Drainage basin hydrologic monitoring method and system based on digital twinning

Abstract

The invention relates to a digital twinning-based watershed hydrological monitoring method and system, and the method comprises the steps: obtaining the coordinates and elevation values of a watershed to be detected through an optical detection and ranging (LiDAR) technology, and constructing a digital elevation model (DEM) through an interpolation algorithm; and in combination with rainfall data of the meteorological station, calculating the rainfall of each acquisition point by using the DEM, constructing a hydrological model based on the rainfall and the DEM, and outputting the runoff and the soil water content of the acquisition points. The rainfall, runoff and soil water content data are further fused, a digital twinborn model of an acquisition point is constructed, and the hydrological state is monitored in real time. According to the model, real-time data processing and analysis are carried out by using an advanced algorithm (such as Kalman filtering), hydrological state information is rapidly and accurately output, the data processing efficiency and accuracy are improved, and support is provided for hydrological prediction and decision making. Through the digital twinborn model, complete virtual mapping of the watershed hydrological state is realized, and a powerful tool is provided for real-time monitoring and management.

Description

Technical Field

[0001] The present invention relates to a watershed hydrological monitoring method and system based on digital twins, belonging to the technical field of water conservancy monitoring. Background Art

[0002] With the development of society and the increasing prominence of environmental problems, the importance of river basin hydrological monitoring in water resources management, flood prevention and disaster reduction, and ecological protection has become increasingly prominent. Traditional river basin hydrological monitoring mainly relies on field measurements and simple data collection. By setting up monitoring facilities such as hydrological stations and rain gauges in the river basin, regular or real-time observations of hydrological elements such as water level, flow, and rainfall are carried out. However, these traditional methods have many limitations and are difficult to meet the high-precision, high-efficiency, and comprehensive requirements of modern hydrological monitoring. The distribution of traditional monitoring stations is often limited, and it is impossible to conduct all-round and high-density monitoring of the entire river basin. It is difficult to deploy monitoring equipment in many remote areas or areas with complex terrain, resulting in blank areas in the data and failing to accurately reflect the overall hydrological conditions of the river basin.

[0003] The patent document with the patent number "CN112785024A" has developed a runoff calculation and prediction method based on a watershed hydrological model. The problem with this method is that although basic data such as DEM elevation data are also used to construct a watershed hydrological model, there is no mention of using advanced surveying and mapping technologies such as LiDAR to obtain more accurate terrain data. In areas with complex terrain, the accuracy of the constructed watershed model may be insufficient, which in turn affects the monitoring accuracy of the hydrological status. It mainly focuses on hydrological simulation and prediction based on existing watershed hydrological models and input localized model data. There is no clear mention of the ability to obtain and update real-time dynamically changing hydrological data. It may not be possible to monitor and warn in a timely and accurate manner when responding to sudden hydrological events or rapid changes in hydrological status. Summary of the invention

[0004] In order to solve the above-mentioned problems existing in the prior art, the present invention proposes a watershed hydrological monitoring method and system based on digital twins.

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

[0006] On the one hand, the present invention provides a watershed hydrological monitoring method based on digital twins, comprising the following steps:

[0007] For the watershed to be measured, the light detection and ranging technology LiDAR is used to obtain the coordinates and elevation values ​​of the collection points, and a digital elevation model is constructed through the interpolation algorithm and the coordinates and elevation values ​​of the collection points;

[0008] Acquire meteorological data of a meteorological station in the watershed to be measured, and calculate the rainfall at the collection point according to the digital elevation model, wherein the meteorological data includes rainfall;

[0009] A hydrological model is constructed according to the rainfall and the digital elevation model, and the runoff and soil moisture content of the collection point are output through the hydrological model;

[0010] A digital twin model of the collection point is constructed according to the rainfall, runoff and soil moisture content, and the hydrological status of the collection point is monitored by the digital twin model.

