A method and system for simulating hydrodynamics of ship locks in estuary areas

Abstract

The present application discloses a method and system for simulating hydrodynamics of ship locks in an estuary area, and relates to the field of numerical simulation, including: obtaining ship lock data in an estuary area; collecting hydrological data; constructing a hydrodynamic model based on the Euler-Lagrangian circulation model according to the ship lock data and hydrological data in the estuary area, wherein the hydrodynamic model includes a bottom friction coefficient that describes the influence of riverbed roughness on water flow movement; using the hydrodynamic model, obtaining the simulated flow velocity of the ship lock in the estuary area through numerical solution; using the ensemble Kalman filter algorithm, comparing the simulated flow velocity with the measured flow velocity, optimizing the bottom friction coefficient, and obtaining the optimal bottom friction coefficient; using the optimal bottom friction coefficient as input, using the hydrodynamic model, simulating the hydrodynamic process of the ship lock in the estuary area, and obtaining the flow field change process of the ship lock in the estuary area. In view of the fact that the bottom friction coefficient in the prior art cannot dynamically adapt to the complex estuary environment, the present application realizes the accurate simulation of the complex hydrodynamic environment of the ship lock in the estuary area.

Description

Technical Field

[0001] The present application relates to the field of numerical simulation, and more specifically, to a method and system for simulating hydrodynamics of ship locks in estuary areas. Background Art

[0002] With the rapid development of the economy and society, inland waterway shipping has become increasingly prominent in the comprehensive transportation system. As an important water conservancy hub connecting the inland river and the open sea, the ship lock in the estuary area plays a key role in the water transport network. In recent years, the flow of ships passing through the locks in the estuary area has increased significantly, and the operating pressure of the ship locks has continued to increase, which has put forward higher requirements on the safety and durability of the hydraulic structures of the ship locks. In order to ensure the normal operation and navigation safety of the ship locks, it is necessary to carry out refined simulation and analysis of the hydrodynamic characteristics of the ship locks in the estuary area, which will provide important theoretical basis and technical support for the planning and design, operation management, maintenance and reinforcement of the ship locks.

[0003] The estuary area is affected by the periodicity of natural tides, with large water level fluctuations, variable flow velocity and direction, and very complex hydrodynamic processes. Accurately predicting the flow movement laws of ship locks in estuary areas is crucial for optimizing the structural layout of lock chambers, determining the optimal water storage level, and formulating reasonable navigation scheduling plans. At present, there are still deficiencies in theoretical analysis and empirical research on the precise simulation of flow fields under complex hydrodynamic conditions of ship locks in estuary areas. Traditional hydrodynamic models are mostly based on simplified assumptions and empirical parameters, which make it difficult to fully consider the dynamic characteristics of estuaries, and the accuracy and reliability of simulation results need to be improved.

[0004] In addition, in actual engineering applications, due to the particularity of the environment in which the ship locks in the estuary area are located and the limitations of monitoring facilities, there is often a lack of long-term, complete and detailed hydrological data. Traditional hydrodynamic models usually rely on a large amount of hydrological observation data as input conditions, and the accuracy of the model depends to a large extent on the quality and quantity of hydrological data. In the absence of sufficient hydrological data, how to ensure the simulation accuracy and applicability of the hydrodynamic model is a key issue that needs to be solved urgently.

[0005] In order to break through the above technical bottlenecks, domestic and foreign scholars have conducted a lot of research and exploration. Among them, the ELCIRC three-dimensional hydrodynamic model has attracted widespread attention for its open source, high efficiency and stability. As an excellent open source software, ELCIRC has significant advantages such as high modularity, high computational efficiency and good numerical stability. It has strong flexibility and scalability, and users can perform secondary development and improvement according to actual needs.

[0006] In the ELCIRC model, the bottom friction coefficient It is a key control parameter that directly affects the simulation accuracy of water flow movement. Traditional hydrodynamic calculations usually use a constant bottom friction coefficient value in the entire study area, which is mainly based on empirical formulas or repeated debugging. However, in the complex environment of the estuary area with obvious dynamic changes, the bottom friction coefficient is not static, but is affected by multiple factors such as tides, topography, and bottom quality, showing significant temporal and spatial distribution characteristics. It is difficult to accurately describe the dynamic changes of water flow movement in the estuary area by using a single constant bottom friction coefficient, and it is impossible to accurately simulate the flow field characteristics in a complex environment. Summary of the invention

[0007] 1. Technical problems to be solved

[0008] In response to the problem in the prior art that the bottom friction coefficient cannot dynamically adapt to the complex estuary environment, the present application provides a method and system for simulating the hydrodynamics of ship locks in estuary areas. The hydrodynamic model is based on the Euler-Lagrangian circulation model, and the bottom friction coefficient is dynamically optimized by introducing the ensemble Kalman filter algorithm, thereby achieving accurate simulation of the complex hydrodynamic environment of ship locks in estuary areas.

[0009] 2. Technical solution

[0010] The purpose of this application is achieved through the following technical solutions.

[0011] One aspect of an embodiment of the present specification provides a method for simulating hydrodynamics of ship locks in an estuary area, comprising: obtaining ship lock data in the estuary area; collecting hydrological data; constructing a hydrodynamic model based on the Euler-Lagrangian circulation model according to the ship lock data and hydrological data in the estuary area, wherein the hydrodynamic model includes a bottom friction coefficient describing the influence of riverbed roughness on water flow movement. ; Using the hydrodynamic model, the simulated flow velocity of the ship lock in the estuary area is obtained through numerical solution; using the ensemble Kalman filter algorithm, the simulated flow velocity is compared with the measured flow velocity, and the bottom friction coefficient is calculated. Perform optimization training to obtain the optimal bottom friction coefficient ; The optimal bottom friction coefficient As input, the hydrodynamic model is used to simulate the hydrodynamic process of the ship lock in the estuary area, and the flow field change process of the ship lock in the estuary area is obtained. The hydrodynamic process of the ship lock in the estuary area under different upstream and downstream water level conditions is simulated, and the relationship curve between the water level difference and the flow velocity under the lock chamber opening mode is established.

[0012] Among them, the Euler-Lagrangian circulation model is a three-dimensional hydrodynamic numerical model used to simulate the water flow movement in waters such as estuaries, coasts and rivers. It combines the advantages of the Euler method and the Lagrangian method, and uses the finite volume method and semi-implicit time integration format to solve the Reynolds-averaged Navier-Stokes equations and continuity equations for incompressible fluids. In this application, a hydrodynamic model of the ship lock in the estuary area is constructed based on the Euler-Lagrangian circulation model. By considering the three-dimensional geometric structure, boundary conditions and initial conditions of the ship lock, a set of hydrodynamic control equations including continuity equations, momentum equations and turbulence equations is established. Using the ELCIRC model for numerical solution, a three-dimensional flow field distribution reflecting the water flow movement characteristics of the ship lock in the estuary area is obtained.

