Method and device for rapidly forecasting dynamic response of waves in harbor and moored ships

Abstract

The invention discloses a method for rapidly forecasting dynamic response of waves in a port and moored ships. The method comprises the steps that S1, a wave numerical model in the port is built; s2, building a mooring numerical model of a preset ship type; s3, establishing a training set by using the in-port wave numerical model and the mooring numerical model, wherein the training set comprises a coastal wave condition parameter, a coastal wind field condition parameter, an in-port wave parameter and mooring ship dynamic response data; s4, taking the coastal wave condition parameters and the coastal wind field condition parameters as input, taking the in-port wave parameters and the mooring ship dynamic response data as output, and training to obtain a neural network model; and S5, inputting coastal wave observation data and coastal wind field observation data into the neural network model to obtain in-port waves and dynamic response prediction data of the mooring ship. Defects in the prior art can be overcome, and rapid forecasting of dynamic response of waves and mooring ships in the harbor is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field related to port engineering, and more specifically, to a method and device for rapidly predicting the dynamic response of port waves and moored ships based on wave numerical simulation, mooring numerical simulation and artificial neural network model. Background Art

[0002] The main function of a port is to provide safe berthing waters for ships to facilitate cargo loading and unloading operations. To ensure safe operation of ships, the meteorological and hydrological conditions in the port waters must not exceed certain thresholds, and the dynamic responses of moored ships, such as the movement, mooring force, and fender reaction force, must not exceed certain standards. Forecasting the wave conditions in the port waters and the dynamic responses of moored ships can provide data support for port operations: loading and unloading operators can reasonably arrange and dispatch according to the wave conditions in the port waters in the future, as well as the movement, mooring force, and fender reaction force of the ships, improve the loading and unloading efficiency of the terminal, and take early warning and risk avoidance measures in time before an emergency occurs to ensure the safe operation of the port.

[0003] However, in order to avoid affecting the daily operation of the port, real-time wave monitoring is usually implemented in the nearshore waters outside the port waters. Wave monitoring in the port waters usually only occurs before the port is officially put into operation, and the monitoring of the motion, mooring force, and fender reaction force of moored ships is even more rare. The wave numerical model can be used to simulate the propagation and deformation process of waves and determine the wave conditions in the port waters, while the mooring numerical model can simulate the dynamic response of the ship under different environmental conditions, including the six degrees of freedom of the ship's motion, the mooring force of the cable, the fender reaction force, etc. However, numerical simulation technology can only simulate fixed working conditions, and cannot directly predict the wave conditions in the port waters and the dynamic response of the moored ship, such as the motion, mooring force, and fender reaction force, under unknown conditions on site. Moreover, the calculation of each working condition of the numerical simulation technology takes a long time and has poor timeliness. It cannot achieve real-time prediction, let alone meet the requirements of practical applications.

[0004] Therefore, it is necessary to design a technical solution that can overcome the above-mentioned defects. Summary of the invention

[0005] An object of the present invention is to provide a method and device for rapidly predicting the dynamic response of waves in a port and moored ships, so as to overcome the defects of the prior art and realize the rapid prediction of the dynamic response of waves in a port and moored ships.

[0006] In order to achieve these purposes and other advantages of the present invention, according to one aspect of the present invention, the present invention provides a method for quickly predicting the dynamic response of harbor waves and moored ships, comprising: S1: constructing a numerical model of harbor waves; S2: constructing a mooring numerical model of a preset ship type; S3: using the harbor wave numerical model and the mooring numerical model to establish a training set, the training set including nearshore wave condition parameters, nearshore wind field condition parameters, harbor wave parameters, and moored ship dynamic response data; S4: using the nearshore wave condition parameters and the nearshore wind field condition parameters as inputs, and the harbor wave parameters and the moored ship dynamic response data as outputs, to train and obtain a neural network model; S5: inputting nearshore wave observation data and nearshore wind field observation data into the neural network model to obtain harbor wave and moored ship dynamic response prediction data.

[0007] Furthermore, in the S1, the nearshore wave condition information is obtained, and the nearshore wave height, period, and wave direction combination are determined as input, and a numerical model of the waves in the harbor is constructed using a gentle slope equation model or a Boussinesq model to simulate the wave movement in the harbor.

