A numerical simulation method for estuary tidal propagation and related device

Abstract

The embodiment of the application provides a numerical simulation method for estuary tide propagation and related equipment, and belongs to the technical field of hydrodynamic numerical simulation. The method comprises the following steps: obtaining relevant data of a target river section, discretizing the river section, determining prediction constraint conditions of each micro-section after discretization based on an improved continuity equation and a motion equation, finally performing numerical simulation on each micro-section, and obtaining an estuary tide propagation prediction result of the target river section; the embodiment of the method introduces first and second constraint factors based on a one-dimensional Saint-Venant equation, considers nonlinear influences generated by bidirectional water exchange of estuary tides and runoff, and considers influences of additional tidal wave energy loss generated in the estuary, so that an improved hydrodynamic control equation set capable of considering nonlinear coupling of runoff and tides and adapting to complex estuary variable working conditions is constructed, and the precision of the prediction of the estuary tide propagation is effectively improved.

Description

Technical Field

[0001] This application relates to the field of hydrodynamic numerical simulation technology, and in particular to a numerical simulation method and related equipment for estuarine tidal propagation. Background Technology

[0002] The estuary area is a complex waterway where sea tides and river runoff meet. As the tides travel from the open sea to the upper reaches of the river, they are affected by multiple factors such as narrowing of the river channel topography, riverbed friction, backwater from upstream runoff, and deformation of shallow water waves. This results in phenomena such as tidal range attenuation, phase lag, asymmetry of rising and falling tides, and tidal wave distortion. It is a core scenario for hydrological simulation, waterway regulation, flood control and drainage, and tidal flat development.

[0003] Existing methods for predicting estuarine tidal propagation generally rely on a single Saint-Venant equation for linear simplification. This approach suffers from large prediction errors and low accuracy when dealing with complex or imperfect river conditions.

[0004] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0005] The main objective of this application is to propose a numerical simulation method and related equipment for estuarine tidal propagation. The provided numerical simulation method introduces constraint factors into the traditional one-dimensional Saint-Venant equations to construct an improved set of hydrodynamic control equations that can consider the nonlinear coupling effect of runoff and tides and adapt to complex estuarine variable conditions, thereby effectively improving the prediction accuracy of estuarine tidal propagation.

[0006] To achieve the above objectives, one aspect of this application proposes a numerical simulation method for estuarine tidal propagation, the method comprising: The target river section is obtained by acquiring the river topographic geometric parameters, riverbed resistance characteristic parameters, river section operating data and boundary hydrological data. Based on the preset discretization strategy and the river section operating data of the target river section, the target river section is discretized into several micro-segments and the spatiotemporal boundary corresponding to each micro-segment is determined. Based on the improved continuity equation, the spatiotemporal boundary corresponding to each micro-segment, and the channel geometry parameters of the target river segment, the first prediction constraint condition of each micro-segment within the corresponding spatiotemporal boundary is determined; the improved continuity equation introduces a first constraint factor based on the one-dimensional Saint-Venant continuity equation; the first constraint factor considers the nonlinear effects caused by the exchange of estuarine tides and runoff. Based on the improved equation of motion, the spatiotemporal boundary corresponding to each micro-segment, and the channel topographic geometric parameters, channel resistance characteristic parameters, and boundary hydrological data of the target river segment, the second predictive constraint condition for each micro-segment within the corresponding spatiotemporal boundary is determined; the improved equation of motion introduces a second constraint factor based on the one-dimensional Saint-Venant equation of motion; the second constraint factor considers the influence of additional tidal wave energy loss generated in the estuary. Numerical simulations are performed based on the river topographic geometric parameters, riverbed resistance characteristic parameters, and boundary hydrological data of the target river section, as well as the first and second prediction constraints of each micro-segment within the corresponding spatiotemporal boundary. The estuary tidal propagation prediction results of the target river section are obtained based on the numerical simulation results.

[0007] In some embodiments, the step of discretizing the target river segment into several micro-segments based on a preset discretization strategy and the river segment condition data of the target river segment includes: Based on the river section condition data of the target river section, the segment nodes of the target river section are determined to determine the segment control conditions of the target river section; the segment nodes are one or more of the following: abrupt change points in the river cross section, inflection points in the riverbed slope, boundary points of changes in riverbed roughness, runoff inflow points, or runoff outflow points of the target river section. Based on the river section condition data of the target river section, the river section state at different locations is determined to determine the segment scale constraints of the target river section at different locations; the river section state is determined by the changes in riverbed slope, the changes in river cross-sectional shape along the course, the changes in local geometric parameters, and the local narrowing of river width. Based on the segmented control conditions of the target river segment and the segmented scale constraints at different locations, the target river segment is discretized into several micro-segments.

[0008] In some embodiments, the spatiotemporal boundary corresponding to each micro-segment is determined in the following way: Based on the preset spatial step size and the discrete results of the target river segment, the spatial boundary corresponding to each micro-segment is determined; The time step and the time boundary corresponding to each micro-segment are determined based on a preset time step.

[0009] In some embodiments, the first constraint factor is characterized by the net runoff inflow per unit length of the river segment; the step of determining the first predictive constraint condition for each micro-segment within the corresponding spatiotemporal boundary based on the improved continuity equation, the spatiotemporal boundary corresponding to each micro-segment, and the river geometry parameters of the target river segment includes: The instantaneous cross-sectional area of ​​the river channel within the corresponding spatiotemporal boundary is obtained by calculating the river topographic geometric parameters of the micro-segment within the corresponding spatiotemporal boundary. Obtain the composite instantaneous flow rate of the micro-segment after the coupling of tidal and runoff flows within the corresponding spatiotemporal boundary; The distance from the starting point of the target river section to each time point within the spatiotemporal boundary is determined based on the spatiotemporal boundary corresponding to the micro-segment; The net runoff inflow per unit length of the river within the corresponding spatiotemporal boundary is calculated based on the improved continuity equation, the instantaneous cross-sectional area of ​​the river within the corresponding spatiotemporal boundary of the micro-segment, the combined instantaneous flow rate after the coupling of tide and runoff, and the distance from the starting point of the target river segment at each time point. The first predictive constraint condition for the micro-segment within the corresponding spatiotemporal boundary is determined based on the improved continuity equation and the net runoff inflow per unit length of the river channel within the corresponding spatiotemporal boundary.

[0010] In some embodiments, the second prediction constraint condition of the micro-segment is determined based on the improved equation of motion and the second constraint factor of the micro-segment within the corresponding spatiotemporal boundary; the second constraint factor is affected by an adaptive correction coefficient; the adaptive correction coefficient of the micro-segment within the corresponding spatiotemporal boundary is confirmed in the following manner: Based on the analysis of the riverbed resistance characteristic parameters of the micro-segment within the corresponding spatiotemporal boundary, the cross-sectional cross-bundle situation, riverbed slope situation and topographic undulation situation of the micro-segment within the corresponding spatiotemporal boundary are determined according to the analysis results. The degree of tidal energy loss of the micro-segment within the corresponding spatiotemporal boundary is determined based on the cross-sectional bundle crossing situation, riverbed slope, and topographic undulation within the corresponding spatiotemporal boundary. The adaptive correction coefficient of the micro-segment within the corresponding spatiotemporal boundary is determined based on the judgment result and the preset value logic.