[0013] The bottom friction coefficient is a key parameter that describes the effect of riverbed roughness on water flow. It reflects the interaction between the riverbed and the water flow, and characterizes the magnitude of the frictional resistance that the riverbed exerts on the water flow. The value of the bottom friction coefficient is related to factors such as the roughness of the riverbed, the composition of sediments, and the distribution of vegetation, and directly affects the flow velocity, turbulence intensity, and energy loss of the water flow. In this application, the bottom friction coefficient is introduced into the control equations of the hydrodynamic model as a key parameter that describes the effect of riverbed roughness. By reasonably setting the initial value of the bottom friction coefficient and using the ensemble Kalman filter algorithm for optimization training, the optimal bottom friction coefficient that can accurately reflect the actual water flow movement of the ship lock in the estuary area is obtained.

[0014] The Ensemble Kalman Filter (EnKF) algorithm is a sequential data assimilation algorithm based on the Monte Carlo method, which is used to estimate the state variables and parameters of nonlinear dynamic systems. It represents the probability distribution of state variables and parameters through a set, and uses the observed data to correct the set members, so as to obtain an estimated result that is closer to the true value. The EnKF algorithm can effectively handle the uncertainty in the model and observed data, and improve the accuracy of state variable and parameter estimation. In this application, the EnKF algorithm is used to optimize the bottom friction coefficient in the hydrodynamic model. By taking the bottom friction coefficient as the state variable, a set of initial sets is generated. The hydrodynamic model is used to perform ensemble prediction to obtain a set of predicted results of the simulated flow velocity. The measured flow velocity is used as the observed value, and the predicted result set is corrected by calculating the prior error covariance and the Kalman gain to obtain an updated set of the bottom friction coefficient. Through iterative optimization, the optimal bottom friction coefficient that can minimize the difference between the simulated flow velocity and the measured flow velocity is finally obtained.

[0015] Furthermore, a hydrodynamic model based on the Euler-Lagrangian circulation model is constructed, including: first, constructing a three-dimensional geometric model of the ship lock in the estuary area, and using three-dimensional modeling software (such as AutoCAD, SolidWorks, etc.) to construct a three-dimensional geometric model of the ship lock in the estuary area based on the acquired ship lock data in the estuary area, including the ship lock structure layout, size parameters and river terrain data. Specifically, according to the ship lock structure layout and size parameters, a three-dimensional solid model of the ship lock chamber, gate, gate head, gate tail and other structures is established; according to the river terrain data, a three-dimensional surface model of the river bed and the shore is established; the ship lock structure model and the river terrain model are assembled to obtain a complete three-dimensional geometric model of the ship lock in the estuary area.

[0016] According to the acquired hydrological data, including upstream water level, downstream water level and measured flow velocity data, the boundary conditions and initial conditions of the three-dimensional geometric model of the ship lock in the estuary area are set. Specifically, the upstream water level data is used as the upstream inflow boundary condition of the three-dimensional geometric model, and the downstream water level data is used as the downstream outflow boundary condition; the measured flow velocity data is used as the initial flow field condition of the three-dimensional geometric model, and the flow velocity boundary condition is set at the ship lock gate; the roughness boundary condition is set on the riverbed surface and solid wall surface of the three-dimensional geometric model, and the roughness coefficient is given according to the actual situation.

[0017] Based on the theoretical basis of the Euler-Lagrangian Circulation (ELCIRC) model, a three-dimensional hydrodynamic control equation group suitable for ship locks in estuary areas is established. The ELCIRC model is a three-dimensional fluid mechanics numerical model that uses the finite volume method and semi-implicit time integration format to solve the Reynolds-averaged Navier-Stokes equations and continuity equations for incompressible fluids. Based on the ELCIRC model and combined with the characteristics of ship locks in estuary areas, this application establishes a three-dimensional hydrodynamic control equation group containing the following equations: the continuity equation, which describes the law of conservation of fluid mass and controls the mass balance of the flow field; the momentum equation, including the horizontal momentum equation and the vertical momentum equation, which respectively describe the motion laws of the fluid in the horizontal and vertical directions and control the momentum balance of the flow field; the turbulence equation, which uses the turbulent kinetic energy-dissipation rate (k-ε) two-equation model to describe the turbulent characteristics of the fluid and close the Reynolds stress term in the momentum equation.

[0018] When establishing the three-dimensional hydrodynamic control equations, the topographic characteristics and water flow characteristics of the ship lock in the estuary area were taken into account, and the bottom friction coefficient was introduced to describe the influence of riverbed roughness on water flow. The bottom friction coefficient reflects the interaction between the riverbed and the water flow, and is a key parameter affecting the simulation accuracy of the hydrodynamic process of the ship lock in the estuary area.

[0019] The constructed three-dimensional geometric model of the ship lock in the estuary area, the boundary conditions and initial conditions set, and the three-dimensional hydrodynamic control equations including the bottom friction coefficient are input into the ELCIRC software and numerically solved using the finite volume method and semi-implicit time integration format. In the numerical solution process, the three-dimensional geometric model is discretized into a large number of control volumes through meshing, and the three-dimensional hydrodynamic control equations are applied to each control volume to form a large sparse algebraic equation system. The algebraic equations are solved using an efficient linear equation solver, such as the GMRES method or the BiCGSTAB method, to obtain physical quantities such as flow velocity, water level and pressure at each grid node.

[0020] Through the numerical solution of ELCIRC software, the three-dimensional water flow field distribution of the estuary ship lock under given boundary conditions and initial conditions was obtained, and a numerical model that can reflect the hydrodynamic characteristics of the estuary ship lock was established, that is, the hydrodynamic model of the estuary ship lock. Among them, the three-dimensional hydrodynamic control equation group is a mathematical model that describes the internal water flow movement of the estuary ship lock, which consists of continuity equations, momentum equations and turbulence equations. These equations are based on the basic laws of fluid mechanics, taking into account the conservation of mass, momentum conservation and turbulence characteristics of the water flow, and can accurately describe the distribution and change laws of physical quantities such as velocity, pressure, turbulence and so on of the water flow in three-dimensional space. In this application, according to the theoretical framework of the Euler-Lagrange circulation model, a three-dimensional hydrodynamic control equation group including continuity equations, momentum equations and turbulence equations was established. By introducing the bottom friction coefficient into the equation group, the influence of riverbed roughness on water flow movement is considered. The control equation group is solved by numerical methods, and a three-dimensional hydrodynamic model reflecting the water flow movement characteristics of the estuary ship lock is obtained.

[0021] Furthermore, the continuity equation describes the law of conservation of fluid mass and represents the mass balance relationship of water flow in space. The continuity equation is: , where x, y, z are the horizontal, longitudinal and vertical coordinates, m; is the velocity component along the x, y, and z directions, ms -1 ; t is time, s.