[0011] As a preferred implementation, the digital elevation model is expressed as:

[0012]

[0013] Among them, h i represents the elevation value of the i-th collection point, σ represents the standard deviation of the preset Gaussian kernel, DEM(x,y) represents the elevation value of any collection point with coordinates (x,y) in the digital elevation model, N represents the preset maximum number, and w i represents the weight coefficient of the i-th collection point, x i Indicates the horizontal coordinate of the i-th acquisition point, y i Indicates the ordinate of the i-th acquisition point, where i represents the bit index;

[0014] The method to obtain the weight coefficient is:

[0015]

[0016] Among them, ∈ represents a preset constant, d i represents the relative density of the ith collection point, It represents the square of the average distance between the i-th collection point and the remaining collection points.

[0017] As a preferred implementation, the calculation method of the rainfall is:

[0018]

[0019] Among them, R(x,y) represents the rainfall at any coordinate (x,y) collection point, M represents the number of preset weather stations, and R j represents the rainfall at the jth weather station, DEM(x j ,y j ) represents the plane coordinates of the jth meteorological station in the digital elevation model (x j ,y j ), λ j Represents the preset weight coefficient of the jth weather station.

[0020] As a preferred implementation, the hydrological model is expressed as:

[0021]

[0022] S(x,y,t)=S(x,y,t-1)+R(x,y)-E(x,y,t)-Q(x,y,t);

[0023] Among them, Q(x,y,t) represents the runoff at the sampling point at any coordinate (x,y) at time t, and C surf represents the preset surface runoff coefficient, A(x,y) represents the influence area of ​​the collection point with arbitrary coordinates (x,y), γ represents the preset terrain influence weight coefficient, max(DEM) represents the maximum elevation value in the digital elevation model, S(x,y,t) represents the soil moisture content of the collection point with arbitrary coordinates (x,y) at time t, and E(x,y,t) represents the evaporation of the collection point with arbitrary coordinates (x,y) at time t.

[0024] As a preferred implementation, the digital twin model is expressed as:

[0025]

[0026] K t =P t-1 ·H T ·(H·P t-1 ·H T +R obs ) -1 ;

[0027]

[0028] in, represents the state vector at time t, represents the state transfer function, K t represents the Kalman gain at time t, z t represents the observed values ​​of R(x,y), Q(x,y,t), and S(x,y,t), represents the observation function, P t represents the covariance matrix of the state estimate at time t, H T represents the transpose of the observation matrix, R obs represents the covariance matrix of the observation noise, I represents the identity matrix, K t The transpose of .

[0029] On the other hand, the present invention also provides a watershed hydrological monitoring system based on digital twins, comprising:

[0030] Digital elevation model module: Use LiDAR technology to obtain the coordinates and elevation values ​​of the collection points for the watershed to be measured, and construct a digital elevation model through the interpolation algorithm and the coordinates and elevation values ​​of the collection points;

[0031] Meteorological module: obtaining meteorological data of the meteorological station in the watershed to be measured, and calculating the rainfall at the collection point according to the digital elevation model, wherein the meteorological data includes rainfall;

[0032] Hydrological model module: construct a hydrological model according to the rainfall and the digital elevation model, and output the runoff and soil moisture content of the collection point through the hydrological model;

[0033] Digital twin model module: construct a digital twin model of the collection point according to the rainfall, runoff and soil moisture content, and monitor the hydrological status of the collection point through the digital twin model.

[0034] The present invention has the following beneficial effects:

[0035] The present invention uses light detection and ranging technology LiDAR to obtain the coordinates and elevation values ​​of the collection points, and constructs a digital elevation model through the interpolation algorithm and the coordinates and elevation values ​​of the collection points, so as to obtain the terrain information of the basin more accurately. The hydrological model constructed based on this can more accurately calculate the runoff and soil moisture content of the collection points, thereby realizing high-precision monitoring of the hydrological state of the basin, making up for the deficiencies of traditional monitoring methods in data accuracy and coverage. By constructing a digital twin model, multi-dimensional data such as rainfall, runoff and soil moisture are organically integrated to form a complete virtual mapping of the hydrological state of the basin. The digital twin model can use advanced algorithms (such as Kalman filtering, etc.) to process and analyze data in real time, quickly and accurately output the hydrological state information of the collection points, improve the efficiency and accuracy of data processing, and provide strong support for timely and accurate hydrological prediction and decision-making. The basin hydrological monitoring method based on the digital twin model can obtain and update the hydrological data of the basin in real time, and predict the hydrological change trend of the basin in real time through dynamic simulation and analysis of the model. For example, when rainfall changes, the digital twin model can quickly adjust the prediction results, providing more scientific and timely decision-making basis for the rational allocation of water resources, flood warning and flood control scheduling, effectively improving the prediction accuracy and decision-making scientificity of river basin hydrological monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 The present invention is a flowchart for implementing the method. DETAILED DESCRIPTION