[0011] In some embodiments, the numerical simulation process is implemented through the following steps: An initial field value is assigned to each micro-segment based on the river channel geometry parameters and riverbed resistance characteristic parameters of the target river section. Based on the boundary hydrological data of the target river section, determine the temporal boundary conditions of each micro-segment within the corresponding spatiotemporal boundary; According to the time sequence, numerical simulation is performed on each micro-segment based on the initial field assignment result of each micro-segment and the first prediction constraint, the second prediction constraint and the temporal boundary condition within the corresponding spatiotemporal boundary of each micro-segment. The numerical simulation result of the target river segment is obtained based on the numerical simulation result of each micro-segment.

[0012] In some embodiments, the numerical simulation results for each micro-segment include tidal level data and flow rate data within the spatiotemporal boundary corresponding to the micro-segment; the method further includes: Determine whether the tidal level difference and flow rate difference within the spatiotemporal boundary corresponding to adjacent micro-segments among the plurality of micro-segments all meet the preset convergence threshold condition. If not, re-perform numerical simulation for each micro-segment until the tidal level difference and flow rate difference within the spatiotemporal boundary corresponding to adjacent micro-segments among the plurality of micro-segments all meet the preset convergence threshold condition or reach the preset number of numerical simulations.

[0013] To achieve the above objectives, another aspect of this application proposes a numerical simulation system for estuarine tidal propagation, the system comprising: The data acquisition module is used to acquire the river channel topographic geometric parameters, riverbed resistance characteristic parameters, river section operating condition data and boundary hydrological data of the target river section. Based on the preset discretization strategy and the river section operating condition data of the target river section, the target river section is discretized into several micro-segments and the spatiotemporal boundary corresponding to each micro-segment is determined. The continuity constraint determination module is used to determine the first predicted constraint conditions of each micro-segment within the corresponding spatiotemporal boundary based on the improved continuity equation, the spatiotemporal boundary corresponding to each micro-segment, and the channel geometry parameters of the target river segment; the improved continuity equation introduces a first constraint factor based on the one-dimensional Saint-Venant continuity equation; the first constraint factor considers the nonlinear effects caused by the exchange of tidal and runoff water bodies at the estuary. The motion constraint determination module is used to determine the second predictive constraint conditions of each micro-segment within the corresponding spatiotemporal boundary based on the improved motion equation, the spatiotemporal boundary corresponding to each micro-segment, and the channel topographic geometric parameters, channel resistance characteristic parameters, and boundary hydrological data of the target river section; the improved motion equation introduces a second constraint factor based on the one-dimensional Saint-Venant motion equation; the second constraint factor considers the influence of additional tidal wave energy loss generated in the estuary. The numerical simulation module is used to perform numerical simulation based on the river topographic geometric parameters, riverbed resistance characteristic parameters, and boundary hydrological data of the target river section, as well as the first and second prediction constraints of each micro-segment within the corresponding spatiotemporal boundary, and to obtain the estuary tidal propagation prediction result of the target river section based on the numerical simulation results.

[0014] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0015] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0016] The embodiments of this application include at least the following beneficial effects: This application provides a numerical simulation method, system, electronic device, and storage medium for estuarine tidal propagation. This scheme acquires the river channel topographic geometric parameters, riverbed resistance characteristic parameters, river section operating condition data, and boundary hydrological data of the target river section. Then, based on the acquired relevant data and improved continuity and motion equations, numerical simulation is performed to obtain the estuarine tidal propagation prediction results for the target river section. On one hand, this method introduces a first constraint factor based on the one-dimensional Saint-Venant continuity equation. This constraint factor considers the nonlinear effects of the bidirectional water exchange between estuarine tides and runoff. Introducing this factor allows for consideration of the nonlinear effects of runoff inflow along the river channel, floodplain water exchange effects, and the dynamic water replenishment and exchange process resulting from the continuous downstream discharge of upstream runoff and the formation of tidal water against upstream currents during the numerical simulation, thereby improving the accuracy of the estuarine tidal propagation prediction for the target river section. On the one hand, this method improves the accuracy of tidal prediction; on the other hand, it introduces a second constraint factor into the one-dimensional Saint-Venant equations of motion. This factor is used to characterize the influence of additional tidal energy loss generated in the estuary. Introducing this factor can further consider the influence of additional tidal energy loss generated in the estuary while retaining the convection term, water surface gradient term, and riverbed friction term in the traditional momentum equations. This allows for the construction of a momentum conservation equation adapted to the nonlinear propagation of estuary tides, improving the fitting degree of numerical simulation to tidal deformation in complex terrain. In addition, the method in this application is based on the one-dimensional Saint-Venant equations for numerical simulation. It only introduces a constraint factor to improve the traditional equations. Compared with numerical simulation methods using two-dimensional or higher numerical models, the prediction process of this method is simpler. By constructing an improved set of hydrodynamic control equations that can consider the nonlinear coupling effect of runoff and tides and adapt to complex estuarine conditions, the accuracy of predicting tidal propagation in the estuary is effectively improved. Attached Figure Description

[0017] Figure 1 This is a flowchart of a numerical simulation method for estuarine tidal propagation provided in an embodiment of this application; Figure 2 This is a schematic diagram of the adaptive non-uniform discrete micro-segmentation of the estuary section provided in the embodiments of this application; Figure 3 This is a structural block diagram of a numerical simulation system for estuarine tidal propagation provided in an embodiment of this application; Figure 4 This is a structural block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0019] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0020] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0022] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0023] 1) Estuarine tidal propagation: refers to the dynamic process in which ocean tidal waves, after entering the confined waters of an estuary, advance upstream under the influence of multiple factors such as runoff backing, bed friction, and changes in cross-sectional morphology, and undergo significant nonlinear deformations such as tidal range adjustment along the course, waveform steepening, and phase lag.

[0024] 2) Saint-Venant equations: a set of one-dimensional hydrodynamic partial differential equations describing unsteady, gradually varying flow in open channels. It consists of continuity equations with mass conservation and motion equations with momentum conservation, and is used to simulate the dynamic evolution of water level (depth) and flow rate in a river over time and space.

[0025] 3) Shallow water wave theory: This is a theory of water wave motion in hydrodynamics that studies the situation where the depth of water is less than the wavelength (i.e., long waves). Its core is based on the assumption of hydrostatic pressure distribution (ignoring vertical acceleration), which simplifies three-dimensional flow into vertically uniform two-dimensional or one-dimensional flow. The Saint-Venant equation is the specific mathematical expression of this theory in one-dimensional unsteady flow in open channels.

[0026] 4) One-dimensional Saint-Venant equations: A set of partial differential equations describing the spatiotemporal evolution of flow rate and water level along a single flow direction (longitudinal) in unsteady gradually varying flow in open channels. It consists of a continuity equation with mass conservation and a motion equation with momentum conservation. Its core assumption is that the flow velocity is uniformly distributed across the cross-section and the vertical acceleration is negligible (approximately hydrostatic pressure).

[0027] 5) Numerical simulation: A method based on mathematical physics governing equations (such as the Saint-Venant equations) and computer technology. By discretizing the continuous time and space domain into finite grids or cells and iteratively solving approximate algebraic equations, the spatiotemporal evolution of actual physical processes (such as hydrodynamics and tidal propagation) can be dynamically reproduced and predicted in a virtual environment.

