Ocean dynamic simulation result generation method for target sea area

Abstract

The embodiment of the invention provides an ocean dynamic simulation result generation method for a target sea area. The ocean dynamic simulation result generation method comprises the following steps: collecting sea area information for the target sea area; constructing a physical information neural network; determining a neural network output function for representing the water level and the flow velocity through the physical information neural network based on the sea area information; constructing a two-dimensional plane two-dimensional hydrodynamic model based on the sea area information and the neural network output function; constructing a residual function through the two-dimensional plane two-dimensional hydrodynamic model; and determining a target weight, adjusting the residual function based on the target weight, and generating an ocean dynamic simulation result for the target sea area, thereby improving the accuracy and efficiency of the ocean dynamic simulation result for the target sea area.

Description

Technical Field

[0001] The present invention relates to the technical field of generating ocean dynamics simulation results for a target sea area, and in particular to a method for generating ocean dynamics simulation results for a target sea area, a device for generating ocean dynamics simulation results for a target sea area, an electronic device and a computer-readable storage medium. Background Art

[0002] The development and construction of offshore wind farms are developing rapidly. In order to rationally and orderly develop and utilize them, manage offshore resources, and promote the comprehensive development and utilization of offshore wind farm projects, it is necessary to carry out in-depth numerical simulation research on the sea areas where wind farms are located, and construct mathematical models suitable for the characteristics of the project area to provide a basis for the research, design and management of offshore wind farm projects. However, at this stage, the design, construction and operation of offshore wind farms mostly use the simulation results of classical physical models. Due to the complex and changeable environmental factors such as offshore hydrology, meteorology and geology, the amount of data is small, and the existing hydrodynamic simulation results are of low accuracy. It is impossible to fully describe the complex hydrodynamic characteristics under the influence of offshore wind power and other marine engineering structures and the real motion state of systems such as offshore wind turbines.

[0003] Therefore, how to improve the accuracy of ocean dynamics simulation is a technical problem that technicians in this field need to overcome. Summary of the invention

[0004] The embodiment of the present invention provides a method, device, electronic device and computer-readable storage medium for generating ocean dynamics simulation results for a target sea area, so as to solve the problem of how to generate ocean dynamics simulation results for a target sea area.

[0005] The embodiment of the present invention discloses a method for generating ocean dynamics simulation results for a target sea area, which may include:

[0006] Collecting sea area information for the target sea area;

[0007] Construct physical information neural network;

[0008] Determining a neural network output function for characterizing water level and flow velocity through the physical information neural network based on the sea area information;

[0009] Constructing a two-dimensional plane and two-dimensional hydrodynamic model based on the sea area information and the neural network output function;

[0010] constructing a residual function through the two-dimensional plane two-dimensional hydrodynamic model;

[0011] A target weight is determined, and the residual function is adjusted based on the target weight, and an ocean dynamics simulation result for the target sea area is generated.

[0012] Optionally, the sea area information may include average flow rate information, sea area boundary information, water level information and total area information, and the average flow rate information includes flow rate information.

[0013] Optionally, the step of determining a neural network output function for characterizing water level and flow velocity through the physical information neural network based on the sea area information may include:

[0014] Determining lateral spatial position information and vertical spatial position information for the target sea area according to the sea area boundary information and the total area information;

[0015] Determining initial weights and detection time of an activation function for the physical information neural network;

[0016] The initial weight, the detection time, the average flow velocity information, the lateral spatial position information and the vertical spatial position information are input into Formula 1 to determine the neural network output function, and Formula 1 is:

[0017] η(x,y,t)≈η s (x,y,t;θ u )

[0018]

[0019] Wherein, x is the horizontal spatial position information, y is the vertical spatial position information, t is the detection time, θ is the initial weight, η is the water level information, and η s is the approximate value of the water level information, u is the flow rate information, is the average flow velocity information for the lateral spatial position information, is an approximate value of the average flow velocity information for the lateral spatial position information, is the average flow velocity information for the vertical spatial position information, is an approximate value of the average flow velocity information for the vertical spatial position information.

[0020] Optionally, the step of constructing a two-dimensional plane two-dimensional hydrodynamic model based on the sea area information and the neural network output function may include:

[0021] Obtaining still water depth information, total water depth information, gravity acceleration information, rotation acceleration information, Scheher coefficient, Coriolis force parameter and Manning coefficient for the target sea area;

[0022] Determining a horizontal eddy viscosity coefficient function for the lateral spatial position information and the vertical spatial position information;

[0023] The static water depth information, the total water depth information, the gravity acceleration information, the rotation acceleration information, the Scheher coefficient, the Coriolis force parameter, the Manning coefficient, the horizontal eddy viscosity coefficient function, the detection time, the vertical spatial position information, the lateral spatial position information and the average flow velocity information are input into Formula 2 to determine the two-dimensional plane two-dimensional hydrodynamic model. Formula 2 is:

[0024]

[0025] Wherein, x is the lateral spatial position information, y is the vertical spatial position information, t is the detection time, η is the water level information, h is the static water depth information, H is the total water depth information, and g is the gravity acceleration information. is the average flow velocity information for the lateral spatial position information, is the average flow velocity information for the vertical spatial position information, is the autobiographical acceleration information for the lateral spatial position information, f is the Coriolis force parameter, is the autobiographical acceleration information for the vertical spatial position information, C is the Xie Cai coefficient, n is the Manning coefficient, ε x is the horizontal eddy viscosity coefficient function for the lateral spatial position information, ε y is the horizontal eddy viscosity coefficient function for the lateral spatial position information.

[0026] Optionally, the step of constructing a residual function through the two-dimensional plane two-dimensional hydrodynamic model may include:

[0027] The lateral spatial position information, the vertical spatial position information, the detection time, the approximate value of the water level information, the approximate value of the average flow velocity information for the lateral spatial position information, the approximate value of the average flow velocity information for the vertical spatial position information, the Coriolis force parameter, the static water depth information, the total water depth information, the gravitational acceleration information, the horizontal eddy viscosity coefficient function for the lateral spatial position information, the horizontal eddy viscosity coefficient function for the vertical spatial position information and the Xie Cai coefficient are input into Formula 3 to construct the residual function. Formula 3 is:

[0028]

[0029] e=e1(θ)+e2(θ)+e3(θ)

[0030] Wherein, e, e1, e2 and e3 are the residual functions, x is the lateral spatial position information, y is the vertical spatial position information, t is the detection time, η sis the approximate value of the water level information, h is the static water depth information, H is the total water depth information, g is the gravity acceleration information, is an approximate value of the average flow velocity information for the lateral spatial position information, is the approximate value of the average flow velocity information for the vertical spatial position information, f is the Coriolis force parameter, C is the Scheide coefficient, ε x is the horizontal eddy viscosity coefficient function for the lateral spatial position information, ε y is the horizontal eddy viscosity coefficient function for the vertical spatial position information.