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0042] Embodiment 1:

[0043] See also Figure 1 The present invention provides a watershed hydrological monitoring method based on digital twins, comprising the following steps:

[0044] For the watershed to be measured, the light detection and ranging technology LiDAR is used to obtain the coordinates and elevation values ​​of the collection points, and a digital elevation model is constructed through the interpolation algorithm and the coordinates and elevation values ​​of the collection points;

[0045] Acquire meteorological data of a meteorological station in the watershed to be measured, and calculate the rainfall at the collection point according to the digital elevation model, wherein the meteorological data includes rainfall;

[0046] A hydrological model is constructed according to the rainfall and the digital elevation model, and the runoff and soil moisture content of the collection point are output through the hydrological model;

[0047] A digital twin model of the collection point is constructed according to the rainfall, runoff and soil moisture content, and the hydrological status of the collection point is monitored by the digital twin model.

[0048] Convert discrete elevation values ​​to a continuous digital elevation model:

[0049] As a preferred implementation, the digital elevation model is expressed as:

[0050]

[0051] Among them, h irepresents the elevation value of the i-th collection point, σ represents the standard deviation of the preset Gaussian kernel, which is used to control the smoothness of the interpolation, DEM(x,y) represents the elevation value of any collection point with coordinates (x,y) in the digital elevation model, N represents the preset maximum number (preset according to the number of elevation values), w i represents the weight coefficient of the i-th collection point, x i Indicates the horizontal coordinate of the i-th acquisition point, y i Indicates the ordinate of the i-th acquisition point, where i represents the bit index;

[0052] The method to obtain the weight coefficient is:

[0053]

[0054] Among them, ∈ represents a preset constant to prevent the denominator from being 0, d i Represents the relative density of the i-th collection point (similarly d j represents the relative density of the jth collection point), It represents the square of the average distance between the i-th collection point and the remaining collection points.

[0055] As a preferred implementation, the calculation method of the rainfall is:

[0056]

[0057] Among them, R(x,y) represents the rainfall at any coordinate (x,y) collection point, M represents the number of preset weather stations, and R j represents the rainfall at the jth weather station, DEM(x j ,y j ) represents the plane coordinates of the jth meteorological station in the digital elevation model (x j ,y j ), λ j Represents the preset weight coefficient of the jth weather station.

[0058] As a preferred implementation, the hydrological model is expressed as:

[0059]

[0060] S(x,y,t)=S(x,y,t-1)+R(x,y)-E(x,y,t)-Q(x,y,t);

[0061] Among them, Q(x,y,t) represents the runoff at the sampling point at any coordinate (x,y) at time t, and C surfrepresents the preset surface runoff coefficient, which is determined based on the land use type and soil characteristics of the watershed. A(x, t) represents the influence area of ​​the collection point with arbitrary coordinates (x, y), which is determined by the digital elevation model. γ represents the preset terrain influence weight coefficient. max(DEM) represents the maximum elevation value in the digital elevation model. S(x, y, t) represents the soil moisture content of the collection point with arbitrary coordinates (x, y) at time t. E(x, y, t) represents the evaporation at the collection point with arbitrary coordinates (x, y) at time t. It can be obtained by any conventional simulation method and is not limited here.