[0028] The estuary area is a complex waterway where sea tides and river runoff meet. As the tides travel from the open sea to the upper reaches of the river, they are affected by multiple factors such as narrowing of the river channel topography, riverbed friction, backwater from upstream runoff, and deformation of shallow water waves. This results in phenomena such as tidal range attenuation, phase lag, asymmetry of rising and falling tides, and tidal wave distortion. It is a core scenario for hydrological simulation, waterway regulation, flood control and drainage, and tidal flat development.

[0029] Existing methods for predicting estuarine tidal propagation can be mainly divided into two categories. One category is the traditional analytical simplification algorithm, which performs linear simplification prediction based on a single Saint-Venant equation. However, it ignores the nonlinear coupling effect between runoff and tides and has poor adaptability to frictional resistance and abrupt changes in cross-section. It is only suitable for ideal channels with uniform topography and stable runoff. Under complex estuarine conditions, the prediction results for estuarine tidal propagation have large errors and low accuracy. The other category is the general numerical simulation method, which mostly uses two-dimensional and three-dimensional global numerical models. Although the accuracy is high, the grid division is complex, the number of model parameters is large, the calculation time is long, and the hardware computing power requirements are high. It cannot meet the needs of rapid forecasting in engineering and rapid extrapolation of tidal propagation in long-sequence river sections.

[0030] Existing technologies generally suffer from the following drawbacks: (1) The coupling effects of runoff backwater, riverbed slope change and frictional resistance are not fully integrated, and the nonlinear deformation characteristics of tidal propagation cannot be accurately characterized; (2) Numerical models have low prediction efficiency, and analytical models have insufficient accuracy, making it difficult to balance high accuracy and high efficiency. (3) There is a lack of targeted correction mechanisms for tidal wave attenuation and phase delay in estuaries, resulting in poor fitting of the tidal propagation law in the upstream and downstream of the tidal river section and weak engineering applicability.

[0031] In view of this, this application provides a numerical simulation method, system, electronic device, and storage medium for estuarine tidal propagation. This method acquires the channel topographic geometric parameters, riverbed resistance characteristic parameters, river section operating data, and boundary hydrological data of the target river section. Then, based on the acquired data and improved continuity and motion equations, numerical simulation is performed to obtain the predicted estuarine tidal propagation results for the target river section. On one hand, this method introduces a first constraint factor based on the one-dimensional Saint-Venant continuity equation. This constraint factor considers the nonlinear effects of the bidirectional water exchange between estuarine tides and runoff. Introducing this factor allows for consideration of the nonlinear effects of runoff inflow along the channel, floodplain water exchange effects, and the dynamic water replenishment and exchange process resulting from the continuous downstream discharge of upstream runoff and the mixing of upstream tidal water during the numerical simulation, thereby improving the accuracy of predicting estuarine tidal propagation for the target river section. On the other hand, this method introduces a second constraint factor into the one-dimensional Saint-Venant equations of motion. This factor characterizes the impact of additional tidal energy loss generated in the estuary. Introducing this factor allows for the retention of convection, water surface gradient, and riverbed friction terms in the traditional momentum equations, while further considering the impact of additional tidal energy loss generated in the estuary. This enables the construction of a momentum conservation equation adapted to the nonlinear propagation of estuary tides, improving the fitting degree of numerical simulation to tidal deformation in complex terrain. Furthermore, this method is based on the one-dimensional Saint-Venant equations for numerical simulation, simply introducing a constraint factor to improve the traditional equations. Compared to numerical simulation methods using two-dimensional or higher numerical models, this method has a simpler prediction process. By constructing an improved set of hydrodynamic control equations that can consider the nonlinear coupling effect of runoff and tides and adapt to complex estuarine conditions, the accuracy of predicting estuarine tidal propagation is effectively improved.

[0032] The numerical simulation method for estuarine tidal propagation provided in this application relates to the field of hydrodynamic numerical simulation technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a tablet, laptop, or desktop computer, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the estuarine tidal propagation numerical simulation method, but is not limited to these forms.

[0033] Figure 1 This is an optional flowchart of a numerical simulation method for estuarine tidal propagation provided in an embodiment of this application. Figure 1 The method may include, but is not limited to, steps S101 to S103.

[0034] Step S101: Obtain the river channel topographic geometric parameters, riverbed resistance characteristic parameters, river section operating condition data, and boundary hydrological data of the target river section. Based on the preset discretization strategy and the river section operating condition data of the target river section, discretize the target river section into several micro-segments and determine the spatiotemporal boundary corresponding to each micro-segment.

[0035] Acquire relevant data for the target river segment, discretize the river segment, and determine the corresponding boundaries of the discretized micro-segments as the data basis for subsequent numerical simulations.

[0036] Step S102: Based on the improved continuity equation, the spatiotemporal boundary corresponding to each micro-segment, and the river channel geometry parameters of the target river segment, determine the first prediction constraint condition of each micro-segment within the corresponding spatiotemporal boundary.

[0037] Among them, the improved continuity equation introduces a first constraint factor based on the one-dimensional Saint-Venant continuity equation; the first constraint factor considers the nonlinear effects caused by the exchange of estuarine tides and runoff. Step S103: Based on the improved motion equation, the spatiotemporal boundary corresponding to each micro-segment, the river topographic geometric parameters, river resistance characteristic parameters, and boundary hydrological data of the target river section, determine the second prediction constraint conditions for each micro-segment within the corresponding spatiotemporal boundary. Among them, the improved equation of motion introduces a second constraint factor based on the one-dimensional Saint-Venant equation of motion; the second constraint factor takes into account the influence of additional tidal wave energy loss generated in the estuary.

[0038] Steps S102 to S103 determine the prediction constraints for each micro-segment discretized from the target river section during the numerical simulation process based on real-time acquired data and improved continuity and motion equations.

[0039] Step S104: Based on the river topographic geometric parameters, riverbed resistance characteristic parameters, and boundary hydrological data of the target river section, as well as the first and second prediction constraints of each micro-segment within the corresponding spatiotemporal boundary, numerical simulation is performed, and the estuary tidal propagation prediction results of the target river section are obtained based on the numerical simulation results.

[0040] Based on relevant data and prediction constraints for each micro-segment, numerical simulations are performed, and the prediction results of estuary tidal propagation for the target river segment are obtained from the numerical simulation results.

[0041] The method described above is improved based on the one-dimensional Saint-Venant equation. A first constraint factor is introduced for calculation based on the one-dimensional Saint-Venant continuity equation, and a second constraint factor is introduced for calculation based on the one-dimensional Saint-Venant motion equation. The first constraint factor introduced in this embodiment is the runoff coupling source term, and the second constraint factor is the tidal wave attenuation dissipation term.

[0042] Specifically, the continuity equation of the one-dimensional Saint-Venant equation can be shown as equation (1):

[0043] in, The instantaneous cross-sectional area of ​​the river channel (m²) varies dynamically with the rise and fall of the tides; To calculate the time (s); The combined instantaneous flow rate (m³ / s) after the coupling of tides and runoff. The longitudinal coordinates (m) along the river course.