[0031] Optionally, the step of determining the target weight may include:

[0032] Determine the learning rate and number of iterations;

[0033] The learning rate, the residual function and the number of iterations are input into Formula 4 to determine the target weight, which is:

[0034]

[0035] Wherein, θ is the initial weight, n is the number of iterations, α is the learning rate, e is the residual function, θ n+1 is the target weight.

[0036] Optionally, the physical information neural network may include two hidden layers.

[0037] The embodiment of the present invention further discloses a device for generating ocean dynamics simulation results for a target sea area, which may include:

[0038] A sea area information collection module, used to collect sea area information for the target sea area;

[0039] Physical information neural network building module, used to build physical information neural network;

[0040] A neural network output function determination module, used to determine a neural network output function for characterizing water level and flow velocity through the physical information neural network based on the sea area information;

[0041] A two-dimensional plane two-dimensional hydrodynamic model output module, used for constructing a two-dimensional plane two-dimensional hydrodynamic model based on the sea area information and the neural network output function;

[0042] A residual function construction module, used for constructing a residual function through the two-dimensional plane two-dimensional hydrodynamic model;

[0043] The target weight determination module is used to determine the target weight, adjust the residual function based on the target weight, and generate an ocean dynamics simulation result for the target sea area.

[0044] The embodiment of the present invention further discloses an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0045] The memory is used to store computer programs;

[0046] The processor is used to implement the method described in the embodiment of the present invention when executing the program stored in the memory.

[0047] The embodiment of the present invention further discloses a computer-readable storage medium having instructions stored thereon, which, when executed by one or more processors, enables the processors to execute the method described in the embodiment of the present invention.

[0048] The embodiments of the present invention include the following advantages:

[0049] The embodiment of the present invention collects sea area information for the target sea area; constructs a physical information neural network; determines a neural network output function for characterizing water level and flow velocity through the physical information neural network based on the sea area information; constructs a two-dimensional plane two-dimensional hydrodynamic model based on the sea area information and the neural network output function; constructs a residual function through the two-dimensional plane two-dimensional hydrodynamic model; determines a target weight, and adjusts the residual function based on the target weight, and generates an ocean dynamics simulation result for the target sea area, thereby achieving improved accuracy and efficiency of the ocean dynamics simulation result for the target sea area. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow chart of the steps of a method for generating ocean dynamics simulation results for a target sea area provided in an embodiment of the present invention;

[0051] Figure 2 is a schematic diagram of an ocean hydrodynamic process provided in an embodiment of the present invention;

[0052] Figure 3 It is a structural block diagram of a device for generating ocean dynamics simulation results for a target sea area provided in an embodiment of the present invention;

[0053] Figure 4 is a hardware structure block diagram of an electronic device provided in each embodiment of the present invention;

[0054] Figure 5 is a schematic diagram of a computer-readable medium provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0056] In order to enable those skilled in the art to better understand the embodiments of the present invention, the technical terms involved in the embodiments of the present invention are explained below.

[0057] PINNs: Physics-Informed Neural Networks, also known as physical information neural networks, is a new machine learning method that combines physical laws with neural networks. It aims to address the limitations of traditional data-based machine learning methods in physical modeling.

[0058] Ocean dynamics simulation is a method of numerically simulating various physical processes in the ocean (such as waves, tides, ocean currents, etc.) using mathematical models and computer technology. Through simulation, we can have a deeper understanding of the movement of the ocean and predict changes in the marine environment, thereby providing a scientific basis for fields such as marine engineering, fisheries, and navigation. Based on the simulation of ocean dynamics, we can predict changes in the marine environment, evaluate the impact of marine engineering, optimize route design, and study marine ecosystems. However, the accuracy of the simulation results of the classical physical model at this stage is often difficult to be satisfactory when facing a complex and changeable marine environment. The acquisition cost of marine hydrological, meteorological, and geological data is high, and the amount of data is relatively small, which limits the accuracy of the model. In addition, the impact of marine engineering structures such as offshore wind farms on the hydrodynamic environment is complex and diverse, and it is difficult to fully describe the existing simulation methods. The embodiment of the present invention provides a method for generating ocean dynamics simulation results for a target sea area, which combines physical information neural networks and two-dimensional plane two-dimensional hydrodynamic models. Using real-time monitoring data, the hydrodynamic process under the conditions of the target sea area can be quickly obtained to improve the accuracy and efficiency of the ocean dynamics simulation results for the target sea area.

[0059] Reference Figure 1 , shows a flow chart of the steps of a method for generating ocean dynamics simulation results for a target sea area provided in an embodiment of the present invention, which may specifically include the following steps:

[0060] Step 101, collecting sea area information for the target sea area;

[0061] Step 102, constructing a physical information neural network;

[0062] Step 103, determining a neural network output function for characterizing water level and flow velocity through the physical information neural network based on the sea area information;

[0063] Step 104, constructing a two-dimensional plane two-dimensional hydrodynamic model based on the sea area information and the neural network output function;

[0064] Step 105, constructing a residual function through the two-dimensional plane two-dimensional hydrodynamic model;

[0065] Step 106, determining a target weight, adjusting the residual function based on the target weight, and generating an ocean dynamics simulation result for the target sea area.

[0066] In practical applications, the target sea area in the embodiments of the present invention may be a sea area where ocean dynamics simulation needs to be performed, for example, the sea area where an offshore wind farm is developed and constructed. In addition, the target sea area may also be a sea area with complex terrain, a sea area affected by human activities, a sea area with frequent extreme weather, an ecologically sensitive sea area, or a sea area with scarce data, etc., where ocean dynamics simulation needs to be performed.