[0062] As a preferred implementation, the digital twin model is expressed as:

[0063]

[0064] K t =P t-1 ·H T ·(H·P t-1 ·H T +R obs ) -1 ;

[0065]

[0066] in, represents the state vector at time t, containing the estimated values ​​of runoff and evaporation, represents the state transfer function, which is used to represent the evolution of the state over time, K t represents the Kalman gain at time t, z t Represents the observed values ​​of R(x,y), Q(x,y,t), and S(x,y,t) (such as the actual measured runoff or soil moisture content). The input of the observed values ​​helps the digital twin model to perform real-time data processing and analysis. By comparing with the state predicted by the model, the model can be further corrected and optimized to improve its accuracy and reliability. represents the observation function, P t represents the covariance matrix of the state estimate at time t, H T Represents the transpose of the observation matrix (which describes the relationship between states and observations and is determined by default), R obs represents the covariance matrix of the observation noise (determined by the statistical properties of the observation data), I represents the identity matrix, K t The transpose of .

[0067] Embodiment 2:

[0068] The present invention also provides a watershed hydrological monitoring system based on digital twins, comprising:

[0069] Digital elevation model module: Use LiDAR technology to obtain the coordinates and elevation values ​​of the collection points for the watershed to be measured, and construct a digital elevation model through the interpolation algorithm and the coordinates and elevation values ​​of the collection points;

[0070] Meteorological module: obtaining meteorological data of the meteorological station in the watershed to be measured, and calculating the rainfall at the collection point according to the digital elevation model, wherein the meteorological data includes rainfall;

[0071] Hydrological model module: construct a hydrological model according to the rainfall and the digital elevation model, and output the runoff and soil moisture content of the collection point through the hydrological model;

[0072] Digital twin model module: construct a digital twin model of the collection point according to the rainfall, runoff and soil moisture content, and monitor the hydrological status of the collection point through the digital twin model.

[0073] The system is used to implement the method in Example 1, which will not be described in detail here.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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 watershed hydrological monitoring method based on digital twins, characterized in that: The following steps are involved: For the watershed to be measured, the light detection and ranging technology LiDAR is used to obtain the coordinates and elevation values ​​of the collection points, and a digital elevation model is constructed through the interpolation algorithm and the coordinates and elevation values ​​of the collection points; Acquire meteorological data of a meteorological station in the watershed to be measured, and calculate the rainfall at the collection point according to the digital elevation model, wherein the meteorological data includes rainfall; A hydrological model is constructed according to the rainfall and the digital elevation model, and the runoff and soil moisture content of the collection point are output through the hydrological model; A digital twin model of the collection point is constructed according to the rainfall, runoff and soil moisture content, and the hydrological status of the collection point is monitored by the digital twin model.

2. The watershed hydrological monitoring method based on digital twin according to claim 1 is characterized in that: The digital elevation model is expressed as: Among them, h i represents the elevation value of the i-th collection point, σ represents the standard deviation of the preset Gaussian kernel, DEM(x,y) represents the elevation value of any collection point with coordinates (x,y) in the digital elevation model, N represents the preset maximum number, and w i represents the weight coefficient of the i-th collection point, x i Indicates the horizontal coordinate of the i-th acquisition point, y i Indicates the ordinate of the i-th acquisition point, where i represents the bit index; The method to obtain the weight coefficient is: Among them, ∈ represents a preset constant, d i represents the relative density of the ith collection point, It represents the square of the average distance between the i-th collection point and the remaining collection points.

3. The watershed hydrological monitoring method based on digital twin according to claim 2 is characterized in that: The calculation method of rainfall is: Where R(x, y) represents the rainfall at any collection point with coordinates (x, y), M represents the number of preset weather stations, and R j represents the rainfall at the jth weather station, DEM(x j ,y j ) represents the plane coordinates of the jth meteorological station in the digital elevation model (x j ,y j ), λ j Represents the preset weight coefficient of the jth weather station.

4. The watershed hydrological monitoring method based on digital twin according to claim 3 is characterized in that: The hydrological model is expressed as: S(x,y,t)=S(x,y,t-1)+R(x,y)-E(x,y,t)-Q(x,y,t); Among them, Q(x, y, t) represents the runoff at the sampling point at any coordinate (x, y) at time t, and C Surf represents the preset surface runoff coefficient, A(x, y) represents the influence area of ​​the collection point with arbitrary coordinates (x, y), γ represents the preset terrain influence weight coefficient, max(DEM) represents the maximum elevation value in the digital elevation model, S(x, y, t) represents the soil moisture content of the collection point with arbitrary coordinates (x, y) at time t, and E(x, y, t) represents the evaporation of the collection point with arbitrary coordinates (x, y) at time t.