[0008] Furthermore, the method for obtaining the nearshore wave condition information includes: downloading from the global wave model and wave reanalysis hindcast database, or outputting the nearshore wave condition information through a pre-established regional wave numerical model, or deploying observation equipment to conduct wave observations in the nearshore waters to collect the nearshore wave condition information.

[0009] Furthermore, the numerical model of the harbor waves adopts a rectangular grid, and the calculation domain includes the entire harbor waters, port engineering buildings and part of the nearshore waters.

[0010] Furthermore, in said S2, the floating body equation of formula (1) is used to construct a mooring numerical model; Among them, x k (t) represents the translation and rotation motion of the floating body in six degrees of freedom. The points above represent the differential with respect to time t, M k and a k are the inertial force recovery matrix and the additional mass impulse response matrix, K k is the wave radiation force impulse response matrix, C k is the static force restitution matrix, F D (t) is the wave exciting force, F nl (t) is the nonlinear external force caused by mooring force, fender reaction force, viscous damping, wind force, flow force, second-order wave drift force and friction damping force.

[0011] Furthermore, the parameters of the preset ship type are obtained, combined with the numerical model of waves in the port, and the floating body equation is used to build a mooring numerical model.

[0012] Furthermore, the nearshore wave condition parameters and the nearshore wind field condition parameters include nearshore wave height, wave period, wave direction, wind speed, and wind direction.

[0013] Furthermore, the wave parameters in the port and the dynamic response data of the moored ship include the six degrees of freedom motion of the ship, the mooring force, and the fender reaction force.

[0014] According to another aspect of the present invention, there is also provided an apparatus for rapidly predicting the dynamic response of harbor waves and moored ships, comprising: a first building module for building a numerical model of harbor waves; a second building module for building a mooring numerical model of a preset ship type; an establishment module for establishing a training set using the harbor wave numerical model and the mooring numerical model, wherein the training set comprises nearshore wave condition parameters, nearshore wind field condition parameters, harbor wave parameters, and moored ship dynamic response data; a training module for training a neural network model using the nearshore wave condition parameters and the nearshore wind field condition parameters as inputs and the harbor wave parameters and the moored ship dynamic response data as outputs; and a prediction module for inputting nearshore wave observation data and nearshore wind field observation data into the neural network model to obtain predicted data of harbor waves and the dynamic response of the moored ship.

[0015] The present invention has at least the following beneficial effects: The present invention proposes a scientific method for rapidly predicting the dynamic response of waves in a port and moored ships based on wave numerical simulation, mooring numerical simulation and artificial neural network model. The method sequentially builds a numerical model of waves in the port and a numerical model of a mooring of a preset ship type, and generates training data using the wave numerical model and the numerical model of the mooring of the preset ship type. The artificial neural network model is trained to establish a mapping relationship between nearshore waves, wind field conditions and the dynamic response of waves in the port and ships. Finally, by combining the nearshore wave observation data and the wind field observation data, rapid prediction of the dynamic response of waves in the port and moored ships is achieved, which can help guide terminal operations, improve terminal loading and unloading efficiency, ensure safe port operation, reduce costly, cumbersome and complex data collection work, and reduce numerical simulations that consume a lot of time and computing resources.

[0016] Other advantages, objectives and features of the present invention will be embodied in part through the following description, and in part will be understood by those skilled in the art through study and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 : An implementation flow chart of an embodiment of the present application for rapidly predicting the dynamic response of waves in a port and moored ships; Figure 2 : An example diagram of a wave numerical model according to an embodiment of the present application; Figure 3 : An example diagram of the arrangement of ship mooring cables in a mooring numerical model according to an embodiment of the present application; Figure 4 : An example diagram of an artificial neural network model according to an embodiment of the present application; Figure 5-6 : An application example diagram of an embodiment of the present application (taking the prediction of the six degrees of freedom motion of a moored ship as an example). DETAILED DESCRIPTION

[0018] The present invention is further described in detail below in conjunction with the accompanying drawings so that those skilled in the art can implement the invention with reference to the description.