[0067] In a specific implementation, the embodiment of the present invention can collect sea area information for a target sea area; construct a physical information neural network; determine a neural network output function for characterizing water level and flow velocity through the physical information neural network based on the sea area information; construct a two-dimensional plane two-dimensional hydrodynamic model based on the sea area information and the neural network output function; construct a residual function through the two-dimensional plane two-dimensional hydrodynamic model; determine a target weight, and adjust the residual function based on the target weight, and generate an ocean dynamic simulation result for the target sea area. Exemplarily, sea area information for the target sea area can be collected, for example, terrain data, including but not limited to obtaining a high-precision DEM (Digital Elevation Model) of the target sea area. Model, also known as digital elevation model), may include seabed topography and coastline information; hydrological data, including but not limited to historical water level observation data, tidal data, river flow data, etc.; meteorological data, including but not limited to wind speed, wind direction, air pressure, precipitation and other meteorological data, marine environmental data, including but not limited to temperature, salinity, tidal, suspended matter concentration and other marine environmental data; human activity data, including but not limited to port operations, reclamation, river into the sea and other human activity data. Then, a physical information neural network is established. The solver established by the physical information neural network PINNs with stronger generalization ability can be selected. PINNs is built based on deep forward neural networks. The forward neural network consists of a series of nonlinear activation functions, which are finally combined into output functions. These output functions can realize the approximation of arbitrary functions in terms of nonlinear function optimization. The output function of the neural network used to characterize the water level and flow rate is determined by PINNs based on the sea area information.

[0068] Preferably, the sea area information may include average flow rate information, sea area boundary information, water level information and total area information, and the average flow rate information may include flow rate information.

[0069] Then, a two-dimensional plane two-dimensional hydrodynamic model can be constructed based on the sea area information and the output function of the neural network. The two-dimensional plane two-dimensional hydrodynamic model has a wide range of applications in hydrology, water conservancy, marine engineering and other fields. Compared with the one-dimensional model, the two-dimensional model can more accurately simulate the flow changes of water on the horizontal plane, including flow velocity distribution, water level changes, etc. It can simulate the influence of complex coastlines, islands, shoals and other terrains on water flow. It can be used for hydrodynamic simulation of large-scale sea areas or lakes, and provide support for regional-scale water environment research. At the same time, compared with the three-dimensional model, the equation group of the two-dimensional model is relatively simple, the calculation amount is small, and it is suitable for parallel computing. It can make full use of the computing resources of computer clusters and improve computing efficiency. In addition, the two-dimensional plane two-dimensional hydrodynamic model can also simulate the propagation and amplitude of tides, simulate the influence of extreme weather events such as typhoons and storm surges on water levels and flow rates, water and salt intrusion at river estuaries, sediment transport and other processes, as well as simulate the diffusion and migration of pollutants in water bodies. It is convenient to couple with other models, and the parameter calibration is simple. It can intuitively display the distribution of hydrodynamic elements such as water level and flow rate, which is convenient for analysis and understanding.

[0070] The residual function is constructed by a two-dimensional plane two-dimensional hydrodynamic model. For example, the residual function can be a ResidualFunction residual function, which can be used to construct ResNet (Residual The output of the residual network not only contains the learning features of the layer, but also contains a direct input from the previous layer. The residual network can alleviate the gradient vanishing problem. Since the gradient vanishing problem in the deep network will make the network difficult to train, the residual connection directly passes the input to the output, so that the gradient can be more easily back-propagated, thereby alleviating the gradient vanishing problem. At the same time, the residual network can accelerate the network convergence and make it easier for the network to find the optimal solution. In addition, the residual network can improve the performance of the network. The residual network has shown superior performance to the traditional network in many tasks, especially in the fields of image classification and target detection. The residual network can effectively solve the network degradation problem that occurs with the increase of network depth, and it is easier to stack together to build a deeper network without significantly increasing the difficulty of training. It helps to effectively transfer the features of the shallow network to the deep network, thereby improving the network's expression ability. In PINNs, by introducing residual connections, PINNs can better fit complex physical systems, thereby improving the prediction accuracy of the model, and can help PINNs better generalize to new data, thereby reducing the risk of overfitting, accelerating the training process of PINNs, and enabling the model to converge faster.

[0071] Furthermore, the target weights can be determined, and the residual function can be adjusted based on the target weights, and the ocean dynamic simulation results for the target sea area can be generated. In PINNs, the target weights can refer to the weight coefficients assigned to the residuals of different physical quantities (such as water level, flow rate, pressure, etc.). These weight coefficients determine the degree of influence of different physical quantities on the final loss function. By adjusting the target weights, the constraints of different physical quantities can be emphasized or weakened, so as to better capture the specific characteristics of the target sea area. Substitute the solution output by the neural network into the model and calculate the residual of the equation. The residual represents the error between the output of the neural network and the true solution. Multiply the residuals of different physical quantities by the corresponding weight coefficients, and then add these products to obtain the total loss function. Through continuous iterative optimization process, the neural network can fit the training data well and meet the physical constraints. The trained neural network is applied to new input data to predict the water level, flow rate and other information of the target sea area. When the residual converges, the output function of the neural network is a two-dimensional plane two-dimensional hydrodynamic model as the result of ocean dynamic simulation. Preferably, the prediction results can be visualized, such as drawing contour maps, vector maps, etc., to intuitively display the simulation results.

[0072] The embodiment of the present invention collects sea area information for the target sea area; constructs a physical information neural network; determines a neural network output function for characterizing water level and flow velocity through the physical information neural network based on the sea area information; constructs a two-dimensional plane two-dimensional hydrodynamic model based on the sea area information and the neural network output function; constructs a residual function through the two-dimensional plane two-dimensional hydrodynamic model; determines a target weight, and adjusts the residual function based on the target weight, and generates an ocean dynamics simulation result for the target sea area, thereby achieving improved accuracy and efficiency of the ocean dynamics simulation result for the target sea area.

[0073] Based on the above embodiment, a variant embodiment of the above embodiment is proposed. It should be noted that in order to make the description concise, only the differences from the above embodiment are described in the variant embodiment.

[0074] In an optional embodiment of the present invention, the step of determining a neural network output function for characterizing water level and flow velocity through the physical information neural network based on the sea area information comprises:

[0075] Determining lateral spatial position information and vertical spatial position information for the target sea area according to the sea area boundary information and the total area information;

[0076] Determining initial weights and detection time of an activation function for the physical information neural network;

[0077] The initial weight, the detection time, the average flow velocity information, the lateral spatial position information and the vertical spatial position information are input into Formula 1 to determine the neural network output function, and Formula 1 is:

[0078] η(x,y,t)≈η s (x,y,t;θ u )

[0079]

[0080] Wherein, x is the horizontal spatial position information, y is the vertical spatial position information, t is the detection time, θ is the initial weight, η is the water level information, and η s is the approximate value of the water level information, u is the flow rate information, is the average flow velocity information for the lateral spatial position information, is an approximate value of the average flow velocity information for the lateral spatial position information, is the average flow velocity information for the vertical spatial position information, is an approximate value of the average flow velocity information for the vertical spatial position information.