[0044] Specifically, the improved continuity equation in this embodiment introduces a runoff coupling source term based on the one-dimensional Saint-Venant continuity equation. This term characterizes the bidirectional water exchange between tidal and runoff at the estuary. Introducing this term allows for consideration of the nonlinear effects of runoff inflow along the river channel, floodplain water exchange, and the dynamic water replenishment and exchange process resulting from the continuous downstream runoff from the upstream estuary and the counter-current mixing of upstream tidal water during numerical simulation. The runoff coupling source term can be derived from the net runoff inflow per unit length of the river segment. Characterization; therefore, the improved continuity equation mentioned in this embodiment can be represented as shown in equation (2):

[0045] in, It represents the net runoff inflow per unit length of river channel (m² / s). A positive value indicates that runoff flows into the river channel, increasing the total water volume; Negative values ​​indicate localized flooding and diversion of water bodies; based on The numerical changes can reflect the simulated situation of water exchange in a real estuary.

[0046] Specifically, when the net runoff inflow per unit length of river channel A positive value indicates that runoff is flowing into the river channel, increasing the total water volume; a negative value indicates that the net runoff per unit length of the river channel is overflowing and diverting water to the local floodplain.

[0047] Specifically, the improved equations of motion provided in this embodiment are based on the traditional Saint-Venant equations of motion, retaining the convection term, the water surface gradient term, and the riverbed friction term, and adding a tidal wave attenuation dissipation term. ; where tidal wave attenuation dissipation term Adaptive attenuation correction factor for tidal wave (The adaptive correction coefficient corresponding to the second constraint factor) influences; the improved equation of motion can be shown in equation (3):

[0048] in, The acceleration due to gravity is taken as 9.81 m / s². The instantaneous tidal level elevation at the cross section (m); is the distance from the upper boundary point of the corresponding micro-segment to the starting point of the target river segment; n is the overall roughness of the riverbed of the corresponding micro-segment; The hydraulic radius (m); This is the inertial term for water flow convection, used to characterize the inertial characteristics of tidal current motion; This is the surface gradient pressure term, used to characterize the propagation of tidal waves upstream and downstream; This is the classic Chezy term for frictional resistance along the riverbed, used to characterize the resistance loss of the riverbed foundation.

[0049] To address this, a tidal wave attenuation dissipation term is introduced to characterize the additional tidal wave energy loss generated in the estuary. Introducing this term allows for the retention of the convection term, water surface gradient term, and riverbed friction term in the traditional momentum equation, while further considering the nonlinear effects of the additional tidal wave energy loss generated in the estuary. This enables the construction of a momentum conservation equation adapted to the nonlinear propagation of estuary tides, thereby improving the fitting degree of tidal wave deformation in complex terrain during numerical simulation.

[0050] In some embodiments, the target river segment is discretized into several micro-segments based on a preset discretization strategy and the river segment condition data of the target river segment, including but not limited to steps S201 to S203: Step S201: Based on the river section working condition data of the target river section, determine the segment nodes of the target river section in order to determine the segment control conditions of the target river section; the segment nodes are one or more of the following: abrupt change points of the river cross section, inflection points of the riverbed slope, boundary points of changes in riverbed roughness, runoff inflow points, or runoff outflow points of the target river section.

[0051] Based on the working data of the river section, several segment nodes are determined on the target river section. The purpose is to ensure that the internal topography, resistance parameters and river morphology of each micro-segment remain consistent as much as possible.

[0052] Step S202: Based on the river section condition data of the target river section, determine the river section status of the target river section at different locations, so as to determine the segment scale constraints of the target river section at different locations; the river section status is determined by the changes in riverbed slope, the changes in river cross-sectional shape along the course, the changes in local geometric parameters, and the local narrowing of river width.

[0053] Furthermore, based on the river condition data within the river segment, the river segment status of the target river segment at different locations is determined, and different segmentation scale constraints are applied to different river segment statuses. This addresses the shortcomings of traditional methods, such as large prediction errors and model instability caused by uniform segmentation in complex estuary areas. Specifically, a standard spatial step size can be used for gentle and uniform river segments, while a denser step size can be used for abrupt terrain changes and narrowing river segments, thereby reducing the need for extensive segmentation across terrain and resistance zones.

[0054] Step S203: Based on the segmented control conditions of the target river segment and its segmented scale constraints at different locations, the target river segment is discretized into several micro-segments.

[0055] Finally, based on the segmented control conditions, division, and segmented scale constraints obtained above, the target river segment is adaptively non-uniformly discretely segmented to prepare for subsequent numerical simulations.

[0056] In some embodiments, the spatiotemporal boundary corresponding to each micro-segment is determined in the following way: Based on the preset spatial step size and the discrete results of the target river segment, the spatial boundary corresponding to each micro-segment is determined; based on the preset time step size, the time step corresponding to each micro-segment and the time boundary corresponding to the time step are determined.

[0057] Based on the discretization results of the river segment and the preset step size, the spatial boundary corresponding to each micro-segment is determined when conducting subsequent numerical simulations. Similarly, the time boundary corresponding to the micro-segment is determined to prepare the step size conditions for the subsequent numerical simulations of the micro-segments.

[0058] In some embodiments, the first constraint factor is characterized by the net runoff inflow per unit length of the river segment; the first predictive constraint condition for each micro-segment within the corresponding spatiotemporal boundary is determined based on the improved continuity equation, the spatiotemporal boundary corresponding to each micro-segment, and the river geometry parameters of the target river segment, including but not limited to steps S301 to S303: Step S301: Calculate the instantaneous cross-sectional area of ​​the river channel within the corresponding spatiotemporal boundary of the micro-segment based on the river topographic geometric parameters within the corresponding spatiotemporal boundary of the micro-segment. Obtain the composite instantaneous flow rate of the micro-segment after coupling of tidal and runoff within the corresponding spatiotemporal boundary of the micro-segment. Determine the distance from the starting point of the target river segment to each time point within the spatiotemporal boundary based on the spatiotemporal boundary corresponding to the micro-segment.

[0059] Step S302: Based on the improved continuity equation, the instantaneous cross-sectional area of ​​the river channel within the corresponding spatiotemporal boundary of the micro-segment, the combined instantaneous flow rate after the coupling of tide and runoff, and the distance from the starting point of the target river segment at each time point, the net runoff inflow per unit length of the river channel within the corresponding spatiotemporal boundary of the micro-segment is calculated.

[0060] Step S303: Based on the improved continuity equation and the net runoff inflow per unit length of the micro-segment within the corresponding spatiotemporal boundary, determine the first prediction constraint condition of the micro-segment within the corresponding spatiotemporal boundary.

[0061] Steps S301 to S303 indicate the process of determining the first prediction constraint for each micro-segment based on the relevant data obtained according to equation (2).

[0062] Similarly, the second prediction constraint for each micro-segment can be determined based on the relevant data obtained according to the above equation (3).