[0022] Furthermore, the momentum equation describes the law of conservation of fluid momentum, which expresses the law of motion of water flow under the action of external forces. The momentum equation is;

[0023]

[0024]

[0025] Among them, x, y, z are the horizontal, longitudinal and vertical coordinates, respectively, m; is the velocity component along the x, y, and z directions, ms -1 ; is the free surface elevation, m; t is the time, s; is the mean still water surface elevation, m; is the Coriolis force coefficient, s -1 ; is the acceleration due to gravity, 9.8ms -2 ; is the effective elasticity factor of the earth, ; is the tidal potential, m; is the atmospheric pressure on the free surface, Nm -2 ; is water density, default reference value ; is the vertical eddy viscosity coefficient, m 2 s -1 ; are the momentum horizontal viscosity term and diffusion term, m 2 s -1 .

[0026] Furthermore, the turbulence equation describes the turbulent characteristics of water flow and reflects the influence of turbulent pulsation on the mean flow field. The turbulence equation consists of two main transport control equations, which are used to control the turbulent kinetic energy. and the universal length scale variable : , ,in, is the turbulent kinetic energy, m 2 s -2 ; is the universal length scale variable, m; , They are , Vertical turbulent diffusion rate, m 2 s -1 ; is the vertical eddy viscosity coefficient, m 2 s -1 ; is the vertical turbulent diffusion coefficient, m 2 s -1 ; , , is a specific model constant, dimensionless; M and N are the shear and buoyancy frequencies, s -1 ; is the dissipation rate, m 2 s -3； surface Indicates constant.

[0027] Furthermore, the optimal bottom friction coefficient is obtained , including: the bottom friction coefficient As a state variable, such as the Monte Carlo method or the Latin hypercube sampling method, n sets of different bottom friction coefficient samples are generated within a given range. , forming the initial set. These samples cover the possible value space of the bottom friction coefficient and provide the initial state set for the subsequent ensemble Kalman filter.

[0028] According to n groups of different bottom friction coefficients , for each group of bottom friction coefficient samples , input it into the constructed estuary lock hydrodynamic model, use ELCIRC software to perform numerical simulation, and calculate the corresponding simulated flow velocity . Set n groups of simulated flow rates The prediction result set is composed, which represents the prediction result of the hydrodynamic model on the flow velocity of the ship lock in the estuary area under the current bottom friction coefficient set.

[0029] According to the set of prediction results, the ensemble Kalman filter algorithm is used to calculate the prior error covariance matrix of the prediction results. The prior error covariance matrix reflects the uncertainty of the prediction results and is an important basis for constructing the Kalman gain matrix.

[0030] Obtain measured flow velocity data for ship locks in estuary areas , and use it as the observation value of the ensemble Kalman filter algorithm. Using the measured flow velocity data and the set of prediction results, the Kalman gain matrix K is calculated through the ensemble Kalman filter algorithm; the Kalman gain matrix K is used to correct the set of prediction results to obtain the corrected bottom friction coefficient set;

[0031] According to the corrected parameter set and measured flow rate , calculate the posterior error covariance through the ensemble Kalman filter algorithm, and use the posterior error covariance to update the prior error covariance;

[0032] The corrected bottom friction coefficient set Perform arithmetic averaging to obtain the average value of the bottom friction coefficient .Will Input into the hydrodynamic model, use ELCIRC software to perform numerical simulation, and calculate the corresponding average simulated flow velocity .

[0033] Determine the average simulated flow rate The measured flow rate The difference Is it less than the preset accuracy threshold? ,if Less than , then As the optimal bottom friction coefficient ;if Greater than or equal to , then return to continue iterating.

[0034] Furthermore, the ensemble Kalman filter algorithm is used to calculate the prior error covariance of the prediction result set, including:

[0035] The hydrological data are used as the driving force variables of the hydrodynamic model, including the boundary condition data such as the upstream water level and downstream water level of the ship lock in the estuary area. As a parameter of the hydrodynamic model, it reflects the influence of riverbed roughness on water flow movement. Obtain n sets of simulated flow velocities as state variables to represent the prediction results of the hydrodynamic model on the flow velocity of the ship lock in the estuary area.

[0036] The hydrological data and bottom friction coefficient are randomly disturbed to generate a set of disturbance samples. The disturbance samples obey the Gaussian distribution with a mean of zero and a given variance or other appropriate probability distribution. The disturbance samples are added to the hydrological data and bottom friction coefficient respectively to obtain a set of driving force variables after disturbance. and parameter set . Where n is the number of ensemble members in the ensemble Kalman filter algorithm.

[0037] For each set of driving force variables after disturbance and parameters , input it into the constructed estuary lock hydrodynamic model, use ELCIRC software to perform numerical simulation, and calculate the corresponding simulated flow velocity . Set n groups of simulated flow rates The prediction set of state variables represents the prediction results of the hydrodynamic model on the flow velocity of the ship lock in the estuary area under the current driving force variables and parameter disturbances.

[0038] Obtain measured flow velocity data for ship locks in estuary areas , which is used as the observation value of the ensemble Kalman filter algorithm. In order to consider the influence of observation error, the measured velocity data is randomly disturbed to generate a set of disturbance samples. The disturbance samples obey the Gaussian distribution with a mean of zero and a given variance or other suitable probability distribution, reflecting the uncertainty of the observation data. The disturbance samples are added to the measured velocity data to obtain a set of disturbed observation values.

[0039] Using a set of observations and a set of predictions for state variables , the prior error covariance matrix of the prediction result set is calculated by the ensemble Kalman filter algorithm . Prior error covariance matrix It reflects the uncertainty of the prediction result set and is a key quantity in the ensemble Kalman filter algorithm. It comprehensively considers the disturbance effects of hydrological data, bottom friction coefficient and observation data, and quantifies the statistical characteristics of the error between the model prediction results and the actual state. The size of reflects the credibility of the prediction results, the diagonal elements represent the variance of each state variable, and the off-diagonal elements represent the covariance between different state variables.

[0040] Furthermore, collecting hydrological data includes: using radar water level gauges to collect upstream water level data and downstream water level data of ship locks in the estuary area; using radar flow velocity sets to collect measured flow velocities of ship locks in the estuary area; and using the collected upstream water level data, downstream water level data and measured flow velocity as hydrological data.

[0041] Furthermore, it also includes: obtaining the designed maximum water level and the minimum navigation water level upstream and downstream of the ship lock in the estuary area; the designed maximum water level refers to the upstream and downstream water levels of the ship lock at the design flood level, and the minimum navigation water level refers to the minimum upstream and downstream water levels that meet the navigation requirements of the ship lock during the dry season.