[0081] In a specific implementation, the embodiment of the present invention can determine the lateral spatial position information and vertical spatial position information for the target sea area based on the sea area boundary information and the total area information; determine the initial weight and detection time of the activation function for the physical information neural network; input the initial weight, detection time, average flow velocity information, lateral spatial position information and vertical spatial position information into the above formula 1 to determine the neural network output function. Exemplarily, η(x, y, t) can be water level information, and the water level information can refer to the actual water level, that is, determine the target value of the water level in the ocean dynamics simulation result for the target sea area, η s (x,y,t;θ u ) can be an approximation of the water level information, that is, it can be a predicted value for the water level, which is a function of the spatial coordinates (x, y), the detection time t and the initial weight θ. It may be the average flow velocity information for the lateral spatial position information, It may be the average flow velocity information for the vertical spatial position information, and It can be an actual value. The output function of the neural network is expressed by η s (x,y,t;θ u ), To approximate the actual water level and flow rate, θ u can be a learnable parameter of the neural network output function, by adjusting θ u, the output of the neural network output function can be made as similar as possible to the true value. The input of the neural network output function includes spatial coordinates (x, y), detection time t and initial weight θ. This shows that the neural network can learn the law of water level and flow velocity changing with space and time, and approximate the real water level and flow velocity through the output value generated by formula 1. By introducing spatial coordinates, time and initial weights, the spatial change, time evolution and initial conditions of the physical process are integrated into the neural network. The initial weight θ is learnable. Through training, a set of optimal weights can be found to minimize the error between the output of the neural network and the true value. The initial weights are initialized based on the average flow velocity information, lateral spatial position information, vertical spatial position information and detection time, which helps the network converge to the optimal solution faster. The neural network output function provides a basis for simulating the hydrodynamic process. By integrating physical information into the neural network and using learnable parameters, the model can effectively capture the complexity of the hydrodynamic process and provide accurate prediction results.

[0082] In an embodiment of the present invention, the lateral spatial position information and the vertical spatial position information for the target sea area are determined according to the sea area boundary information and the total area information;

[0083] Determining initial weights and detection time of an activation function for the physical information neural network;

[0084] The initial weight, the detection time, the average flow velocity information, the lateral spatial position information and the vertical spatial position information are input into Formula 1 to determine the neural network output function, and Formula 1 is:

[0085] η(x,y,t)≈η s (x,y,t;θ u )

[0086]

[0087] Wherein, x is the horizontal spatial position information, y is the vertical spatial position information, t is the detection time, θ is the initial weight, η is the water level information, and η s is the approximate value of the water level information, u is the flow rate information, is the average flow velocity information for the lateral spatial position information, is an approximate value of the average flow velocity information for the lateral spatial position information, is the average flow velocity information for the vertical spatial position information, It is an approximate value of the average flow velocity information for the vertical spatial position information, thereby further improving the accuracy of generating ocean dynamics simulation results for the target sea area.

[0088] In an optional embodiment of the present invention, the step of constructing a two-dimensional plane two-dimensional hydrodynamic model based on the sea area information and the neural network output function includes:

[0089] Obtaining still water depth information, total water depth information, gravity acceleration information, rotation acceleration information, Scheher coefficient, Coriolis force parameter and Manning coefficient for the target sea area;

[0090] Determining a horizontal eddy viscosity coefficient function for the lateral spatial position information and the vertical spatial position information;

[0091] The static water depth information, the total water depth information, the gravity acceleration information, the rotation acceleration information, the Scheher coefficient, the Coriolis force parameter, the Manning coefficient, the horizontal eddy viscosity coefficient function, the detection time, the vertical spatial position information, the lateral spatial position information and the average flow velocity information are input into Formula 2 to determine the two-dimensional plane two-dimensional hydrodynamic model. Formula 2 is:

[0092]

[0093] Wherein, x is the lateral spatial position information, y is the vertical spatial position information, t is the detection time, η is the water level information, h is the static water depth information, H is the total water depth information, and g is the gravity acceleration information. is the average flow velocity information for the lateral spatial position information, is the average flow velocity information for the vertical spatial position information, is the autobiographical acceleration information for the lateral spatial position information, f is the Coriolis force parameter, is the autobiographical acceleration information for the vertical spatial position information, C is the Xie Cai coefficient, n is the Manning coefficient, ε x is the horizontal eddy viscosity coefficient function for the lateral spatial position information, ε y is the horizontal eddy viscosity coefficient function for the lateral spatial position information.

[0094] In a specific implementation, the embodiment of the present invention can obtain the still water depth information, total water depth information, gravity acceleration information, rotation acceleration information, Xie Cai coefficient, Coriolis force parameter and Manning coefficient for the target sea area; determine the horizontal eddy viscosity coefficient function for the lateral spatial position information and the vertical spatial position information; input the still water depth information, total water depth information, gravity acceleration information, rotation acceleration information, Xie Cai coefficient, Coriolis force parameter, Manning coefficient, horizontal eddy viscosity coefficient function, detection time, vertical spatial position information, lateral spatial position information and average flow rate information into Formula 2 to determine a two-dimensional plane two-dimensional hydrodynamic model. For example, x is the lateral spatial position information, y is the vertical spatial position information, t is the detection time, η is the water level information, h is the still water depth information, H is the total water depth information, g is the gravity acceleration information, is the average velocity information for the lateral spatial position information, is the average flow velocity information for the vertical spatial position information, is the autobiographical acceleration information for the lateral spatial position information, f is the Coriolis force parameter, is the autobiographical acceleration information for the vertical spatial position information, C is the Xie Cai coefficient, n is the Manning coefficient, ε x is the horizontal eddy viscosity coefficient function for the lateral spatial position information, ε y The horizontal eddy viscosity coefficient function for the lateral spatial position information, the Coriolis force parameter can characterize the effect of the earth's rotation on the flow, the Xie Cai coefficient can be used to calculate the friction, the Manning coefficient can be used to calculate the bottom friction, and the horizontal eddy viscosity coefficient can take into account the effect of turbulence on the flow. Through the two-dimensional plane two-dimensional hydrodynamic model, strict constraints can be imposed on the output of the neural network to ensure that the prediction results of the neural network conform to the physical laws. Preferably, when training the neural network, the residual of the two-dimensional plane two-dimensional hydrodynamic model is used as part of the loss function. By minimizing the residual, the output of the neural network can better satisfy the physical equations. By introducing the physical equations as constraints, the physical consistency of the model can be significantly improved, so that the model can better capture the essence of the hydrodynamic process. Through physical parameters and physical processes, stronger physical constraints can be provided to improve the accuracy of the model. It can be applied to a wider range of hydrodynamic problems, such as tides, storm surges, rivers entering the sea, etc., so that the physical information neural network has a stronger physical basis when simulating hydrodynamic processes. By using physical equations as constraints, the accuracy and reliability of the model can be improved.