[0063] In step S102 of some embodiments, the second prediction constraint condition of the micro-segment is determined based on the improved equation of motion and the second constraint factor of the micro-segment within the corresponding spatiotemporal boundary; the second constraint factor is affected by the adaptive correction coefficient; the adaptive correction coefficient of the micro-segment within the corresponding spatiotemporal boundary is confirmed in the following way: Step S401: Analyze the riverbed resistance characteristic parameters of the micro-segment within the corresponding spatiotemporal boundary, and determine the cross-sectional cross-bend situation, riverbed slope, and topographic undulation of the micro-segment within the corresponding spatiotemporal boundary based on the analysis results. Step S402: Determine the degree of tidal energy loss of the micro-segment within the corresponding spatiotemporal boundary based on the cross-sectional bundle crossing situation, riverbed slope, and topographic undulation within the corresponding spatiotemporal boundary. Determine the adaptive correction coefficient of the micro-segment within the corresponding spatiotemporal boundary based on the judgment result and the preset value logic.

[0064] Steps S401 to S402 are the process of confirming the adaptive correction coefficient corresponding to the second constraint factor; corresponding to the adaptive tidal wave attenuation correction coefficient corresponding to the tidal wave attenuation dissipation term proposed in this embodiment; the tidal wave attenuation dissipation term proposed in this embodiment is used to quantify the additional tidal wave energy loss caused by the narrowing of the estuary section, the undulation of the riverbed, and the countercurrent of tidal runoff, and is adapted to the nonlinear attenuation and distortion of estuary tidal waves; while the adaptive tidal wave attenuation correction coefficient is a demonstration of the adaptive adjustment characteristics of this term; this correction coefficient is dynamically and adaptively confirmed according to the micro-segment topographic parameters, which is more adaptable than the fixed constant in the traditional method; specifically, the adaptive tidal wave attenuation correction coefficient The relevant formula can be shown in equation (4):

[0065] in, The reference attenuation coefficient is fixed at a value of 0.001 to 0.003. ; The standard width of the open river at the river mouth (m); The current width of the river segment (m); The baseline slope for the river mouth; This represents the longitudinal slope of the current micro-segment of the riverbed.

[0066] Specifically, the preset value logic in step S402 above includes: The narrower the cross-section, the steeper the riverbed slope, and the more dramatic the topographic relief. The larger the value, the stronger the tidal wave energy loss, accurately matching the characteristics of rapid tidal wave attenuation and severe distortion in areas with abrupt topographic changes; open and gentle river sections. Smaller values ​​reduce additional losses and align with the natural, gentle propagation pattern. The setting of the adaptive attenuation correction coefficient based on tidal waves can effectively solve the problems of fixed loss coefficients and poor adaptability in traditional models.

[0067] In some embodiments, the numerical simulation process is implemented through the following steps: Step S501: Assign initial field values ​​to each micro-segment based on the river channel geometry parameters and riverbed resistance characteristic parameters of the target river section.

[0068] Initial field values ​​are assigned to each micro-segment based on relevant data from the target river section, laying the data foundation for subsequent numerical simulations.

[0069] Step S502: Determine the temporal boundary conditions of each micro-segment within the corresponding spatiotemporal boundary based on the boundary hydrological data of the target river section.

[0070] Based on the boundary hydrological data of the target river segment, determine the temporal boundary conditions of each micro-segment within the corresponding spatiotemporal boundary, and then determine the boundary condition basis corresponding to the first prediction constraint and the second prediction constraint.

[0071] Step S503: Based on the initial field assignment results of each micro-segment and the first prediction constraint, second prediction constraint and temporal boundary condition within the corresponding spatiotemporal boundary of each micro-segment, numerical simulation is performed on each micro-segment according to the time sequence, and numerical simulation results of the target river segment are obtained based on the numerical simulation results of each micro-segment.

[0072] Numerical simulations are performed based on the assignment results of each micro-segment, the corresponding constraints, and the temporal boundary conditions to finally obtain the numerical simulation results of the target river segment.

[0073] In some embodiments, the numerical simulation results for each micro-segment include tidal level data and flow rate data within the spatiotemporal boundary corresponding to the micro-segment; the method further includes: Determine whether the tidal level difference and flow rate difference within the spatiotemporal boundary corresponding to adjacent micro-segments in a number of micro-segments all meet the preset convergence threshold condition. If not, re-perform numerical simulation for each micro-segment until the tidal level difference and flow rate difference within the spatiotemporal boundary corresponding to adjacent micro-segments in a number of micro-segments all meet the preset convergence threshold condition or the preset number of numerical simulations is reached.

[0074] After numerical simulation based on the initial field assignment results, corresponding prediction constraints, and time series boundaries of each micro-segment, the tidal level data and flow rate data within the spatiotemporal boundary corresponding to each micro-segment are obtained. The numerical simulation is repeated for each micro-segment until it is determined that the tidal level difference and flow rate difference within the spatiotemporal boundary corresponding to several adjacent micro-segments meet the preset convergence threshold condition, or the preset number of numerical simulations is reached.

[0075] To avoid numerical oscillations and ensure prediction stability, the numerical simulation process of this embodiment sets dual convergence thresholds, using the difference in tidal level and the difference in flow rate between two adjacent iterations as the judgment criteria; the specific judgment criteria can be shown in equation (5).

[0076] In the formula: This represents the number of iterations. The tidal level convergence threshold is set at 0.001m for engineering purposes. The flow convergence threshold is set to 0.01 m³ / s. When all sections of the entire river segment simultaneously meet both convergence conditions, the prediction for this time step is considered converged, the iteration is stopped, and the current tidal level, flow rate, velocity, and phase data of each micro-segment are saved. If convergence is not achieved, the variables are updated and the iteration continues until the convergence conditions are met or the maximum number of iterations is reached.

[0077] Specifically, after completing the numerical simulation, a complete set of tidal propagation parameters for the entire river segment is output, including: (1) Time series data: hourly tidal level process curves of each section, hourly tidal velocity and direction process, hourly tidal phase time series; (2) Characteristic data: maximum tidal range, minimum tidal range, average tidal range, duration of rising and falling tides, tidal wave propagation speed, and total phase lag at each cross section along the route; (3) Regular data: tidal range attenuation curve along the path, tidal phase lag distribution curve along the path, and tidal dynamics attenuation distribution pattern along the path.

[0078] In one embodiment, a typical tidal estuary in the plains of southeastern coastal China is selected as a field verification case. This estuary is characterized by a narrow channel in the middle section, slight undulations in the riverbed, significant seasonal fluctuations in runoff, and obvious tidal wave distortion and attenuation, making it a typical estuary with complex working conditions. This case is used to verify the advantages of the proposed method compared to traditional analytical algorithms and conventional one-dimensional numerical algorithms; wherein: 1. Basic parameters and working condition settings for engineering cases The selected study section, totaling 30 km in length, is a typical tidal estuary segment characterized by a gradual change in the upper reaches, narrowing in the middle section, and widening in the lower reaches, fully covering the core area of ​​tidal intrusion. Specific basic parameters are as follows: (1) Topographic parameters: the baseline width of the river mouth =1200m, minimum river width in the narrow section in the middle section =650m; average water depth at the mouth =7.2m, average riverbed slope across the entire area =0.00023, mid-section undulating area =0.00045; the riverbed medium is silty sand, and the overall roughness of the entire area is... =0.022.