[0019] It should be understood that the terms such as "having", "including" and "comprising" used in the embodiments of the present application do not exclude the existence or addition of one or more other elements or their combinations. All directional indications (such as up, down, left, right, front, back...) in the embodiments of the present application are only used to explain the relative position relationship, movement, etc. between the components in a certain specific posture. If the specific posture changes, the directional indication will also change accordingly. When an element is referred to as "fixed on" or "set on" another element, it can be directly on the other element or there may be a centering element at the same time. When an element is referred to as "connecting" another element, it can be directly connected to another element or it can be indirectly connected to another element through a centering element. The description of "first", "second", etc. in the embodiments of the present application is only for descriptive purposes, and cannot be understood as indicating or implying its relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" can explicitly or implicitly include at least one of the features.

[0020] It should be noted that the technical solutions between the various embodiments of the present application can be combined with each other, but it must be based on the fact that ordinary technicians in the field can implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0021] like Figure 1 As shown, the embodiment of the present application provides a method for quickly predicting the dynamic response of waves in a port and a moored ship, including: S1: Build a numerical model of harbor waves; The numerical model of harbor waves is usually a phase-resolving model, including the mild-slope equation model and the Boussinesq model. The phase-resolving model can solve the phase problem and simulate physical phenomena such as wave diffraction, reflection, shallow water deformation, and breaking, thereby improving the simulation accuracy of harbor waves.