[0095] In the embodiment of the present invention, by acquiring the still water depth information, total water depth information, gravity acceleration information, rotation acceleration information, Scheher coefficient, Coriolis force parameter and Manning coefficient for the target sea area;

[0096] Determining a horizontal eddy viscosity coefficient function for the lateral spatial position information and the vertical spatial position information;

[0097] The static water depth information, the total water depth information, the gravity acceleration information, the rotation acceleration information, the Scheher coefficient, the Coriolis force parameter, the Manning coefficient, the horizontal eddy viscosity coefficient function, the detection time, the vertical spatial position information, the lateral spatial position information and the average flow velocity information are input into Formula 2 to determine the two-dimensional plane two-dimensional hydrodynamic model. Formula 2 is:

[0098]

[0099] Wherein, x is the lateral spatial position information, y is the vertical spatial position information, t is the detection time, η is the water level information, h is the static water depth information, H is the total water depth information, and g is the gravity acceleration information. is the average flow velocity information for the lateral spatial position information, is the average flow velocity information for the vertical spatial position information, is the autobiographical acceleration information for the lateral spatial position information, f is the Coriolis force parameter, is the autobiographical acceleration information for the vertical spatial position information, C is the Xie Cai coefficient, n is the Manning coefficient, ε x is the horizontal eddy viscosity coefficient function for the lateral spatial position information, ε y In order to develop a horizontal eddy viscosity coefficient function for the lateral spatial position information, more physical parameters are introduced to improve the physical consistency of the results, thereby further increasing the accuracy and effectiveness of the generation of ocean dynamics simulation results.

[0100] In an optional embodiment of the present invention, the step of constructing a residual function through the two-dimensional plane two-dimensional hydrodynamic model includes:

[0101] The lateral spatial position information, the vertical spatial position information, the detection time, the approximate value of the water level information, the approximate value of the average flow velocity information for the lateral spatial position information, the approximate value of the average flow velocity information for the vertical spatial position information, the Coriolis force parameter, the static water depth information, the total water depth information, the gravitational acceleration information, the horizontal eddy viscosity coefficient function for the lateral spatial position information, the horizontal eddy viscosity coefficient function for the lateral spatial position information and the Xie Cai coefficient are input into Formula 3 to construct the residual function. Formula 3 is:

[0102]

[0103] e=e1(θ)+e2(θ)+e3(θ)

[0104] Wherein, e, e1, e2 and e3 are the residual functions, x is the lateral spatial position information, y is the vertical spatial position information, t is the detection time, η s is the approximate value of the water level information, h is the static water depth information, H is the total water depth information, g is the gravity acceleration information, is an approximate value of the average flow velocity information for the lateral spatial position information, is the approximate value of the average flow velocity information for the vertical spatial position information, f is the Coriolis force parameter, C is the Scheide coefficient, ε x is the horizontal eddy viscosity coefficient function for the lateral spatial position information, ε y is the horizontal eddy viscosity coefficient function for the vertical spatial position information.

[0105] In a specific implementation, the embodiment of the present invention can input the lateral spatial position information, the vertical spatial position information, the detection time, the approximate value of the water level information, the approximate value of the average flow velocity information for the lateral spatial position information, the approximate value of the average flow velocity information for the vertical spatial position information, the Coriolis force parameter, the still water depth information, the total water depth information, the gravity acceleration information, the horizontal eddy viscosity coefficient function for the lateral spatial position information, the horizontal eddy viscosity coefficient function for the vertical spatial position information and the Xie Cai coefficient into Formula 3 to construct a residual function. For example, e, e1, e2 and e3 can be residual functions, x can be the lateral spatial position information, y can be the vertical spatial position information, t can be the detection time, η s can be the approximate value of the water level information, h can be the static water depth information, H can be the total water depth information, g can be the gravity acceleration information, can be an approximation of the average velocity information for the lateral spatial position information, is the approximate value of the average flow velocity information for the vertical spatial position information, f can be the Coriolis force parameter, C can be the Schertz coefficient, ε x can be the horizontal eddy viscosity coefficient function for the lateral spatial position information, ε y It can be a horizontal eddy viscosity coefficient function for vertical spatial position information. By substituting e1, e2 and e3 into the formula where e is, the total residual value is calculated, which represents the error between the numerical solution and the true solution. The purpose of the residual function is to measure the accuracy of the numerical model. By minimizing the residual, the numerical solution can be made closer to the real physical process, which represents the total error between the numerical solution and the true solution at the spatial point (x, y) and the detection time t.

[0106] In the embodiment of the present invention, the residual function is constructed by inputting the lateral spatial position information, the vertical spatial position information, the detection time, the approximate value of the water level information, the approximate value of the average flow velocity information for the lateral spatial position information, the approximate value of the average flow velocity information for the vertical spatial position information, the Coriolis force parameter, the static water depth information, the total water depth information, the gravitational acceleration information, the horizontal eddy viscosity coefficient function for the lateral spatial position information, the horizontal eddy viscosity coefficient function for the vertical spatial position information and the Xie Cai coefficient into Formula 3, and the formula 3 is:

[0107]

[0108] e=e1(θ)+e2(θ)+e3(θ)

[0109] Wherein, e, e1, e2 and e3 are the residual functions, x is the lateral spatial position information, y is the vertical spatial position information, t is the detection time, η s is the approximate value of the water level information, h is the static water depth information, H is the total water depth information, g is the gravity acceleration information, is an approximate value of the average flow velocity information for the lateral spatial position information, is the approximate value of the average flow velocity information for the vertical spatial position information, f is the Coriolis force parameter, C is the Scheide coefficient, ε x is the horizontal eddy viscosity coefficient function for the lateral spatial position information, ε y In order to obtain the horizontal eddy viscosity coefficient function for the vertical spatial position information, the residual function is used to evaluate the accuracy of the model, optimize the model parameters, and further improve the accuracy and effectiveness of the result generation.