[0079] (2) Hydrological conditions: The typical normal water level + alternating tidal and neap tide conditions were selected, with a continuous forecast duration of 30 days; the mean tidal cycle of the outer sea boundary. =12.4h, the average tidal range at the mouth of the river is 2.86m during spring tides and 1.12m during neap tides; the average upstream runoff is =185 m³ / s, the maximum instantaneous runoff during the flood season is 420 m³ / s, and the multi-year maximum runoff is =860m³ / s.

[0080] (3) Model prediction parameters: time step =60s, space step =500m, the entire area is discretized into 60 uniform micro-segments + mid-section topographically reinforced micro-segments; tidal wave reference attenuation coefficient =0.002 Runoff weighting coefficient =0.4, terrain influence coefficient =0.4; Tidal convergence threshold =0.001m, flow convergence threshold =0.01m³ / s.

[0081] (4) Algorithm settings for comparison: Three mainstream methods are used simultaneously for prediction under the same working conditions for accuracy comparison: ① Traditional Saint-Venant analytical simplification method (method 1); ② Conventional fixed parameter one-dimensional numerical method (method 2); ③ The adaptive coupling correction method of this invention (method 3).

[0082] 2. Complete step-by-step implementation process Step 1: Data Preprocessing and River Section Discretization Topographic data of the river section, hourly offshore tide data over 30 days, and hourly runoff data of the upstream area were collected and studied. The 3σ criterion was used to remove outliers caused by wind and waves, and short-term missing data were interpolated and uniformly converted to the national elevation datum. Based on the locations of abrupt changes in the river cross-section, the 30km river section was divided into 60 standard micro-segments. The narrow, undulating 2km section in the middle was further subdivided into 40 fine micro-segments. Average river width, water depth, slope, and roughness parameters were assigned to each segment, completing the parameter ledger construction. Initial still water surface elevation was set for the entire area, and initial flow and velocity were reset to zero, completing the initial field assignment.

[0083] like Figure 2 As shown, Figure 2 This is a schematic diagram of the adaptive non-uniform discrete micro-segmentation of the estuary section provided in the embodiments of this application.

[0084] Step 2: Substitute the parameters of the improved control equations Substituting the topographic and resistance parameters of each micro-segment into the improved continuity and motion equations, and dynamically determining the tidal wave attenuation correction coefficient for each micro-segment based on the adaptive α value formula: downstream open river section =0.0021~0.0025 The middle section is narrow and undulating. =0.0032~0.0038 It accurately matches differences in terrain resistance. Simultaneously, it assigns the source term of runoff along the route based on real-time runoff flow. This enables fully coupled modeling of runoff, topography, and tidal wave loss.

[0085] Step 3: Adaptive Correction of Tidal Phase Delay Phase correction is performed step-by-step and segment-by-segment: First, the basic phase delay of each segment is determined based on the shallow water wave formula. Then, based on the real-time runoff ratio Determine runoff correction factor The terrain correction factor is determined based on the cross-sectional narrowing ratio. ;according to The combined phase delay of each micro-segment is obtained by coupling. The total phase lag of each section relative to the open sea is obtained by accumulating along the process, and the tide level and tidal current time series curves are dynamically shifted and corrected to adapt to the dynamic differences in operating conditions such as neap tides, high and low runoff.

[0086] Specifically, the fundamental phase delay This can be confirmed by equation (6):

[0087] In the formula: The basic phase delay (rad) is a single microsegment. To calculate the spatial step size (m) of the micro-segment; The tidal mean period (s); The acceleration due to gravity (m / s²) The average water depth of the micro-segment (m), and This represents the propagation wave speed in shallow water. This formula describes the inherent phase lag characteristic of tidal waves in a river channel under purely topographical and water depth conditions.

[0088] Specifically, runoff correction factor This can be confirmed by equation (7):

[0089] In the formula: This is the runoff phase correction factor (its value is always greater than 1). The runoff impact weighting coefficient is adaptively selected according to the estuary type, with a value of 0.3~0.5 for gentle plain estuaries and 0.5~0.8 for narrow mountain estuaries; The instantaneous upstream runoff flow rate (m³ / s) corresponds to the micro-segment; The tidal mean tidal flow rate (m³ / s) at this cross section is used. The nonlinear phase interference effect of runoff backflow is quantified by the ratio of runoff to tidal flow rate.

[0090] Specifically, terrain correction factor This can be confirmed by equation (8):

[0091] In the formula: This is the terrain phase correction factor; The topographic influence coefficient is fixed at 0.4. The baseline river width (m) for the downstream open section; The current calculated micro-segment river width (m) is given. The greater the narrowing of the cross-section, the larger the correction factor, to accurately match the characteristics of tidal wave propagation being obstructed and phase lagging significantly in areas with abrupt topographic changes.

[0092] Step 4: Finite difference discrete iterative solution The governing equations were discretized globally using a forward time-difference + central spatial-difference scheme. With measured tidal levels in the downstream open ocean as the driving boundary and measured runoff in the upstream as the inflow boundary, a step-by-step iterative strategy of "flux pre-calculation - dynamic correction - phase coupling - global recursion" was employed for numerical simulation. Each time step iterated until both tidal level and flow rate met the convergence thresholds, automatically proceeding to the next time series. This process continuously solved the 30-day full-time hydrodynamic process, outputting initial tidal level, flow rate, velocity, and phase time series data for each cross-section.

[0093] Step 5: Secondary Correction for Tidal Range Attenuation and Output of Results Predicting the reference tidal range along the route based on the topographic reference attenuation formula Superimposed runoff dynamic correction coefficient Terrain correction coefficient It differentiates tidal range by neap tide, flood, and low tide conditions, and corrects for variations in tidal range. It also specifically corrects for asymmetric deviations in tidal duration, eliminating cumulative errors from numerical iterations. Finally, it outputs a complete set of parameters for hourly tidal level processes, tidal velocity, propagation phase, tidal range attenuation patterns along the path, and tidal wave propagation speed for each micro-segment across the entire domain.

[0094] Specifically, the reference tidal range along the route It can be predicted using equation (9):

[0095] In the formula: Distance from the outer sea boundary The baseline tidal range (m) at the location; The measured tidal range (m) is the outer sea boundary. This is the topographic attenuation coefficient, which is positively correlated with the riverbed roughness. This represents the current cross-section distance from the outer sea boundary (m). The average water depth at the river mouth (m); The reference width of the river mouth (m).

[0096] Specifically, the runoff dynamic correction coefficient is superimposed. Terrain correction coefficient The process of differentiating tidal range by differentiating between neap tides and low tides can be achieved by equation (10):

[0097] The parameters in the formula are defined as follows: This is the corrected tidal range. The weight for adjusting runoff attenuation is set to 0.25~0.4, with a larger value during flood season and a smaller value during dry season. The multi-year average maximum runoff of the river section (m³ / s) is used for dimensionless normalization. The local topographic correction factor is 1.0 for open river sections and 0.7 to 0.95 for river sections with narrow cross sections or sudden rise in riverbed. The more severe the topographic contraction, the stronger the tidal range attenuation, and the smaller the correction factor.

[0098] 3. Prediction results of the method of the present invention Through the complete prediction process described above, we obtained the full-dimensional prediction results of tidal propagation within a 30km range of the target river section. The core feature results are as follows: (1) Tide level simulation results: The tide level process curve along the entire area is smooth and continuous, without sudden oscillations; the tide level at the downstream outlet is completely consistent with the measured value, and the trend of the tide level change at the farthest upstream tide-sensing section is consistent with the measured height.