[0042] According to the designed maximum water level and minimum navigation water level, set up multiple different upstream and downstream water level combination schemes and establish a water level combination data set;

[0043] According to the optimal bottom friction coefficient , using the hydrodynamic model to simulate the hydrodynamic process of each water level combination scheme in the water level combination data set, and obtain the simulated flow velocity data under each water level combination scheme;

[0044] Extract the upstream and downstream water level difference data and the corresponding simulated flow velocity data of each water level combination scheme from the simulation results, and construct a water level difference-flow velocity data set;

[0045] The water level difference-flow velocity data set is fitted to obtain the relationship curve between different water level differences and flow velocities. Commonly used data fitting methods include polynomial fitting, spline fitting, Gaussian process regression, etc. By selecting the appropriate fitting method and fitting order, a fitting curve that can accurately describe the relationship between water level difference and flow velocity is obtained. This curve reflects the relationship between the optimal bottom friction coefficient and the water level difference. Under the conditions of , the average flow velocity variation law corresponding to different upstream and downstream water level differences in the estuary area ship lock.

[0046] By using the optimal bottom friction coefficient, water level combination scheme and hydrodynamic model simulation, a water level difference-flow velocity relationship curve reflecting the flow characteristics inside the ship lock in the estuary area was obtained. This curve comprehensively considers the design water level range of the ship lock and the influence of the bottom friction coefficient.

[0047] Another aspect of the embodiments of the present specification also provides a hydrodynamic simulation system for estuary ship locks, including a data acquisition module for acquiring estuary ship lock data and hydrological data; a model construction module for constructing a three-dimensional geometric model of the estuary ship lock based on the estuary ship lock data, and setting boundary conditions and initial conditions of the three-dimensional geometric model based on the hydrological data, as well as constructing a three-dimensional hydrodynamic control equation group of the estuary ship lock including the bottom friction coefficient based on the Euler-Lagrange circulation model, and numerically solving the equations using the ELCIRC software to obtain the hydrodynamic model of the estuary ship lock; a data assimilation module for using the bottom friction coefficient as a state variable, generating n groups of different bottom friction coefficients through random sampling, and using the hydrodynamic model to calculate the corresponding n groups of simulated flow velocities to form prediction results. Set, use the ensemble Kalman filter algorithm to calculate the prior error covariance of the prediction result set; obtain the measured flow rate data to correct the prediction result set; use the ensemble Kalman filter algorithm to calculate the posterior error covariance to update the prior error covariance and obtain the optimal bottom friction coefficient; the hydrodynamic simulation module, according to the optimal bottom friction coefficient, uses the hydrodynamic model to simulate the hydrodynamic process of the estuary ship lock, and obtains the water flow field change process of the estuary ship lock; the flow velocity prediction module obtains the designed maximum water level and minimum navigation water level upstream and downstream of the estuary ship lock, uses the hydrodynamic model to calculate the simulated flow velocity under different water level combinations, and according to the upstream and downstream water level difference data under different water level combinations and the corresponding simulated flow velocity, fits the relationship curve between different water level differences and flow velocities.

[0048] 3. Beneficial effects

[0049] Compared with the prior art, the advantages of this application are:

[0050] (1) In view of the complexity and variability of the estuary hydrodynamic environment, the ensemble Kalman filter algorithm is innovatively introduced to dynamically optimize the key parameter bottom friction coefficient. The traditional method determines the bottom friction coefficient through empirical selection or repeated debugging, which is difficult to adapt to the dynamic changes of the ship locks in the estuary area. This application uses the bottom friction coefficient as the state variable to be optimized, and dynamically trains it through the ensemble Kalman filter algorithm. It randomly generates multiple groups of bottom friction coefficient values ​​and substitutes them into the hydrodynamic model to calculate the corresponding simulated flow rate, and compares them with the measured flow rate. Through the iterative update of the prior error covariance and the posterior error covariance, the bottom friction coefficient is continuously corrected to finally obtain the optimal value. This method overcomes the shortcomings of the traditional method, improves the calculation efficiency, enhances the simulation accuracy, and realizes the dynamic optimization of the bottom friction coefficient.

[0051] (2) When constructing a three-dimensional hydrodynamic model, this application established a complete set of three-dimensional hydrodynamic control equations including continuity equations, momentum equations and turbulence equations based on the Euler-Lagrangian circulation model. The turbulence equation consists of two main transport control equations: turbulent kinetic energy and universal length scale variables, and takes into account the influence of turbulent characteristics on water flow motion. The momentum equation comprehensively considers the influence of multiple force factors such as Coriolis force, gravity, tidal potential, atmospheric pressure, and the vertical eddy viscosity coefficient. Through a high-precision mathematical model, the complex water flow movement laws of the ship lock in the estuary area are fully characterized, providing a guarantee for the reliability of the simulation results.

[0052] (3) Using the optimal bottom friction coefficient obtained by optimization, the hydrodynamic process of the ship lock in the estuary area under different upstream and downstream water level combinations was simulated through the hydrodynamic model. Taking into account the design maximum water level and the minimum navigation water level of the ship lock, a number of different water level combination schemes were set to obtain the water level difference and flow velocity data under various working conditions. On this basis, the relationship curve between the water level difference and the flow velocity in the lock chamber opening mode was established through data fitting. This curve intuitively represents the water flow characteristics of the ship lock in the estuary area under different working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A schematic diagram of the method flow of this application;

[0054] Figure 2 This is a detailed flowchart of the application method;

[0055] Figure 3 A three-dimensional topographic map of the study area in the reliability test;

[0056] Figure 4 Schematic diagram of the computational grid for the study area in the reliability test;

[0057] Figure 5 Calculate results for comparison and verification results in reliability testing. DETAILED DESCRIPTION

[0058] The present application is described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0059] The core of the data assimilation method is to consider both the observed data and the prediction model data on the basis of the Bayesian framework, and to reduce the uncertainty of the final result and improve the prediction accuracy through the organic combination of the two. These methods include the ensemble Kalman filter method, the Kalman filter method, and the extended Kalman filter method. The ensemble Kalman filter method is an extension of the Kalman filter data assimilation method. Compared with the traditional Kalman filter method, it can be directly applied to nonlinear models and does not require the direct definition of model errors. Therefore, this method couples the ELCIEC three-dimensional hydrodynamic model with the ensemble Kalman filter method to build a mathematical model, so that it can more accurately combine the measured data to dynamically correct the hydrodynamic process in a complex environment, thereby achieving the purpose of accurate simulation.

[0060] This application proposes a method for simulating hydrodynamic processes of ship locks in estuary areas based on data assimilation, aiming to solve the problem of accurately simulating complex hydrodynamic environments in the absence of hydrological data.

[0061] like Figure 1 and Figure 2 As shown in the figure, the method first collects the hydraulic structure data and river channel data of the study area, including the structural layout, size parameters and river channel topography of the ship lock. These data lay the foundation for the subsequent construction of the three-dimensional geometric model. Next, radar equipment is used to collect key hydrological data of the study area, including the upstream and downstream water levels of the ship lock and the measured flow rate. Among them, the radar water level meter is used to obtain the upstream and downstream water levels, and the radar flow meter is used to obtain the measured flow rate. The actual hydrological data obtained through field monitoring can provide an important reference for model verification and parameter optimization.