[0110] In an optional embodiment of the present invention, the step of determining the target weight includes:

[0111] Determine the learning rate and number of iterations;

[0112] The learning rate, the residual function and the number of iterations are input into Formula 4 to determine the target weight, which is:

[0113]

[0114] Wherein, θ is the initial weight, n is the number of iterations, α is the learning rate, e is the residual function, θ n+1 is the target weight.

[0115] In a specific implementation, the embodiment of the present invention can determine the learning rate and the number of iterations; the learning rate, the residual function and the number of iterations are input into Formula 4 to determine the target weight. For example, θ can be the initial weight, n can be the number of iterations, α can be the learning rate, e can be the residual function, θ n+1 can be the target weight, and the weight formula can be updated by the gradient descent method to optimize the neural network model, θ n+1 is the target weight, which can be the weight after the n+1th iteration, θ n is the weight after the nth iteration. The α learning rate can control the step size of each update. In the process of updating parameters, if the step size is too large, the model may jump in the parameter space, resulting in failure to converge to the optimal solution or even divergence. If the step size is too small, the model will be more inclined to use the existing information and gradually approach the local optimal solution, but it may fall into the local minimum. Selecting the optimal learning rate can make the model converge to the optimal solution quickly. Through multiple experiments, the learning rate can be gradually adjusted to find the optimal value. As the training progresses, the learning rate can be gradually reduced to improve the stability of convergence. It represents the gradient of the residual function e(θ) with respect to the initial weight θ, and identifies the steepest descent direction of the loss function at the current weight point. By finding an optimal set of weights θ as the target weights, the error between the model output and the true value is minimized. Through continuous iteration, the model weights will gradually adjust in the direction of minimizing the loss function, thereby improving the performance of the model. When the residual converges, the output function of the neural network is a two-dimensional plane two-dimensional hydrodynamic process, which can be used as the ocean dynamic simulation result for the target sea area.

[0116] In the embodiment of the present invention, a learning rate and a number of iterations are determined;

[0117] The learning rate, the residual function and the number of iterations are input into Formula 4 to determine the target weight, which is:

[0118]

[0119] Wherein, θ is the initial weight, n is the number of iterations, α is the learning rate, e is the residual function, θ n+1 The target weight is used to further improve the performance of the model, optimize the model, and increase the accuracy of the ocean dynamics simulation results.

[0120] In an optional embodiment of the present invention, the physical information neural network includes two hidden layers.

[0121] In a specific implementation, the physical information neural network in an embodiment of the present invention may include two hidden layers. For example, when the physical information neural network is PINNs, the first hidden layer may be mainly responsible for extracting low-level features from the input data, such as the relationship between simple physical quantities, and may learn the relationship between basic physical quantities such as speed, pressure, and density. The second hidden layer may further extract high-level features based on the features extracted by the first layer to capture more complex physical laws and nonlinear relationships, and may learn more complex fluid phenomena such as vorticity and boundary layers. The two hidden layers provide sufficient model capacity to learn complex physical laws. Too few hidden layers may cause the model to be underfitted and unable to capture the complexity of the data; too many hidden layers may cause overfitting and poor generalization ability. The first hidden layer may learn features related to physical laws, and the second hidden layer may combine these features to form a more advanced physical representation. In this way, PINNs can better integrate physical information and data information.

[0122] In practical applications, physical information neural networks contain two hidden layers, which can work together to improve the performance of the model by extracting features, introducing nonlinearity, increasing model capacity, and fusing physical information. Reasonable design of the structure and parameters of the hidden layer is crucial to building an efficient and accurate PINNs model.

[0123] In the embodiment of the present invention, the accuracy and efficiency of the physical information neural network are further improved by making the physical information neural network include two hidden layers.

[0124] In order to enable those skilled in the art to better understand the embodiments of the present invention, the embodiments of the present invention are described below using a complete example.

[0125] refer to Figure 2 , showing a schematic diagram of an ocean hydrodynamic process provided in an embodiment of the present invention;

[0126] With the rapid development and construction of offshore wind farms, the complexity of the sea areas where they are located has brought huge challenges to engineering design and management. Traditional numerical simulation methods are often not accurate enough when dealing with the coupling of multiple factors such as waves, currents, and geology. In order to more accurately evaluate the performance of offshore wind farms in complex marine environments, it is urgent to establish a more complete numerical simulation model to determine more accurate marine dynamic simulation results. The specific process is as follows:

[0127] Collect information on the target sea area, including: average flow velocity, temperature and salinity tidal data collected in the sea area, and determine the boundaries and total area of ​​the sea area.

[0128] A solver is established using physical information neural networks (PINNs) with stronger generalization ability. PINNs are built based on deep forward neural networks. The forward neural network consists of a series of nonlinear activation functions, which are ultimately combined into output functions. These output functions can approximate any function in terms of nonlinear function optimization. Here, the neural network output functions of the unknown quantities water level, vertical flow velocity and lateral flow velocity in the two-dimensional plane two-dimensional hydrodynamic equation are constructed, as shown in the above formula 1.

[0129] based on Figure 2 The topological structure in the schematic diagram of the ocean hydrodynamic process shown establishes a two-dimensional plane two-dimensional hydrodynamic model of the sea area, and the control equation can be shown as in the above formula 2.

[0130] By adding physical constraints based on the two-dimensional hydrodynamic equations of the two-dimensional plane to the neural network, a residual term that does not depend on data can be constructed. Here, a continuous time format can be used. Using the model shown in the above formula two, a residual function can be constructed, as shown in the above formula three.

[0131] When the residual sum is closer to zero, the approximate solution of PINNs is closer to the true solution.

[0132] Then, the value of the residual can be reduced by adjusting the weight θ so that the output function of the neural network approximates the two-dimensional hydrodynamic equation of the two-dimensional plane. A gradient algorithm suitable for convex optimization can be used, and the basic form can be shown in the above formula four.

[0133] Preferably, the Adam (Adaptive Moment Estimation) algorithm can be selected. The Adam algorithm uses an adaptive learning rate to improve learning efficiency and introduces momentum to enhance the stability of the algorithm, making it more suitable for complex non-convex optimization problems. Furthermore, when local convergence problems occur, global optimization algorithms such as GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) can be used. After the residual converges, the output function of the neural network is a two-dimensional plane two-dimensional hydrodynamic process.

[0134] By introducing physical information neural networks, partial differential equations describing physical processes are integrated into the loss function of neural networks, and complex physical systems can be accurately learned and simulated with very little data. Not only can the laws of marine hydrodynamic processes be quickly extracted from limited monitoring data, but also the model prediction results can be highly consistent with the actual physical process, thus providing strong support for marine engineering, marine environmental monitoring and other fields.