[0099] (2) Tidal range attenuation pattern: Under spring tide conditions, the tidal range at the mouth is 2.86m, the tidal range at 15km decreases to 1.62m, and the tidal range at the upstream end of 30km decreases to 0.48m; under neap tide conditions, the tidal range at the mouth is 1.12m, and the tidal range at the upstream end decreases to 0.18m. The attenuation trend is consistent with the nonlinear attenuation characteristics of natural estuaries.

[0100] (3) Phase lag results: The average phase lag times of the tide propagation to 10km, 20km and 30km positions are 12min, 27min and 43min respectively. The phase lag increment during the flood season is significantly greater than that during the dry season, accurately restoring the runoff backwater effect.

[0101] (4) Tidal dynamics results: The tidal velocity along the river gradually decreased from 0.85 m / s at the mouth to 0.22 m / s upstream. The velocity in the narrow section of the middle reaches increased locally and the loss intensified, which is consistent with the field observation.

[0102] 4. Comparative Analysis of the Precision Quantification of Different Methods Using hourly measured tide level, tidal range, and tidal phase data as the true values, the mean absolute error, maximum error, and fitting success rate of the three algorithms were statistically analyzed, and the quantitative comparison results are as follows: (1) Comparison of tide level prediction accuracy Traditional analytical simplification method (Method 1): average error ±11.2cm, maximum error 23.5cm, data fitting pass rate 78.2%; there is obvious systematic bias, which cannot be adapted to the mid-section terrain narrowing and runoff backing conditions.

[0103] Conventional fixed-parameter one-dimensional numerical method (Method 2): average error ±6.8cm, maximum error 15.3cm, data fitting pass rate 89.5%; accuracy is better than traditional methods, but fixed loss parameters cannot be adapted to dynamic working conditions, and the error increases significantly during high tide and flood season.

[0104] The method in this embodiment (Method 3) has an average error of ±2.8cm, a maximum error of 5.1cm, and a data fitting pass rate of 98.7%. The overall error is extremely small, and the fitting degree is excellent under all working conditions, including tidal bore, flood, and dry season.

[0105] (2) Comparison of prediction accuracy of tidal range attenuation Method 1 has an average relative error of 18.6% in tidal range, which seriously underestimates the attenuation of tidal range in areas with abrupt changes in topography; Method 2 has an average relative error of 9.3% in tidal range, with significant deviations under dynamic conditions; The method of this invention has an average relative error of only 3.2% in tidal range, and the attenuation curve along the path basically coincides with the measured curve.

[0106] (3) Comparison of tidal phase timing accuracy Method 1 has an average phase deviation of 14.8 min, which cannot characterize dynamic phase lag; Method 2 has an average phase deviation of 8.2 min; The method of this invention has an average phase deviation of 4.1 min, and its timing synchronization is significantly better than that of traditional methods.

[0107] 5. Efficiency Comparison and Verification of Comprehensive Advantages Under the same hardware computing power and the same prediction time: the traditional analytical method has the shortest prediction time but extremely poor accuracy; the conventional two-dimensional numerical model takes 2.7 times longer to predict, consumes a lot of computing power and cannot make fast predictions; the method of this invention has far exceeded the accuracy of the traditional one-dimensional algorithm and is close to the accuracy of the two-dimensional fine model, while improving the prediction efficiency by more than 60%, truly achieving a balance between high accuracy and high efficiency.

[0108] In summary, the embodiments of this method have at least the following beneficial effects: (1) The method of this application is based on the one-dimensional Saint-Venant equation for numerical simulation. It simply introduces the runoff coupling source term and the tidal wave attenuation dissipation term to improve the traditional equation. Compared with numerical simulation methods that use two-dimensional or higher numerical models, the prediction process of this method is simpler. By constructing an improved set of hydrodynamic control equations that can consider the nonlinear coupling effect of runoff and tide and adapt to complex estuary variable conditions, the accuracy of predicting estuary tidal propagation is effectively improved.

[0109] (2) The setting of the adaptive attenuation correction coefficient based on the tidal wave can effectively solve the problems of fixed loss coefficient and poor adaptability of the traditional model, and improve the fitting adaptability of tidal wave deformation under complex estuary conditions.

[0110] (3) The prediction process sets a dual convergence threshold, which effectively improves the prediction stability.

[0111] Please see Figure 3 This application also provides a numerical simulation system for estuarine tidal propagation, which can implement the above-mentioned method. The system includes: The data acquisition module is used to acquire the river topographic geometric parameters, riverbed resistance characteristic parameters, river section operating data and boundary hydrological data of the target river section. Based on the preset discretization strategy and the river section operating data of the target river section, the target river section is discretized into several micro-segments and the spatiotemporal boundary corresponding to each micro-segment is determined. The continuity constraint determination module is used to determine the first predictive constraint conditions of each micro-segment within the corresponding spatiotemporal boundary based on the improved continuity equation, the spatiotemporal boundary corresponding to each micro-segment, and the channel geometry parameters of the target river segment. The improved continuity equation introduces a first constraint factor based on the one-dimensional Saint-Venant continuity equation. The first constraint factor considers the nonlinear effects caused by the exchange of tidal and runoff water bodies in the estuary. The motion constraint determination module is used to determine the second predictive constraint conditions of each micro-segment within the corresponding spatiotemporal boundary based on the improved motion equation, the spatiotemporal boundary corresponding to each micro-segment, the channel topographic geometric parameters, channel resistance characteristic parameters, and boundary hydrological data of the target river segment; the improved motion equation introduces a second constraint factor based on the one-dimensional Saint-Venant motion equation; the second constraint factor takes into account the influence of additional tidal wave energy loss generated in the estuary. The numerical simulation module is used to perform numerical simulations based on the river topographic geometric parameters, riverbed resistance characteristic parameters, and boundary hydrological data of the target river segment, as well as the first and second prediction constraints of each micro-segment within the corresponding spatiotemporal boundary. The prediction results of the estuary tidal propagation of the target river segment are obtained based on the numerical simulation results.

[0112] It is understood that the content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0113] like Figure 4 As shown in the illustration, this application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0114] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0115] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0116] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0117] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0118] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0119] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0120] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0121] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0122] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0123] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0124] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0125] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0126] If the integrated unit is implemented as 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 all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0127] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A numerical simulation method for estuarine tidal propagation, characterized in that, The method includes the following steps: The target river section is obtained by acquiring the river topographic geometric parameters, riverbed resistance characteristic parameters, river section operating data and boundary hydrological data. Based on the preset discretization strategy and the river section operating data of the target river section, the target river section is discretized into several micro-segments and the spatiotemporal boundary corresponding to each micro-segment is determined. Based on the improved continuity equation, the spatiotemporal boundary corresponding to each micro-segment, and the channel geometry parameters of the target river segment, the first prediction constraint condition of each micro-segment within the corresponding spatiotemporal boundary is determined; the improved continuity equation introduces a first constraint factor based on the one-dimensional Saint-Venant continuity equation; the first constraint factor considers the nonlinear effects caused by the exchange of estuarine tides and runoff. Based on the improved equation of motion, the spatiotemporal boundary corresponding to each micro-segment, and the channel topographic geometric parameters, channel resistance characteristic parameters, and boundary hydrological data of the target river segment, the second predictive constraint condition for each micro-segment within the corresponding spatiotemporal boundary is determined; the improved equation of motion introduces a second constraint factor based on the one-dimensional Saint-Venant equation of motion; the second constraint factor considers the influence of additional tidal wave energy loss generated in the estuary. Numerical simulations are performed based on the river topographic geometric parameters, riverbed resistance characteristic parameters, and boundary hydrological data of the target river section, as well as the first and second prediction constraints of each micro-segment within the corresponding spatiotemporal boundary. The estuary tidal propagation prediction results of the target river section are obtained based on the numerical simulation results.