[0062] After the data preparation is completed, the ELCIRC hydrodynamic model of the study area is built using the Fortran language. ELCIRC is an open source hydrodynamic model software based on the finite element method, which has the advantages of high modularity, high computational efficiency, and good numerical stability. Through Fortran programming, the ELCIRC executable file (.exe) and model parameter file (.in) are generated to prepare for subsequent model calculations.

[0063] In order to achieve data assimilation, this application uses Python language to build a mathematical model that couples the data assimilation algorithm with the hydrodynamic model. Among them, the bottom friction coefficient Cd is introduced into the ELCIRC hydrodynamic model as a global control parameter. The bottom friction coefficient is a key parameter that affects the movement of water flow. Reasonable determination of the Cd value is crucial to improving the simulation accuracy. Traditional methods usually use a single constant Cd value, which is difficult to adapt to the complex and dynamically changing environment of the estuary area. In the absence of some hydrological data (such as upstream flow), the model is optimized by rationally utilizing existing observation data (such as upstream and downstream water levels) and combining the data assimilation algorithm, which breaks through the dependence of traditional hydrodynamic models on complete hydrological data and expands the scope of application of the model.

[0064] This application innovatively introduces the ensemble Kalman filter algorithm to optimize the training of the ELCIRC hydrodynamic model. Through the ensemble Kalman filter, the model simulation results and measured data can be considered simultaneously, the Cd parameter can be dynamically adjusted, and the difference between the simulated flow rate and the actual flow rate can be continuously reduced, thereby improving the simulation accuracy of the model. Even in the absence of some hydrological data (such as upstream flow), the model can be trained and optimized using existing observation data (such as upstream and downstream water levels) through data assimilation methods, ultimately achieving accurate simulation of the complex hydrodynamic processes of ship locks in the estuary area.

[0065] Specifically, obtain the ship lock data in the estuary area. By collecting the design drawings and completion data of the ship lock, obtain the structural layout and detailed dimensional parameters of the ship lock. These parameters include the total length of the ship lock, the effective gate length, the width of the lock chamber, the height of the gate, the elevation of the bottom of the lock, etc. At the same time, it is also necessary to collect river channel data in the study area, mainly the bottom elevation data of the river channel, which can be obtained by river channel measurement or consulting historical data. The river channel bottom elevation data is very important for constructing the three-dimensional geometric model of the ship lock in the estuary area, which directly affects the authenticity and accuracy of the model.

[0066] Collect hydrological data; In order to obtain real-time hydrological information of the study area, this application uses advanced radar equipment for data collection. The radar equipment mainly consists of three parts: radar water level meter, radar flow meter and remote telemetry terminal. The radar water level meter is installed at appropriate positions upstream and downstream of the ship lock to monitor the water level changes upstream and downstream in real time. Water level data can be collected continuously and with high precision by scanning and reflecting the water surface with radar waves. The upstream water level and downstream water level are important boundary conditions that affect the hydrodynamic process of the ship lock. Obtaining accurate water level data is crucial for model verification and parameter calibration.

[0067] The radar flow meter is installed at the lock section to monitor the flow rate of the water flow in real time. The radar flow meter uses the Doppler effect principle to measure the speed of the water flow by transmitting and receiving high-frequency radar waves. Obtaining the actual flow rate data can provide an important reference for model verification and reflect the dynamic change characteristics of the water flow in the lock.

[0068] The remote telemetry terminal is connected to the radar water level meter and radar current meter to automatically collect and transmit hydrological monitoring data. The telemetry terminal can set the collection frequency, collect water level and flow rate data at regular intervals, and send the data to the control center in real time through a wireless communication module (such as GPRS, 4G, etc.). This remote telemetry method can realize the automatic collection and transmission of hydrological data, reduce the workload of manual operations, and improve the timeliness and reliability of data collection.

[0069] The ELCIRC hydrodynamic model of the study area was built using the Fortran language to generate *.exe executable files and *.in parameter files. First, based on the acquired hydraulic structure data, river channel data, and hydrological data, the three-dimensional hydrodynamic model of the study area was built using the Fortran language under the ELCIRC hydrodynamic model framework. The specific steps are as follows: the structural layout, dimensional parameters, and riverbed elevation data of the ship lock were imported into the ELCIRC model, and a three-dimensional geometric model of the study area was constructed through meshing and interpolation methods. The degree of refinement of the geometric model can be appropriately adjusted according to research requirements and calculation conditions.

[0070] The collected hydrological data, including upstream and downstream water levels and measured flow rates, are input into the ELCIRC model as the boundary conditions and initial conditions of the model. The upstream water level is used as the upper boundary condition, the downstream water level is used as the lower boundary condition, and the measured flow rate data can be used for model verification and parameter calibration. Various physical parameters and numerical methods are set in the ELCIRC model, such as time step, spatial discretization format, turbulence mode, dry-wet judgment, etc., to ensure the computational stability and convergence of the model.

[0071] The main program and subroutines of the ELCIRC model are written in Fortran language to complete the construction and debugging of the model. The *.exe executable file is generated by compilation for the operation and calculation of the model. Various parameters involved in the model calculation, such as grid information, boundary conditions, initial conditions, physical parameters, etc., are saved in the *.in parameter file. This can achieve unified management and rapid modification of model parameters, and improve the repeatability and portability of the model.

[0072] Use Python language to build a mathematical model that couples the data assimilation algorithm with the hydrodynamic model, and convert a single parameter The value is used as the global control parameter, and the ensemble Kalman filter algorithm is used to optimize the ELCIRC hydrodynamic model. In the absence of hydrological data, the difference between the simulated flow velocity and the actual flow velocity of the ELCIRC hydrodynamic model is gradually reduced to achieve the simulation of the hydrodynamic process of the ship lock in the estuary area. The specific steps are as follows: Use Python language to write the coupling program of the data assimilation algorithm and the ELCIRC hydrodynamic model. Python language is flexible and concise, suitable for rapid development and integration of different algorithms and models.

[0073] The Ensemble Kalman Filter (EnKF) algorithm is used as the core algorithm for data assimilation. EnKF represents the probability distribution of state variables through a set and uses observation data to update the set members, thereby achieving dynamic estimation of model parameters and state variables.

[0074] The bottom friction coefficient It is introduced into the ELCIRC hydrodynamic model as a global control parameter of the EnKF algorithm. The value directly affects the resistance characteristics of water flow movement, so it should be adjusted reasonably. The value can significantly improve the simulation accuracy of the model. Specifically, ① modify the bottom friction coefficient in the *.in parameter file , use it as a state variable, run the *.exe executable file, and calculate n groups of different The simulated flow rate corresponding to the value ,② n groups of state variables And corresponding to different simulated flow rates Summarize and organize the prediction results ,③According to the prediction result collection , calculate the prior error covariance , construct the collective Kalman filter gain matrix ④ Receive the measured flow velocity through the radar remote telemetry terminal , the measured flow velocity is transmitted to the ensemble Kalman filter algorithm to correct the parameter set, and the parameter set is updated to ⑤According to the revised parameter set , calculate the corrected error covariance ,⑥Calculate the updated n groups of state variables The average , and Rewrite the *.in parameter file, run the *.exe executable file, and calculate the output corresponding to the average simulated flow velocity , ⑦ Calculate the average simulated flow velocity The measured flow rate If the difference is less than the accuracy threshold, the average state variable at this time is output and the average simulated flow rate As the optimal result; otherwise, return to step ① and continue iterative calculation; This application uses the coupling of the ensemble Kalman filter algorithm and the ELCIRC hydrodynamic model to dynamically estimate and update the model parameters using existing observation data in the absence of some hydrological data, continuously reducing the difference between the simulated flow velocity and the measured flow velocity, and finally obtains a more accurate simulation result of the hydrodynamic process of the ship lock in the estuary area. This data assimilation method fully utilizes the advantages of the model and observation data, and improves the applicability and reliability of the model.

