scientifically estimate the evaporation of the basin to be measured, which is of great significance for understanding the water cycle process in the basin and evaluating the water resources situation. The flood evolution model constructed by combining the runoff, evaporation and the coordinates of the measuring points can be solved by the finite difference method, and the flow and water level of the measuring points can be predicted in real time, providing a basis for timely warning of water disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 The present invention is a flowchart for implementing the method. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0045] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0046] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0047] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0048] Embodiment 1:

[0049] See also Figure 1 The present invention provides a hydrological and meteorological integrated monitoring method for water disaster early warning, comprising the following steps:

[0050] Collecting comprehensive hydrological and meteorological data of the watershed to be measured, the comprehensive hydrological and meteorological data includes rainfall, soil moisture, temperature, wind speed and coordinates of measuring points (since the watershed to be measured is relatively wide, the coordinates of specific measuring points in the watershed to be measured can more accurately feedback the situation of the measuring points), and preprocessing the comprehensive hydrological and meteorological data, the preprocessing includes denoising and smoothing;

[0051] Constructing a hydrological model of the watershed to be measured according to the preprocessed hydrological and meteorological comprehensive data, and obtaining the runoff of the watershed to be measured through the hydrological model;

[0052] Constructing a meteorological model of the watershed to be measured according to the preprocessed hydrological and meteorological comprehensive data, and obtaining the evaporation of the watershed to be measured through the meteorological model;

[0053] A flood evolution model is constructed according to the runoff, evaporation and coordinates of the measuring point, and the flow and water level of the measuring point are obtained by solving the flood evolution model by a finite difference method;

[0054] A water disaster warning for the measuring point is issued based on the flow and water level.

[0055] As a preferred embodiment, the pretreatment method is:

[0056] P ct (t) = P raw (t)+α·ΔP(t)+β·Smooth(P raw (t));

[0057] Among them, P ct (t) represents the comprehensive hydrological and meteorological data at time t after preprocessing, P raw (t) represents the comprehensive hydrological and meteorological data at time t before preprocessing, α represents the preset noise correction coefficient, ΔP(t) represents the change of the comprehensive hydrological and meteorological data, β represents the preset smoothing coefficient, Smooth(P raw (t)) represents the smoothed P raw (t), t represents time;

[0058] The calculation method of the change ΔP(t) is:

[0059] ΔP(t)=P raw (t)-P raw (t-Δt);

[0060] Wherein, Δt represents a preset time variable.

[0061] As a preferred implementation, the hydrological model is expressed as:

[0062]

[0063] I a =0.2ln(S+1);

[0064] Where Q represents the runoff, I a represents the loss value of rainfall, P represents rainfall, S represents soil water absorption, CN represents the preset soil curve number, ε represents the preset soil influence weight, SM represents soil moisture, γ represents the preset temperature influence weight, T represents temperature, θ represents the preset wind speed influence weight, and W represents wind speed.

[0065] As a preferred implementation, the meteorological model is expressed as:

[0066]

[0067] R n =R ns +R nl ;

[0068]

[0069] e a =e S ×RH;

[0070]

[0071] Among them, E T represents the evaporation rate, p a Indicates air density (preset according to site conditions), c p Indicates the specific heat capacity of air (preset according to on-site conditions), e s Indicates the preset saturated water vapor pressure, e a represents the actual water vapor pressure, Represents the slope of the saturated water vapor pressure curve, R n represents the net radiation, G soil heat flux, r a represents the resistance of air to water evaporation, τ represents the preset latent heat of vaporization of water, r s represents the preset vegetation resistance to water evaporation, σ represents the preset time first-order derivative weight, represents the first-order derivative of temperature with respect to time, μ represents the preset slope factor, and R ns represents the shortwave net radiation, R nl represents the long-wave net radiation, p s represents soil density, c s is the specific heat capacity of soil, represents the time rate of change of soil temperature, RH represents the preset relative humidity, ks represents the preset Karman constant, z represents the preset height, and d represents the preset zero plane displacement.