[0135] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0136] Reference Figure 3 , shows a structural block diagram of a device for generating ocean dynamics simulation results for a target sea area provided in an embodiment of the present invention, which may specifically include the following modules:

[0137] A sea area information collection module 301 is used to collect sea area information for the target sea area;

[0138] A physical information neural network construction module 302 is used to construct a physical information neural network;

[0139] A neural network output function determination module 303 is used to determine a neural network output function for characterizing water level and flow velocity based on the sea area information through the physical information neural network;

[0140] A two-dimensional plane two-dimensional hydrodynamic model output module 304 is used to construct a two-dimensional plane two-dimensional hydrodynamic model based on the sea area information and the neural network output function;

[0141] A residual function construction module 305 is used to construct a residual function through the two-dimensional plane two-dimensional hydrodynamic model;

[0142] The target weight determination module 306 is used to determine the target weight, adjust the residual function based on the target weight, and generate an ocean dynamics simulation result for the target sea area.

[0143] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0144] In addition, an embodiment of the present invention further provides an electronic device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the various processes of the above-mentioned embodiment of the method for generating ocean dynamic simulation results for the target sea area are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0145] The embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, each process of the above-mentioned method for generating ocean dynamic simulation results for a target sea area is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0146] Figure 4 A schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present invention.

[0147] The electronic device 400 includes but is not limited to: a radio frequency unit 401, a network module 402, an audio output unit 403, an input unit 404, a sensor 405, a display unit 406, a user input unit 407, an interface unit 408, a memory 409, a processor 410, and a power supply 411. Those skilled in the art will appreciate that Figure 4 The electronic device structure shown in the figure does not constitute a limitation on the electronic device, and the electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. In the embodiments of the present invention, the electronic device includes but is not limited to a mobile phone, a tablet computer, a laptop computer, a PDA, a vehicle-mounted terminal, a wearable device, and a pedometer.

[0148] It should be understood that in the embodiment of the present invention, the radio frequency unit 401 can be used for receiving and sending signals during information transmission or communication. Specifically, after receiving downlink data from the base station, it is sent to the processor 410 for processing; in addition, uplink data is sent to the base station. Generally, the radio frequency unit 401 includes but is not limited to an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, etc. In addition, the radio frequency unit 401 can also communicate with the network and other devices through a wireless communication system.

[0149] The electronic device provides users with wireless broadband Internet access through the network module 402, such as helping users to send and receive emails, browse web pages, and access streaming media.

[0150] The audio output unit 403 can convert the audio data received by the RF unit 401 or the network module 402 or stored in the memory 409 into an audio signal and output it as sound. Moreover, the audio output unit 403 can also provide audio output related to a specific function performed by the electronic device 400 (for example, a call signal reception sound, a message reception sound, etc.). The audio output unit 403 includes a speaker, a buzzer, a receiver, etc.

[0151] The input unit 404 is used to receive audio or video signals. The input unit 404 may include a graphics processor (GPU) 4041 and a microphone 4042, and the graphics processor 4041 processes the image data of a static picture or video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The processed image frame can be displayed on the display unit 406. The image frame processed by the graphics processor 4041 can be stored in the memory 409 (or other storage medium) or sent via the radio frequency unit 401 or the network module 402. The microphone 4042 can receive sound and can process such sound into audio data. The processed audio data can be converted into a format output that can be sent to a mobile communication base station via the radio frequency unit 401 in the case of a telephone call mode.

[0152] The electronic device 400 also includes at least one sensor 405, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor includes an ambient light sensor and a proximity sensor, wherein the ambient light sensor can adjust the brightness of the display panel 4061 according to the brightness of the ambient light, and the proximity sensor can turn off the display panel 4061 and / or the backlight when the electronic device 400 is moved to the ear. As a type of motion sensor, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally three axes), and can detect the magnitude and direction of gravity when stationary, which can be used to identify the posture of the electronic device (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; the sensor 405 can also include a fingerprint sensor, a pressure sensor, an iris sensor, a molecular sensor, a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor, etc., which will not be repeated here.

[0153] The display unit 406 is used to display information input by the user or information provided to the user. The display unit 406 may include a display panel 4061, which may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like.

[0154] The user input unit 407 can be used to receive input digital or character information, and to generate key signal input related to user settings and function control of the electronic device. Specifically, the user input unit 407 includes a touch panel 4071 and other input devices 4072. The touch panel 4071, also known as a touch screen, can collect the user's touch operation on or near it (such as the user's operation on the touch panel 4071 or near the touch panel 4071 using any suitable object or accessory such as a finger, stylus, etc.). The touch panel 4071 may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch orientation, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into the contact point coordinates, and then sends it to the processor 410, receives the command sent by the processor 410 and executes it. In addition, the touch panel 4071 can be implemented using various types such as resistive, capacitive, infrared and surface acoustic waves. In addition to the touch panel 4071, the user input unit 407 may also include other input devices 4072. Specifically, other input devices 4072 may include but are not limited to a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which are not described in detail here.

[0155] Furthermore, the touch panel 4071 may be overlaid on the display panel 4061. When the touch panel 4071 detects a touch operation on or near it, it is transmitted to the processor 410 to determine the type of the touch event. Then, the processor 410 provides a corresponding visual output on the display panel 4061 according to the type of the touch event. Figure 4 In the figure, the touch panel 4071 and the display panel 4061 are two independent components to realize the input and output functions of the electronic device. However, in some embodiments, the touch panel 4071 and the display panel 4061 can be integrated to realize the input and output functions of the electronic device, which is not limited here.

[0156] The interface unit 408 is an interface for connecting an external device to the electronic device 400. For example, the external device may include a wired or wireless headset port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device with an identification module, an audio input / output (I / O) port, a video I / O port, a headphone port, etc. The interface unit 408 may be used to receive input (e.g., data information, power, etc.) from an external device and transmit the received input to one or more elements within the electronic device 400 or may be used to transmit data between the electronic device 400 and an external device.

[0157] The memory 409 can be used to store software programs and various data. The memory 409 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory 409 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0158] The processor 410 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. It executes various functions of the electronic device and processes data by running or executing software programs and / or modules stored in the memory 409, and calling data stored in the memory 409, so as to monitor the electronic device as a whole. The processor 410 may include one or more processing units; preferably, the processor 410 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, etc., and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 410.

[0159] The electronic device 400 may also include a power supply 411 (such as a battery) for supplying power to each component. Preferably, the power supply 411 may be logically connected to the processor 410 through a power management system, thereby implementing functions such as charging, discharging, and power consumption management through the power management system.