[0022] Taking the Boussinesq wave model as an example, the governing equation of the model is: Wherein, equation (1) is the continuity equation, equation (2) and equation (3) are the momentum equations in the x-direction and y-direction respectively, n is the porosity, ξ is the water surface height, t is the time, P and Q are the flux densities in the x-direction and y-direction respectively, h is the total water surface height, Rxx, Rxy and Ryy represent the residual momentum generated by the non-uniform velocity distribution, Fx and Fy are the horizontal stresses in the x-direction and y-direction respectively, g is the gravitational acceleration, α and β are the laminar and turbulent resistance coefficients in the porous medium respectively, C is the Xie Cai coefficient, ψx and ψy are the Boussinesq dispersion terms in the x-direction and y-direction respectively; the Boussinesq wave model solves the control equations in time and space through numerical discretization methods, including finite difference, finite element, finite volume and other methods; There are many wave numerical models in the prior art, which can be built with reference to the prior art; here a feasible method is provided, in the S1, the nearshore wave condition information is obtained, the nearshore wave height, period, and wave direction combination is determined as input, and the gentle slope equation model or the Bussinessk model is used to build a numerical model of the waves in the port to simulate the wave motion in the port; specifically, the wave height-period-wave direction combination is used to generate irregular incident wave input to simulate the wave motion in the port waters under the wave condition, and the simulation time needs to be long enough to stabilize the wave motion state in the port; the numerical model of the waves in the port adopts a rectangular grid, and the calculation domain includes the entire port waters, port engineering buildings and part of the nearshore waters; specifically, Figure 2 This is an example of a typical numerical model of harbor waves: the grid size needs to be fine enough to depict small-scale terrain and structures such as harbor waters, channels, and breakwaters; at locations such as beaches, breakwaters, revetments, and dock walls, a porous layer is set to simulate the incomplete reflection of waves corresponding to different reflection coefficients; at the water-water boundary of the model, a sponge layer is set to absorb wave energy to prevent wave energy from reflecting back to the model calculation domain; Exemplarily, the method for obtaining the nearshore wave condition information includes: downloading from the global wave model and wave reanalysis hindcast database, or outputting the nearshore wave condition information through a pre-established regional wave numerical model, or deploying observation devices to conduct wave observations in the nearshore waters and collect nearshore wave condition information; specifically, the nearshore wave condition information can be obtained in one of the following ways: 1. downloading through the global wave model and wave reanalysis hindcast database, the global wave model and wave reanalysis hindcast database include: NOAA Wind Wave Model (Wind Wave Model; WAVEWATCH III), ECMWF Wave Simulation Project (Wave Modelling Project; WAM), China Communications Construction Fourth Navigation Institute Global Wave Model (CCCC-FHDI GlobalWave Model), etc.; 2. conducting regional wave numerical simulations of large and medium-scale sea areas through wave numerical models, and the regional wave numerical models can output nearshore wave information; 3. conducting wave observations in the nearshore waters and collecting nearshore wave information by deploying observation devices such as wave meters and wave measuring buoys; After extracting wave information near the shore, the wave height-period-wave direction combination of the typical nearshore wave condition is determined through statistical analysis methods such as wave height-period-wave direction joint distribution. In order to make the numerical simulation of in-harbor waves feasible, the wave height, period and wave direction can usually be merged according to the joint distribution of wave height-period-wave direction of nearshore waves. For example, different incident wave heights are selected at intervals of 0.5~1.0m, a representative wave period is taken every 2~3s, and a representative wave direction is taken every 22.5° phase angle for numerical simulation; S2: Build a mooring numerical model of a preset ship type; There are many wave numerical models in the existing technology, which can be built with reference to the existing technology. Here, a feasible method is provided, that is, using the floating body equation of formula (4) to build a mooring numerical model; Among them, x k (t) represents the translation and rotation of the floating body in six degrees of freedom, namely longitudinal movement, transverse movement, heave, rotation, pitch and roll. The points above represent the differential with respect to time t, M k and a k are the inertial force recovery matrix and the additional mass impulse response matrix, K k is the wave radiation force impulse response matrix, C k is the static force recovery matrix, τ is time, and is the integral variable. In equation (4), the first term on the left describes the inertial force, the second term describes the dynamic force (or wave radiation force), the third term describes the static force, and the right side summarizes the wave excitation force F D (t) and other nonlinear external forces F nl(t) is the sum of the external forces, namely: The right-hand side terms in equation (5) represent the nonlinear external forces caused by mooring force, fender reaction force, viscous damping, wind, flow, second-order wave drift force and friction damping force; Exemplarily, the parameters of the preset ship type are obtained, and the numerical model of the mooring is constructed by using the floating body equation in combination with the numerical model of the wave in the port. Specifically, the numerical model of the mooring usually adopts a numerical discrete method, such as the boundary element method, to first calculate the additional mass impulse response matrix a k and the wave radiation force impulse response matrix K k , and calculate the inertial force restoration matrix M according to the geometric characteristics of the floating body k and the static force recovery matrix C k , by combining the wave input conditions to calculate the wave excitation force F D (t), and finally solve the motion equation (4) of the floating body in the time domain to calculate the motion x of the floating body. k (t) and the mooring force F line (t) and fender reaction force F fender (t) and other parameters; in actual application, a grid is constructed, and the grid area needs to include the berthing waters and major structures such as docks. According to the preset ship type parameters, including ship draft, displacement, windward area, etc. (to determine the geometric characteristics of the ship), the added mass impulse response matrix a is calculated k 、Inertial force restoration matrix M k and the static force recovery matrix C k According to the wave field in the harbor waters under the action of typical nearshore wave conditions obtained by S1 through the wave numerical model, the wave radiation force impulse response matrix K is calculated k and the wave excitation force F D (t), combined with the mooring arrangement, cable parameters (the relationship between force and deformation), fender parameters (the relationship between force and deformation), wind field conditions, etc., determine the nonlinear external forces caused by viscous damping, wind, flow, second-order wave drift force and friction damping force, and finally solve the floating body motion control equation to simulate the irregular motion of the ship in the moored state, and output the six degrees of freedom motion of the ship corresponding to different preset ship types, typical near-shore wave conditions, wind field conditions, and dynamic response data such as mooring force and fender reaction force; Figure 3This is an example of the ship mooring arrangement in the mooring numerical model: the cable arrangement consists of 4 bow cables and 4 stern cables, as well as two forward and rear return cables for controlling the longitudinal movement of the ship. The cable arrangement and cable parameters can refer to the ship design rules and the guidelines of the International Shipping Association, such as: "Mooring Equipment Guidelines 4th Edition (MEG4)" and "Guidance on Shipboard Towing and Mooring Equipment"; S3: using the port wave numerical model and the mooring numerical model to establish a training set, wherein the training set includes nearshore wave condition parameters, nearshore wind field condition parameters, port wave parameters, and moored ship dynamic response data; Exemplarily, the nearshore wave condition parameters and the nearshore wind field condition parameters include nearshore wave height, wave period, wave direction, wind speed, and wind direction; Exemplarily, the in-port wave parameters and the moored ship dynamic response data include the ship's six-degree-of-freedom motion, mooring force, and fender reaction force.