[0072] As a preferred implementation, the flood evolution model is expressed as:

[0073]

[0074] Where x represents the coordinates of the measuring point, A represents the preset water-passing cross-sectional area of ​​the measuring point, g represents the gravitational acceleration, Q loss represents a preset water loss value (including the rainfall loss value I introduced in this embodiment) a , soil water absorption S, which is only used as an example and not limited here), h represents the water level, δ, ∈ represent the preset adjustment coefficients, S f represents the friction slope (which can be obtained by the Manning formula in this embodiment), represents the first-order derivative of runoff with respect to time, Indicates (Q 2 / A) the first derivative of the coordinates of the measuring point, represents the first derivative of the water level with respect to the coordinates of the measuring point, represents the second-order derivative of the runoff with respect to the coordinates of the measuring point, Indicates the first-order derivative of the preset water-passing cross-sectional area of ​​the measuring point with respect to time, represents the first derivative of runoff with respect to the coordinates of the measuring point, Represents the second-order derivative of the preset water-passing cross-sectional area of ​​the measuring point with respect to the coordinates of the measuring point;

[0075] The flood evolution model is meshed and discretized by the finite difference method, and then the flow Q(x, t) and water level h(x, t) at the measuring point at time t are obtained through iterative solution (it can also be solved by the finite element method. The required data can be obtained by conventional solution through the above two methods, which will not be explained in detail here).

[0076] Embodiment 2:

[0077] The present invention also provides a hydrological and meteorological integrated monitoring system for water disaster early warning, comprising:

[0078] Data acquisition module: collects the hydrological and meteorological comprehensive data of the basin to be measured, the hydrological and meteorological comprehensive data includes rainfall, soil moisture, temperature, wind speed and coordinates of the measuring points, and pre-processes the hydrological and meteorological comprehensive data, the pre-processing includes denoising and smoothing;

[0079] Hydrological model building module: constructing a hydrological model of the basin to be measured based on the pre-processed hydrological and meteorological comprehensive data, and obtaining the runoff of the basin to be measured through the hydrological model;

[0080] Meteorological model building module: building a meteorological model of the basin to be measured based on the pre-processed hydrological and meteorological comprehensive data, and obtaining the evaporation of the basin to be measured through the meteorological model;

[0081] Flood evolution model construction module: construct a flood evolution model according to the runoff, evaporation and coordinates of the measuring point, and solve the flood evolution model by finite difference method to obtain the flow and water level of the measuring point;

[0082] Disaster warning module: issues water disaster warnings for the measurement point based on the flow and water level.

[0083] The system is used to implement the method in Example 1, which will not be described in detail here.

[0084] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0085] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, 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 this application.

[0086] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0087] In several embodiments provided in the present 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 the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0088] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A hydrological and meteorological integrated monitoring method for water disaster early warning, characterized in that: The following steps are involved: Collecting comprehensive hydrological and meteorological data of the basin to be measured, the comprehensive hydrological and meteorological data including rainfall, soil moisture, temperature, wind speed and coordinates of measuring points, and preprocessing the comprehensive hydrological and meteorological data, the preprocessing including denoising and smoothing; Constructing a hydrological model of the watershed to be measured according to the preprocessed hydrological and meteorological comprehensive data, and obtaining the runoff of the watershed to be measured through the hydrological model; Constructing a meteorological model of the watershed to be measured according to the preprocessed hydrological and meteorological comprehensive data, and obtaining the evaporation of the watershed to be measured through the meteorological model; A flood evolution model is constructed according to the runoff, evaporation and coordinates of the measuring point, and the flow and water level of the measuring point are obtained by solving the flood evolution model by a finite difference method; A water disaster warning for the measuring point is issued based on the flow and water level.