2. The method according to claim 1, characterized in that, The method, based on a preset discretization strategy and the river condition data of the target river segment, discretizes the target river segment into several micro-segments, including: Based on the river section condition data of the target river section, the segment nodes of the target river section are determined to determine the segment control conditions of the target river section; the segment nodes are one or more of the following: abrupt change points in the river cross section, inflection points in the riverbed slope, boundary points of changes in riverbed roughness, runoff inflow points, or runoff outflow points of the target river section. Based on the river section condition data of the target river section, the river section state at different locations is determined to determine the segment scale constraints of the target river section at different locations; the river section state is determined by the changes in riverbed slope, the changes in river cross-sectional shape along the course, the changes in local geometric parameters, and the local narrowing of river width. Based on the segmented control conditions of the target river segment and the segmented scale constraints at different locations, the target river segment is discretized into several micro-segments.

3. The method according to claim 1, characterized in that, The spatiotemporal boundary corresponding to each micro-segment is determined in the following way: Based on the preset spatial step size and the discrete results of the target river segment, the spatial boundary corresponding to each micro-segment is determined; The time step and the time boundary corresponding to each micro-segment are determined based on a preset time step.

4. The method according to claim 1, characterized in that, The first constraint factor is characterized by the net runoff inflow per unit length of the river segment; the determination of the first prediction constraint condition for each micro-segment within the corresponding spatiotemporal boundary based on the improved continuity equation, the spatiotemporal boundary corresponding to each micro-segment, and the river channel geometry parameters of the target river segment includes: The instantaneous cross-sectional area of ​​the river channel within the corresponding spatiotemporal boundary is obtained by calculating the river topographic geometric parameters of the micro-segment within the corresponding spatiotemporal boundary. Obtain the composite instantaneous flow rate of the micro-segment after the coupling of tidal and runoff flows within the corresponding spatiotemporal boundary; The distance from the starting point of the target river section to each time point within the spatiotemporal boundary is determined based on the spatiotemporal boundary corresponding to the micro-segment; The net runoff inflow per unit length of the river within the corresponding spatiotemporal boundary is calculated based on the improved continuity equation, the instantaneous cross-sectional area of ​​the river within the corresponding spatiotemporal boundary of the micro-segment, the combined instantaneous flow rate after the coupling of tide and runoff, and the distance from the starting point of the target river segment at each time point. The first predictive constraint condition for the micro-segment within the corresponding spatiotemporal boundary is determined based on the improved continuity equation and the net runoff inflow per unit length of the river channel within the corresponding spatiotemporal boundary.

5. The method according to claim 1, characterized in that, The second predictive constraint condition of the micro-segment is determined based on the improved equation of motion and the second constraint factor of the micro-segment within the corresponding spatiotemporal boundary; the second constraint factor is affected by an adaptive correction coefficient; the adaptive correction coefficient of the micro-segment within the corresponding spatiotemporal boundary is confirmed in the following way: Based on the analysis of the riverbed resistance characteristic parameters of the micro-segment within the corresponding spatiotemporal boundary, the cross-sectional cross-bundle situation, riverbed slope situation and topographic undulation situation of the micro-segment within the corresponding spatiotemporal boundary are determined according to the analysis results. The degree of tidal energy loss of the micro-segment within the corresponding spatiotemporal boundary is determined based on the cross-sectional bundle crossing situation, riverbed slope, and topographic undulation within the corresponding spatiotemporal boundary. The adaptive correction coefficient of the micro-segment within the corresponding spatiotemporal boundary is determined based on the judgment result and the preset value logic.

6. The method according to claim 1, characterized in that, The numerical simulation process is achieved through the following steps: An initial field value is assigned to each micro-segment based on the river channel geometry parameters and riverbed resistance characteristic parameters of the target river section. Based on the boundary hydrological data of the target river section, determine the temporal boundary conditions of each micro-segment within the corresponding spatiotemporal boundary; According to the time sequence, numerical simulation is performed on each micro-segment based on the initial field assignment result of each micro-segment and the first prediction constraint, the second prediction constraint and the temporal boundary condition within the corresponding spatiotemporal boundary of each micro-segment. The numerical simulation result of the target river segment is obtained based on the numerical simulation result of each micro-segment.

7. The method according to claim 6, characterized in that, The numerical simulation results for each micro-segment include tidal level data and flow rate data within the spatiotemporal boundary corresponding to the micro-segment; the method further includes: Determine whether the tidal level difference and flow rate difference within the spatiotemporal boundary corresponding to adjacent micro-segments among the plurality of micro-segments all meet the preset convergence threshold condition. If not, re-perform numerical simulation for each micro-segment until the tidal level difference and flow rate difference within the spatiotemporal boundary corresponding to adjacent micro-segments among the plurality of micro-segments all meet the preset convergence threshold condition or reach the preset number of numerical simulations.

8. A numerical simulation system for estuarine tidal propagation, characterized in that, The system includes: The data acquisition module is used to acquire the river channel topographic geometric parameters, riverbed resistance characteristic parameters, river section operating condition data and boundary hydrological data of the target river section. Based on the preset discretization strategy and the river section operating condition data of the target river section, the target river section is discretized into several micro-segments and the spatiotemporal boundary corresponding to each micro-segment is determined. The continuity constraint determination module is used to determine the first predicted constraint conditions of each micro-segment within the corresponding spatiotemporal boundary based on the improved continuity equation, the spatiotemporal boundary corresponding to each micro-segment, and the channel geometry parameters of the target river segment; the improved continuity equation introduces a first constraint factor based on the one-dimensional Saint-Venant continuity equation; the first constraint factor considers the nonlinear effects caused by the exchange of tidal and runoff water bodies at the estuary. The motion constraint determination module is used to determine the second predictive constraint conditions of each micro-segment within the corresponding spatiotemporal boundary based on the improved motion equation, the spatiotemporal boundary corresponding to each micro-segment, and the channel topographic geometric parameters, channel resistance characteristic parameters, and boundary hydrological data of the target river section; the improved motion equation introduces a second constraint factor based on the one-dimensional Saint-Venant motion equation; the second constraint factor considers the influence of additional tidal wave energy loss generated in the estuary. The numerical simulation module is used to perform numerical simulation based on the river topographic geometric parameters, riverbed resistance characteristic parameters, and boundary hydrological data of the target river section, as well as the first and second prediction constraints of each micro-segment within the corresponding spatiotemporal boundary, and to obtain the estuary tidal propagation prediction result of the target river section based on the numerical simulation results.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

Images