[0023] S4: taking the nearshore wave condition parameters and the nearshore wind field condition parameters as inputs, and the in-port wave parameters and the moored ship dynamic response data as outputs, training to obtain a neural network model; Exemplarily, the neural network used for training may be a feedforward neural network, a BP neural network, a convolutional neural network, etc.; Figure 4 A feasible neural network model is shown: the artificial neural network model consists of an input layer, a hidden layer and an output layer; When using the artificial neural network model to predict the dynamic response of waves in the port and moored ships, the wave conditions and wind field conditions near the shore can be selected, that is, the nearshore wave height H, wave period T, wave direction D, and wind speed W. s Wind direction W d As the input signal of the model, the dynamic response of the waves in the port and the moored ship, namely the wave height H', wave period T', wave direction D', and the six degrees of freedom motion of the ship x k (k=1,2,...,6), and the mooring force F i line and fender reaction force F j fender(i, j represent the number of cables and fenders used for ship mooring respectively) as the expected output of the model. Therefore, the input layer of the artificial neural network model consists of 6 input neurons (including nearshore wave height, period, wave direction, wind speed, wind direction and fixed input + 1), the output layer consists of 9+i+j output neurons (including port wave height, period, wave direction, six degrees of freedom motion of moored ships, mooring force and fender reaction force), and the hidden layer consists of 10 hidden neurons by default, and the number can be further adjusted according to the prediction accuracy of the model; The analysis data of the artificial neural network model are all derived from the output of the wave numerical model and the dynamic mooring numerical model; the nearshore waves, wind farms, harbor waves and ship dynamic response data that need to be analyzed are divided into three subsets: training data is used to train the artificial neural network, and the model is made suitable for wave and ship dynamic response analysis and prediction by adjusting the synaptic weights in the model; cross-validation data, cross-validation data is used to avoid overfitting; when the error between the predicted value and the expected value in the cross-validation data begins to increase, the training will stop, and the artificial neural network model is considered to be the best universal model at this time; test data, test data is used to test the application accuracy of the artificial neural network model; once the model passes training and cross-validation, the synaptic weights are frozen, the test data is input into the artificial neural network model for prediction, and the predicted value is compared with the expected value to evaluate the model prediction accuracy.

[0024] S5: inputting the nearshore wave observation data and the nearshore wind field observation data into the neural network model to obtain the in-port wave and the dynamic response prediction data of the moored ship; Through training, cross-validation and testing of artificial neural network models, a mapping relationship between nearshore waves, wind field conditions and port waves and ship dynamic responses was established, which can be used to predict port waves and ship dynamic responses. In actual application, nearshore wave observation data and wind field observation data can be used as inputs to the artificial neural network model, and port waves and moored ship dynamic responses can be used as model outputs to achieve rapid prediction of port waves and moored ship dynamic responses; Figure 5-6The application example of the six degrees of freedom motion of a moored ship predicted by this embodiment is demonstrated, and the curve of the six degrees of freedom motion of a moored ship predicted over time is realized; furthermore, by combining the ship operation wave standard and the ship motion, mooring force and fender reaction force standard, the terminal operation can be guided, the terminal loading and unloading efficiency can be improved, and the safe operation of the port can be ensured; the ship operation wave standard and the ship motion, mooring force and fender reaction force standard can refer to the cable and fender product manual and the following specifications and guidelines: "General Design Specification for Seaports JTS 165-2013", "Criteria for Movements of Moored Ships in Harbours: A Practical Guide" and "Criteria for the (Un)loading of Container Vessels"; It can be seen that the present embodiment proposes a scientific method for rapidly predicting the dynamic response of port waves and moored ships based on wave numerical simulation, mooring numerical simulation and artificial neural network model. The method sequentially builds a port wave numerical model and a mooring numerical model of a preset ship type, and generates training data using the wave numerical model and the mooring numerical model of the preset ship type, and trains the artificial neural network model to establish a mapping relationship between nearshore waves, wind field conditions and port waves and ship dynamic responses, and finally realizes rapid prediction of port waves and moored ship dynamic responses by combining nearshore wave observation data and wind field observation data; compared with the prior art, the present embodiment overcomes the defects of the prior art in that it is difficult to monitor the motion, mooring force and fender reaction force of port waters and moored ships, and overcomes the defect that the numerical model cannot be predicted in real time, and realizes rapid prediction of port waves and moored ship dynamic responses, which can help guide terminal operations, improve terminal loading and unloading efficiency, ensure safe port operation, and reduce costly, cumbersome and complex data collection work.