2. The hydrological and meteorological integrated monitoring method for water disaster early warning according to claim 1 is characterized in that: The pretreatment method is: P ct (t)=P raw (t)+αΔP(t)+β·Smooth(P raw (t)); Among them, P ct (t) represents the comprehensive hydrological and meteorological data at time t after preprocessing, P raw (t) represents the comprehensive hydrological and meteorological data at time t before preprocessing, α represents the preset noise correction coefficient, ΔP(t) represents the change of the comprehensive hydrological and meteorological data, β represents the preset smoothing coefficient, Smooth(P raw (t)) represents the smoothed P raw (t), t represents time; The calculation method of the change ΔP(t) is: ΔP(t)=P raw (t)-P raw (t-Δt); Wherein, Δt represents a preset time variable.

3. The hydrological and meteorological integrated monitoring method for water disaster early warning according to claim 2 is characterized in that: The hydrological model is expressed as: I a =0.2ln(S+1); Where Q represents the runoff, I a represents the loss value of rainfall, P represents rainfall, S represents soil water absorption, CN represents the preset soil curve number, ε represents the preset soil influence weight, SM represents soil moisture, γ represents the preset temperature influence weight, T represents temperature, θ represents the preset wind speed influence weight, and W represents wind speed.

4. The hydrological and meteorological integrated monitoring method for water disaster early warning according to claim 2 is characterized in that: The meteorological model is expressed as: R n =R ns +R nl ; And a =and s ×RH; Among them, E T represents the evaporation rate, p a represents the air density, c p is the specific heat capacity of air, e s Indicates the preset saturated water vapor pressure, e a represents the actual water vapor pressure, φ represents the slope of the saturated water vapor pressure curve, R n represents the net radiation, G soil heat flux, r a represents the resistance of air to water evaporation, τ represents the preset latent heat of vaporization of water, r s represents the preset vegetation resistance to water evaporation, σ represents the preset time first-order derivative weight, represents the first-order derivative of temperature with respect to time, μ represents the preset slope factor, and R ns represents the shortwave net radiation, R nl represents the long-wave net radiation, p s represents soil density, c s is the specific heat capacity of soil, represents the time rate of change of soil temperature, RH represents the preset relative humidity, ks represents the preset Karman constant, z represents the preset height, and d represents the preset zero plane displacement.

5. The hydrological and meteorological integrated monitoring method for water disaster early warning according to claim 3 or 4, characterized in that: The flood evolution model is expressed as: Where x represents the coordinates of the measuring point, A represents the preset water-passing cross-sectional area of ​​the measuring point, g represents the gravitational acceleration, Q loss represents the preset water loss value, h represents the water level, δ,∈ represents the preset adjustment coefficient, S f represents the friction slope, represents the first-order derivative of runoff with respect to time, Indicates (Q 2 / A) the first derivative of the coordinates of the measuring point, represents the first derivative of the water level with respect to the coordinates of the measuring point, represents the second-order derivative of the runoff with respect to the coordinates of the measuring point, Represents the first-order derivative of the preset water-passing cross-sectional area at the measuring point with respect to time, represents the first derivative of runoff with respect to the coordinates of the measuring point, Represents the second-order derivative of the preset water-passing cross-sectional area of ​​the measuring point with respect to the coordinates of the measuring point; The flood evolution model is gridded and discretized by the finite difference method, and the flow Q(x, t) and water level h(x, t) at the measuring point at time t are obtained through iterative solution.

6. A hydrological and meteorological integrated monitoring system for water disaster early warning, characterized in that: include: Data acquisition module: collects the hydrological and meteorological comprehensive data of the basin to be measured, the hydrological and meteorological comprehensive data includes rainfall, soil moisture, temperature, wind speed and coordinates of the measuring points, and pre-processes the hydrological and meteorological comprehensive data, the pre-processing includes denoising and smoothing; Hydrological model building module: constructing a hydrological model of the basin to be measured based on the pre-processed hydrological and meteorological comprehensive data, and obtaining the runoff of the basin to be measured through the hydrological model; Meteorological model building module: building a meteorological model of the basin to be measured based on the pre-processed hydrological and meteorological comprehensive data, and obtaining the evaporation of the basin to be measured through the meteorological model; Flood evolution model construction module: construct a flood evolution model according to the runoff, evaporation and coordinates of the measuring point, and solve the flood evolution model by finite difference method to obtain the flow and water level of the measuring point; Disaster warning module: issues water disaster warnings for the measurement point based on the flow and water level.