5. The watershed hydrological monitoring method based on digital twin according to claim 4 is characterized in that: The digital twin model is expressed as: K t =P t-1 ·H T ·(H·P t-1 ·H T +R obs ) -1 ; in, represents the state vector at time t, represents the state transfer function, K t represents the Kalman gain at time t, z t represents the observed values ​​of R(x,y), Q(x,y,t), and S(x,y,t), represents the observation function, P t represents the covariance matrix of the state estimate at time t, H T represents the transpose of the observation matrix, R obs represents the covariance matrix of the observation noise, I represents the identity matrix, K t The transpose of .

6. A watershed hydrological monitoring system based on digital twins, characterized in that: include: Digital elevation model module: Use LiDAR technology to obtain the coordinates and elevation values ​​of the collection points for the watershed to be measured, and construct a digital elevation model through the interpolation algorithm and the coordinates and elevation values ​​of the collection points; Meteorological module: obtaining meteorological data of the meteorological station in the watershed to be measured, and calculating the rainfall at the collection point according to the digital elevation model, wherein the meteorological data includes rainfall; Hydrological model module: construct a hydrological model according to the rainfall and the digital elevation model, and output the runoff and soil moisture content of the collection point through the hydrological model; Digital twin model module: construct a digital twin model of the collection point according to the rainfall, runoff and soil moisture content, and monitor the hydrological status of the collection point through the digital twin model.

7. The watershed hydrological monitoring system based on digital twin according to claim 6 is characterized in that: The digital elevation model is expressed as: Among them, h i represents the elevation value of the i-th collection point, σ represents the standard deviation of the preset Gaussian kernel, DEM(x,y) represents the elevation value of any collection point with coordinates (x,y) in the digital elevation model, N represents the preset maximum number, and w i represents the weight coefficient of the i-th collection point, x i Indicates the horizontal coordinate of the i-th acquisition point, y i Indicates the ordinate of the i-th acquisition point, where i represents the bit index; The method to obtain the weight coefficient is: Among them, ∈ represents a preset constant, d i represents the relative density of the ith collection point, It represents the square of the average distance between the i-th collection point and the remaining collection points.

8. The watershed hydrological monitoring system based on digital twin according to claim 7 is characterized in that: The meteorological module calculates rainfall as follows: Where R(x, y) represents the rainfall at any collection point with coordinates (x, y), M represents the number of preset weather stations, and R j represents the rainfall at the jth weather station, DEM(x j ,y j ) represents the plane coordinates of the jth meteorological station in the digital elevation model (x j ,y j ), λ j Represents the preset weight coefficient of the jth weather station.

9. The watershed hydrological monitoring system based on digital twin according to claim 8 is characterized in that: The hydrological model is expressed as: S(x,y,t)=S(x,y,t-1)+R(x,y)-E(x,y,t)-Q(x,y,t); Among them, Q(x, y, t) represents the runoff at the sampling point at any coordinate (x, y) at time t, and C Surf represents the preset surface runoff coefficient, A(x, y) represents the influence area of ​​the collection point with arbitrary coordinates (x, y), γ represents the preset terrain influence weight coefficient, max(DEM) represents the maximum elevation value in the digital elevation model, S(x, y, t) represents the soil moisture content of the collection point with arbitrary coordinates (x, y) at time t, and E(x, y, t) represents the evaporation of the collection point with arbitrary coordinates (x, y) at time t.

10. The watershed hydrological monitoring system based on digital twin according to claim 9 is characterized in that: The digital twin model is expressed as: K t =P t-1 ·H T ·(H·R t-1 ·H T +R obs ) -1 ; in, represents the state vector at time t, represents the state transfer function, K t represents the Kalman gain at time t, z t represents the observed values ​​of R(x,y), Q(x,y,t), and S(x,y,t), represents the observation function, P t represents the covariance matrix of the state estimate at time t, H T represents the transpose of the observation matrix, R obs represents the covariance matrix of the observation noise, I represents the identity matrix, K t The transpose of .

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