[0075] In the ELCIRC hydrodynamic model, the hydrostatic equations under the Boussinesq approximation are used to solve the three-dimensional flow velocity and free surface elevation:

[0076] Continuity equation:

[0077]

[0078] Momentum equation:

[0079]

[0080] ;

[0081] Turbulence equations:

[0082]

[0083] Among them, x, y, z are the horizontal, longitudinal and vertical coordinates, respectively, m; is the velocity component along the x, y, and z directions, ms -1 ; is the free surface elevation, m; t is the time, s; is the mean still water surface elevation, m; is the Coriolis force coefficient, s -1 ; is the acceleration due to gravity, 9.8ms -2 ; is the effective elasticity factor of the earth, ; is the tidal potential, m; is the atmospheric pressure on the free surface, Nm -2 ; is water density, default reference value ; is the vertical eddy viscosity coefficient, m 2 s -1 ; are the momentum horizontal viscosity term and diffusion term, m 2 s -1 .

[0084] In the ensemble Kalman filter algorithm, the central idea is to continuously update the ensemble Kalman filter gain matrix and covariance matrix by continuously advancing the filter forward, deal with the inconsistency between the model and observation in a complex environment, and then update each member of the set in the parameter set, and update the state variables. Perform optimal estimation. The specific calculation process of the ensemble Kalman filter is:

[0085] ① In the disturbance control equation, the initial values ​​and parameters can be described as:

[0086]

[0087]

[0088] in, is the model driving force variable of the i-th set member at time t+1; is the model parameter at time t+1; is the model error, and its mean is 0 and its variance is Normal distribution ; It is the set of driving force variables of the model at time t+1.

[0089] ② Obtain the ensemble members of the state variables. In the hydrodynamic model, the state variables and parameters can be described by the theory of nonlinear random dynamic processes. Each ensemble member performs model prediction:

[0090]

[0091] Where X represents the state variable; is the prediction set at time t+1; is the optimal set at the previous time t; is the error of the i-th set at time t+1.

[0092] ③Perturb the observation value. Similar to the initial value perturbation, perturb the observation set:

[0093]

[0094] in, Represents the state variable The observed values ​​of the members of the set; is the observation error of the i-th set member, and conforms to the normal distribution with mean 0 and variance S , H is the observation operator.

[0095] ④ Calculate the model prior error covariance :

[0096]

[0097]

[0098] in, is the set mean of the state variables at time t+1.

[0099] ⑤Update the state variables, i.e. the optimal estimate:

[0100]

[0101]

[0102] in, is the collective Kalman filter gain matrix, which represents the weight relationship between the observed value and the model predicted value; is the optimal set at time t+1.

[0103] In actual engineering, due to various limiting factors, it is often impossible to obtain the flow conditions upstream of the ship lock in the estuary area. In order to overcome this problem, this application uses the upstream water level conditions measured by radar equipment as the upper boundary input conditions of the hydrodynamic model, replacing the traditional flow boundary conditions.

[0104] The core of this application is to couple the ELCIRC hydrodynamic model and the ensemble Kalman filter algorithm, which combines the observation data and the prediction model data to reduce the uncertainty of the simulation results caused by the lack of water level data and complex environment. Compared with the traditional hydrodynamic model, this application can simulate the dynamic change process of the flow field in the absence of hydrological data and complex environment more accurately, making up for the shortcomings of the traditional method.

[0105] In order to verify the reliability and practicality of the method proposed in this application, a specific time period of a ship lock project in an estuary area is selected as an example. The upper and lower boundaries of the model use water level conditions as input boundaries. The data assimilation-hydrodynamic coupling mathematical model proposed in this application and the constant bottom friction coefficient are used. The verification area is the double-line ship lock in the estuary area (see Figure 3 and Figure 4 ),in Figure 3 For topographic maps, Figure 4 The figure is a schematic diagram of the calculation grid. Affected by the tides in the estuary area, when the water level difference between the upstream and downstream is small, the upstream and downstream gates are opened at the same time, and the lock mode is adopted to allow ships to pass quickly. During this period, the ship lock in the estuary area is affected by the tides, the upstream and downstream water levels change dynamically, and the hydrodynamic process is complex. For a selected lock opening time period, this application conducts four groups of tests respectively. In addition to the key control parameters The other conditions are the same except for the value. The four groups of test variables are as follows: ① Based on the mathematical model of data assimilation and hydrodynamic coupling proposed in this application, The above four groups of tests can comprehensively evaluate the performance of the proposed method compared with the traditional constant The advantage of the value method. This application dynamically adjusts The traditional method uses constant value, it is difficult to adapt to the dynamically changing hydrodynamic conditions, resulting in increased uncertainty in the simulation results.

[0106] In this application, data assimilation technology is coupled with a hydrodynamic model to form a data assimilation-hydrodynamic coupling mathematical model. On the one hand, the model uses a hydrodynamic model to numerically simulate the water flow movement of the ship lock in the estuary area to obtain the predicted values ​​of physical quantities such as flow velocity and water level; on the other hand, the measured hydrological data is introduced into the model through a data assimilation algorithm to correct and optimize the model prediction results. The coupling of data assimilation and hydrodynamic models realizes the organic integration of observed data and model prediction results, and improves the accuracy and reliability of model simulation.

[0107] In the present application, the ensemble Kalman filter algorithm is used for data assimilation. The algorithm represents the probability distribution of state variables and parameters through a set, and uses the observed data to correct the set members, so as to obtain an estimation result that is closer to the true value. Specifically, an initial set is generated, that is, a set of state variables and parameters are randomly extracted from the prior distribution as set members. Each set member is predicted using the hydrodynamic model to obtain a set of prediction results. The measured hydrological data is used as the observation value to calculate the mean and covariance of the prediction result set. The a priori estimate is corrected using the posterior estimate, the state variables and parameters are updated, and new set members are obtained. Through the ensemble Kalman filter algorithm, the correction of the model prediction results by the observed data is realized, the model parameters are dynamically updated, and the difference between the simulated value and the measured value is continuously reduced, and finally a more accurate and reliable simulation result of the hydrodynamic process of the ship lock in the estuary area is obtained.