7. The hydrological and meteorological integrated monitoring system for water disaster early warning according to claim 6 is characterized in that: The data acquisition module preprocessing method is: P ct (t)=P raw (t)+α·ΔP(t)+β·Smooth(P raw (t)); Among them, P ct (t) represents the comprehensive hydrological and meteorological data at time t after preprocessing, P raw (t) represents the comprehensive hydrological and meteorological data at time t before preprocessing, α represents the preset noise correction coefficient, ΔP(t) represents the change of the comprehensive hydrological and meteorological data, β represents the preset smoothing coefficient, Smooth(P raw (t)) represents the smoothed P raw (t), t represents time; The calculation method of the change ΔP(t) is: ΔP(t)=P raw (t)-P raw (t-Δt); Wherein, Δt represents a preset time variable.

8. The hydrological and meteorological integrated monitoring system for water disaster early warning according to claim 7 is characterized in that: The hydrological model is expressed as: I a =0.2ln(S+1); Where Q represents the runoff, I a represents the loss value of rainfall, P represents rainfall, S represents soil water absorption, CN represents the preset soil curve number, ε represents the preset soil influence weight, SM represents soil moisture, γ represents the preset temperature influence weight, T represents temperature, θ represents the preset wind speed influence weight, and W represents wind speed.

9. The hydrological and meteorological integrated monitoring system for water disaster early warning according to claim 7 is characterized in that: The meteorological model is expressed as: R n =R ns +R nl ; And a =and s ×RH; Among them, E T represents the evaporation rate, p a represents the air density, c p is the specific heat capacity of air, e s Indicates the preset saturated water vapor pressure, e a represents the actual water vapor pressure, φ represents the slope of the saturated water vapor pressure curve, R n represents the net radiation, G soil heat flux, r a represents the resistance of air to water evaporation, τ represents the preset latent heat of vaporization of water, r s represents the preset vegetation resistance to water evaporation, σ represents the preset time first-order derivative weight, represents the first-order derivative of temperature with respect to time, μ represents the preset slope factor, and R ns represents the shortwave net radiation, R nl represents the long-wave net radiation, p s represents soil density, c s is the specific heat capacity of soil, represents the time rate of change of soil temperature, RH represents the preset relative humidity, ks represents the preset Karman constant, z represents the preset height, and d represents the preset zero plane displacement.

10. The hydrological and meteorological integrated monitoring system for water disaster early warning according to claim 8 or 9, characterized in that: The flood evolution model is expressed as: Where x represents the coordinates of the measuring point, A represents the preset water-passing cross-sectional area of ​​the measuring point, g represents the gravitational acceleration, Q loss represents the preset water loss value, h represents the water level, δ,∈ represents the preset adjustment coefficient, S f represents the friction slope, represents the first-order derivative of runoff with respect to time, Indicates (Q 2 / A) the first derivative of the coordinates of the measuring point, represents the first derivative of the water level with respect to the coordinates of the measuring point, represents the second-order derivative of the runoff with respect to the coordinates of the measuring point, Represents the first-order derivative of the preset water-passing cross-sectional area at the measuring point with respect to time, represents the first derivative of runoff with respect to the coordinates of the measuring point, Represents the second-order derivative of the preset water-passing cross-sectional area of ​​the measuring point with respect to the coordinates of the measuring point; The flood evolution model is gridded and discretized by the finite difference method, and the flow Q(x, t) and water level h(x, t) at the measuring point at time t are obtained through iterative solution.

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