[0160] In addition, the electronic device 400 includes some functional modules not shown, which will not be described in detail here.

[0161] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0162] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0163] like Figure 5 As shown, in another embodiment provided by the present invention, a computer-readable storage medium 501 is also provided, in which instructions are stored. When the computer-readable storage medium 501 is run on a computer, the computer executes the method for generating ocean dynamics simulation results for the target sea area described in the above embodiment.

[0164] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

[0165] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the embodiments of the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0166] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0167] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0168] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0169] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0170] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.

[0171] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for generating ocean dynamics simulation results for a target sea area, characterized in that: include: Collecting sea area information for the target sea area; Construct physical information neural network; Determining a neural network output function for characterizing water level and flow velocity through the physical information neural network based on the sea area information; Constructing a two-dimensional plane and two-dimensional hydrodynamic model based on the sea area information and the neural network output function; constructing a residual function through the two-dimensional plane two-dimensional hydrodynamic model; A target weight is determined, and the residual function is adjusted based on the target weight, and an ocean dynamics simulation result for the target sea area is generated.

2. The method according to claim 1, characterized in that The sea area information includes average flow rate information, sea area boundary information, water level information and total area information, and the average flow rate information includes flow rate information.

3. The method according to claim 2, characterized in that The step of determining the neural network output function for characterizing water level and flow velocity through the physical information neural network based on the sea area information comprises: Determining lateral spatial position information and vertical spatial position information for the target sea area according to the sea area boundary information and the total area information; Determining initial weights and detection time of an activation function for the physical information neural network; The initial weight, the detection time, the average flow velocity information, the lateral spatial position information and the vertical spatial position information are input into Formula 1 to determine the neural network output function, and Formula 1 is: η(x,y,t)≈η s (x,y,t;θ u ) Wherein, x is the horizontal spatial position information, y is the vertical spatial position information, t is the detection time, θ is the initial weight, η is the water level information, and η s is the approximate value of the water level information, u is the flow rate information, is the average flow velocity information for the lateral spatial position information, is an approximate value of the average flow velocity information for the lateral spatial position information, is the average flow velocity information for the vertical spatial position information, is an approximate value of the average flow velocity information for the vertical spatial position information.

4. The method according to claim 3, characterized in that The step of constructing a two-dimensional plane two-dimensional hydrodynamic model based on the sea area information and the neural network output function comprises: Obtaining still water depth information, total water depth information, gravity acceleration information, rotation acceleration information, Scheher coefficient, Coriolis force parameter and Manning coefficient for the target sea area; Determining a horizontal eddy viscosity coefficient function for the lateral spatial position information and the vertical spatial position information; The static water depth information, the total water depth information, the gravity acceleration information, the rotation acceleration information, the Scheher coefficient, the Coriolis force parameter, the Manning coefficient, the horizontal eddy viscosity coefficient function, the detection time, the vertical spatial position information, the lateral spatial position information and the average flow velocity information are input into Formula 2 to determine the two-dimensional plane two-dimensional hydrodynamic model. Formula 2 is: Wherein, x is the lateral spatial position information, y is the vertical spatial position information, t is the detection time, η is the water level information, h is the static water depth information, H is the total water depth information, and g is the gravity acceleration information. is the average flow velocity information for the lateral spatial position information, is the average flow velocity information for the vertical spatial position information, is the autobiographical acceleration information for the lateral spatial position information, f is the Coriolis force parameter, is the autobiographical acceleration information for the vertical spatial position information, C is the Xie Cai coefficient, n is the Manning coefficient, ε x is the horizontal eddy viscosity coefficient function for the lateral spatial position information, ε y is the horizontal eddy viscosity coefficient function for the lateral spatial position information.

5. The method according to claim 4, characterized in that The step of constructing a residual function through the two-dimensional plane two-dimensional hydrodynamic model comprises: The lateral spatial position information, the vertical spatial position information, the detection time, the approximate value of the water level information, the approximate value of the average flow velocity information for the lateral spatial position information, the approximate value of the average flow velocity information for the vertical spatial position information, the Coriolis force parameter, the static water depth information, the total water depth information, the gravitational acceleration information, the horizontal eddy viscosity coefficient function for the lateral spatial position information, the horizontal eddy viscosity coefficient function for the vertical spatial position information and the Xie Cai coefficient are input into Formula 3 to construct the residual function. Formula 3 is: e=e1(θ)+e2(θ)+e3(θ) Wherein, e, e1, e2 and e3 are the residual functions, x is the lateral spatial position information, y is the vertical spatial position information, t is the detection time, η s is the approximate value of the water level information, h is the static water depth information, H is the total water depth information, g is the gravity acceleration information, is an approximate value of the average flow velocity information for the lateral spatial position information, is the approximate value of the average flow velocity information for the vertical spatial position information, f is the Coriolis force parameter, C is the Scheide coefficient, ε x is the horizontal eddy viscosity coefficient function for the lateral spatial position information, ε y is the horizontal eddy viscosity coefficient function for the vertical spatial position information.

6. The method according to claim 5, characterized in that The step of determining the target weight comprises: Determine the learning rate and number of iterations; The learning rate, the residual function and the number of iterations are input into Formula 4 to determine the target weight, which is: Wherein, θ is the initial weight, n is the number of iterations, α is the learning rate, e is the residual function, θ n+1 is the target weight.

7. The method according to any one of claims 1 to 6, characterized in that: The physical information neural network includes two hidden layers.

8. A device for generating ocean dynamics simulation results for a target sea area, characterized in that: include: A sea area information collection module, used to collect sea area information for the target sea area; Physical information neural network building module, used to build physical information neural network; A neural network output function determination module, used to determine a neural network output function for characterizing water level and flow velocity through the physical information neural network based on the sea area information; A two-dimensional plane two-dimensional hydrodynamic model output module, used for constructing a two-dimensional plane two-dimensional hydrodynamic model based on the sea area information and the neural network output function; A residual function construction module, used for constructing a residual function through the two-dimensional plane two-dimensional hydrodynamic model; The target weight determination module is used to determine the target weight, adjust the residual function based on the target weight, and generate an ocean dynamics simulation result for the target sea area.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; The memory is used to store computer programs; The processor is used to implement the method according to any one of claims 1 to 7 when executing the program stored in the memory.

10. A computer-readable storage medium having instructions stored thereon, which, when executed by one or more processors, cause the processors to perform the method according to any one of claims 1 to 7.

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