[0108] In the data assimilation-hydrodynamic coupling mathematical model, the dynamic update of model parameters is the key to improving the model prediction accuracy. Through the ensemble Kalman filter algorithm, the model parameters are incorporated into the data assimilation process as state variables. In each assimilation step, the model parameters are adjusted and optimized according to the observed data, so that the model prediction results are more consistent with the measured values. This dynamic update mechanism can adapt to the spatiotemporal variation characteristics of the hydrodynamic process of the ship lock in the estuary area, continuously correct the model parameters, and improve the applicability and reliability of the model.

[0109] The hydrodynamic process of ship locks in estuary areas is affected by many factors, such as tides, wind and waves, and sediment movement, and has significant nonlinearity and uncertainty. Traditional hydrodynamic models often find it difficult to accurately describe these complex processes, especially in the absence of hydrological data and complex environmental conditions, where the model prediction results often deviate greatly from the actual situation. The data assimilation-hydrodynamic coupling mathematical model, through the ensemble Kalman filter algorithm, makes full use of the observed data to constrain and correct the model, overcomes the shortcomings of the traditional hydrodynamic model, and improves the accuracy and reliability of the model prediction. This provides strong technical support for the refined management of ship locks in estuary areas, enabling managers to more accurately grasp the laws of water flow movement in ship locks, optimize scheduling decisions, and improve the efficiency and safety of ship lock operations.

[0110] This test verified the superiority of the data assimilation and hydrodynamic model coupling method of this application by comparing the calculation results of four different methods. Figure 5 The difference between the velocity calculated by each group of methods and the measured velocity is intuitively demonstrated. Group ① adopts the method of coupling data assimilation and hydrodynamic model proposed in this application, and dynamically adjusts the coupled mathematical model according to the measured flow velocity on site through the ensemble Kalman filter algorithm. Figure 5 It can be seen that the calculated velocity (red solid line) of group ① is basically consistent with the measured velocity (black circle), and the two are highly consistent. This shows that the proposed method can make full use of the observed data, make real-time corrections to the model parameters, and dynamically adapt to complex hydrodynamic conditions, thereby obtaining simulation results that are consistent with the actual situation.

[0111] In contrast, groups ②, ③, and ④ all use a constant bottom friction coefficient. The traditional ELCIRC hydrodynamic model calculations were performed with the values ​​of 0.05, 0.1 and 0.2 respectively. Figure 5 It can be seen that there is a large deviation between the flow velocity calculated by these three methods (blue, gray and yellow dotted lines, respectively) and the measured flow velocity. Although the calculation results corresponding to different Cd values ​​are different, it is generally difficult to accurately reflect the actual flow velocity change process. This shows that in the complex estuary lock environment, the use of a constant Cd value is difficult to adapt to the dynamically changing hydrodynamic conditions, resulting in reduced accuracy and reliability of the simulation results.

[0112] Through the above comparative analysis, it can be reasonably believed that combining data assimilation algorithm with hydrodynamic model is an effective method to effectively make up for the lack of hydrological data. When the water level condition is adopted at the upstream input boundary, the model parameters are dynamically adjusted through the ensemble Kalman filter algorithm, which realizes the accurate simulation and calibration of hydrodynamic processes in complex environments.

[0113] The proposed method makes full use of the field measured data and continuously reduces the difference between the simulated value and the measured value through data assimilation technology, so that the model can dynamically adapt to the actual hydrodynamic conditions. This method overcomes the limitations of the traditional constant parameter method and improves the applicability and accuracy of the model in complex environments.

[0114] The invention of the present application and its implementation methods are described schematically above. The description is not restrictive. Without departing from the spirit or basic features of the present application, the present application can be implemented in other specific forms. What is shown in the accompanying drawings is only one of the implementation methods of the invention of the present application. The actual structure is not limited to this. Any figure mark in the claims should not limit the claims involved. Therefore, if a person of ordinary skill in the art is inspired by it, without departing from the purpose of the present invention, a structural method and an embodiment similar to the technical solution are designed without creativity, which should all fall within the scope of protection of this patent. In addition, the word "including" does not exclude other elements or steps, and the word "one" before the element does not exclude the inclusion of "multiple" elements. The multiple elements stated in the product claim can also be implemented by one element through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any specific order.

Claims

1. A method for simulating hydrodynamics of ship locks in estuary areas, characterized in that: include: Obtain ship lock data in estuary areas; collect hydrological data; According to the ship lock data and hydrological data of the estuary area, a hydrodynamic model based on the Euler-Lagrangian circulation model is constructed. The hydrodynamic model includes the bottom friction coefficient that describes the effect of riverbed roughness on water flow movement. ,include: Constructing a three-dimensional geometric model of the estuary lock according to the estuary lock data, wherein the estuary lock data includes the lock structure layout, size parameters and river channel topography; Setting boundary conditions and initial conditions of the three-dimensional geometric model according to hydrological data, wherein the hydrological data includes upstream water level, downstream water level and measured flow velocity; According to the Euler-Lagrange circulation model, a three-dimensional hydrodynamic control equation group of the ship lock in the estuary area is established, wherein the three-dimensional hydrodynamic control equation group includes a continuity equation, a momentum equation and a turbulence equation; Setting the bottom friction coefficient in the three-dimensional hydrodynamic control equations ; According to the three-dimensional geometric model of the estuary lock, boundary conditions, initial conditions and bottom friction coefficient The three-dimensional hydrodynamic control equations are solved to obtain the hydrodynamic model of the ship lock in the estuary area. Using the hydrodynamic model, the simulated flow velocity of the ship lock in the estuary area is obtained through numerical solution; The ensemble Kalman filter algorithm is used to compare the simulated flow velocity with the measured flow velocity and to determine the bottom friction coefficient. Perform optimization training to obtain the optimal bottom friction coefficient ,include: The bottom friction coefficient As a state variable, n groups of different bottom friction coefficients are generated by random sampling. ; According to n groups of different bottom friction coefficients , using the hydrodynamic model to calculate the corresponding n groups of simulated flow velocities to form a set of prediction results; The ensemble Kalman filter algorithm is used to calculate the prior error covariance of the prediction result set; Obtaining the measured flow velocity at the ship lock in the estuary area As input, the set of prediction results is corrected using the ensemble Kalman filter algorithm to obtain a corrected parameter set; According to the corrected parameter set and measured flow rate , calculate the posterior error covariance through the ensemble Kalman filter algorithm, and use the posterior error covariance to update the prior error covariance; Calculate the bottom friction coefficient based on the corrected parameter set The average , and the corresponding average simulated flow velocity is calculated using the hydrodynamic model ; Determine the average simulated flow rate The measured flow rate The difference Is it less than the preset accuracy threshold? ,if Less than , then As the optimal bottom friction coefficient ;if Greater than or equal to , then return to continue iteration; The optimal bottom friction coefficient As input, the hydrodynamic model is used to simulate the hydrodynamic process of the ship lock in the estuary area and obtain the change process of the water flow field of the ship lock in the estuary area.

2. The method for simulating hydrodynamics of ship locks in estuary areas according to claim 1, characterized in that: The continuity equation is: in, are the horizontal, longitudinal and vertical coordinates respectively; For along Directional velocity component; For time.

3. The method for simulating hydrodynamics of ship locks in estuary areas according to claim 2, characterized in that: The momentum equation is; in, is the free surface elevation; For time; is the mean still water surface elevation; is the Coriolis force coefficient; is the acceleration due to gravity; is the effective elasticity factor of the earth; is the tidal potential; is the atmospheric pressure on the free surface; is the water density; is the vertical eddy viscosity coefficient; are the momentum horizontal viscosity term and diffusion term respectively.

4. The method for simulating hydrodynamics of ship locks in estuary areas according to claim 3 is characterized by: Turbulence equations: in, is the turbulent kinetic energy; is the universal length scale variable; , They are , The vertical turbulent diffusion rate; is the vertical eddy viscosity coefficient; is the vertical turbulent diffusion coefficient; and are the shear and buoyancy frequencies, respectively; is the dissipation rate; is a universal length scale variable The model constant, , , They correspond to three different constant values ​​respectively.

5. The method for simulating hydrodynamics of ship locks in estuary areas according to claim 2, characterized in that: The ensemble Kalman filter algorithm is used to calculate the prior error covariance of the prediction result set, including: The hydrological data is used as the driving force variable of the hydrodynamic model, and the bottom friction coefficient As the parameters of the hydrodynamic model; obtain n sets of simulated flow velocities as state variables; By randomly disturbing the hydrological data and the bottom friction coefficient , get the driving force variable set and parameter set of the hydrodynamic model; According to the driving force variable set and parameter set, the hydrodynamic model is used to make predictions to obtain the prediction set of state variables; The measured flow rate As the observation value, the observation value is disturbed to obtain the observation value set; According to the set of observation values ​​and the predicted set of state variables, the prior error covariance of the predicted result set is calculated by the ensemble Kalman filter algorithm.

6. The method for simulating hydrodynamics of ship locks in estuary areas according to claim 5, characterized in that: Collect hydrological data, including: Use radar water level gauges to collect water level data upstream and downstream of ship locks in estuary areas; Using radar velocity collection to collect measured velocity at ship locks in estuary areas; The collected upstream water level data, downstream water level data and measured flow velocity are used as hydrological data.

7. The method for simulating hydrodynamics of ship locks in estuary areas according to any one of claims 1 to 6, characterized in that: Also includes: Obtain the designed maximum water level and minimum navigation water level upstream and downstream of the ship lock in the estuary area; According to the designed maximum water level and minimum navigation water level, set up multiple different upstream and downstream water level combination schemes and establish a water level combination data set; According to the optimal bottom friction coefficient , using the hydrodynamic model to simulate the hydrodynamic process of each water level combination scheme in the water level combination data set, and obtain the simulated flow velocity data under each water level combination scheme; Extract the upstream and downstream water level difference data and the corresponding simulated flow velocity data of each water level combination scheme from the simulation results, and construct a water level difference-flow velocity data set; The water level difference-flow velocity data set is fitted to obtain the relationship curve between different water level differences and flow velocities.

8. A hydrodynamic simulation system for ship locks in an estuary area, characterized in that: include: Data acquisition module, to obtain ship lock data and hydrological data in the estuary area; Model building module, which builds a three-dimensional geometric model of the estuary ship lock according to the estuary ship lock data, sets the boundary conditions and initial conditions of the three-dimensional geometric model according to the hydrological data, and builds a three-dimensional hydrodynamic control equation group of the estuary ship lock including the bottom friction coefficient based on the Euler-Lagrangian circulation model, and uses the ELCIRC software to perform numerical solutions to obtain the hydrodynamic model of the estuary ship lock; The data assimilation module uses the bottom friction coefficient as a state variable, generates n groups of different bottom friction coefficients through random sampling, and uses the hydrodynamic model to calculate the corresponding n groups of simulated flow velocities to form a set of prediction results. The ensemble Kalman filter algorithm is used to calculate the prior error covariance of the prediction result set. Obtain measured flow velocity data to correct the prediction result set; use the ensemble Kalman filter algorithm to calculate the posterior error covariance to update the prior error covariance and obtain the optimal bottom friction coefficient; The hydrodynamic simulation module uses the hydrodynamic model to simulate the hydrodynamic process of the ship lock in the estuary area according to the optimal bottom friction coefficient, and obtains the flow field change process of the ship lock in the estuary area; The flow velocity prediction module obtains the designed maximum water level and minimum navigation water level upstream and downstream of the ship lock in the estuary area, calculates the simulated flow velocity under different water level combinations using the hydrodynamic model, and fits the relationship curve between different water level differences and flow velocities based on the upstream and downstream water level difference data and the corresponding simulated flow velocities under different water level combinations; Construct a hydrodynamic model based on the Euler-Lagrangian circulation model, including: Constructing a three-dimensional geometric model of the estuary lock according to the estuary lock data, wherein the estuary lock data includes the lock structure layout, size parameters and river channel topography; Setting boundary conditions and initial conditions of the three-dimensional geometric model according to hydrological data, wherein the hydrological data includes upstream water level, downstream water level and measured flow velocity; According to the Euler-Lagrange circulation model, a three-dimensional hydrodynamic control equation group of the ship lock in the estuary area is established, wherein the three-dimensional hydrodynamic control equation group includes a continuity equation, a momentum equation and a turbulence equation; Setting the bottom friction coefficient in the three-dimensional hydrodynamic control equations ; According to the three-dimensional geometric model of the estuary lock, boundary conditions, initial conditions and bottom friction coefficient The three-dimensional hydrodynamic control equations are solved to obtain the hydrodynamic model of the ship lock in the estuary area. Get the optimal bottom friction coefficient ,include: The bottom friction coefficient As a state variable, n groups of different bottom friction coefficients are generated by random sampling. ; According to n groups of different bottom friction coefficients , using the hydrodynamic model to calculate the corresponding n groups of simulated flow velocities to form a set of prediction results; The ensemble Kalman filter algorithm is used to calculate the prior error covariance of the prediction result set; Obtaining the measured flow velocity at the ship lock in the estuary area As input, the set of prediction results is corrected using the ensemble Kalman filter algorithm to obtain a corrected parameter set; According to the corrected parameter set and measured flow rate , calculate the posterior error covariance through the ensemble Kalman filter algorithm, and use the posterior error covariance to update the prior error covariance; Calculate the bottom friction coefficient based on the corrected parameter set The average , and the corresponding average simulated flow velocity is calculated using the hydrodynamic model ; Determine the average simulated flow rate The measured flow rate The difference Is it less than the preset accuracy threshold? ,if Less than , then As the optimal bottom friction coefficient ;if Greater than or equal to , then return to continue iterating.

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