[0025] The embodiment of the present application also provides a device for quickly predicting the dynamic response of port waves and moored ships, including: a first building module, used to build a numerical model of port waves; a second building module, used to build a mooring numerical model of a preset ship type; a building module, used to use the port wave numerical model and the mooring numerical model to establish a training set, wherein the training set includes nearshore wave condition parameters, nearshore wind field condition parameters, port wave parameters, and moored ship dynamic response data; a training module, used to use the nearshore wave condition parameters and the nearshore wind field condition parameters as inputs, and the port wave parameters and the moored ship dynamic response data as outputs, to train and obtain a neural network model; a prediction module, used to input nearshore wave observation data and nearshore wind field observation data into the neural network model, and obtain port wave and moored ship dynamic response prediction data; Based on the same inventive concept as the above-mentioned embodiment, the present embodiment uses a computer program to complete the construction of a first building module, a second building module, a building module, a training module and a prediction module. For any port that needs to be predicted, the first building module is used to build a numerical model of waves in the port, the second building module is used to build a numerical model of mooring of a preset ship type, the building module is used to generate data and establish a training set according to the numerical model of waves in the port and the numerical model of mooring of the preset ship type, the training module is used to train a neural network prediction model, and the neural network prediction model is used to predict the dynamic response of waves in the port and moored ships according to nearshore wave observation data and nearshore wind field observation data. Compared with the prior art, the present embodiment overcomes the defects of the prior art in that it is difficult to monitor the movement, mooring force and fender reaction force of the port water area and moored ships, and overcomes the defect that the numerical model cannot be predicted in real time, and realizes the rapid prediction of the dynamic response of waves in the port and moored ships, which can help guide terminal operations, improve terminal loading and unloading efficiency, and ensure safe port operation.

[0026] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes, and they can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the illustrations shown and described herein.

Claims

1. A method for rapidly predicting the dynamic response of waves in a port and moored ships, characterized in that: include: S1: Build a numerical model of harbor waves; S2: Build a mooring numerical model of a preset ship type; S3: using the port wave numerical model and the mooring numerical model to establish a training set, wherein the training set includes nearshore wave condition parameters, nearshore wind field condition parameters, port wave parameters, and moored ship dynamic response data; S4: taking the nearshore wave condition parameters and the nearshore wind field condition parameters as inputs, and the in-port wave parameters and the moored ship dynamic response data as outputs, training to obtain a neural network model; S5: Inputting the nearshore wave observation data and the nearshore wind field observation data into the neural network model to obtain the in-port wave and the moored ship dynamic response prediction data.

2. The method for rapidly predicting the dynamic response of waves in a harbor and moored ships according to claim 1, characterized in that: In the S1, the nearshore wave condition information is obtained, the nearshore wave height, period, and wave direction combination is determined as input, and a numerical model of the waves in the harbor is constructed using a gentle slope equation model or a Boussinesq model to simulate the wave movement in the harbor.

3. The method for rapidly predicting the dynamic response of waves in a harbor and moored ships according to claim 2, characterized in that: The method for obtaining the nearshore wave condition information includes: downloading from the global wave model and wave reanalysis hindcast database, or outputting the nearshore wave condition information through a pre-established regional wave numerical model, or deploying observation equipment to conduct wave observation in the nearshore waters to collect the nearshore wave condition information.

4. The method for rapidly predicting the dynamic response of harbor waves and moored ships as claimed in claim 2, characterized in that: The numerical model of harbor waves adopts a rectangular grid, and the calculation domain includes the entire harbor waters, harbor buildings and part of the nearshore waters.

5. The method for rapidly predicting the dynamic response of waves in a harbor and moored ships according to claim 1, characterized in that: In said S2, the floating body equation of formula (1) is used to build a mooring numerical model; Among them, x k (t) represents the translation and rotation motion of the floating body in six degrees of freedom. The points above represent the differential with respect to time t, M k and a k are the inertial force recovery matrix and the additional mass impulse response matrix, K k is the wave radiation force impulse response matrix, C k is the static force restitution matrix, F D (t) is the wave exciting force, F nl (t) is the nonlinear external force caused by mooring force, fender reaction force, viscous damping, wind force, flow force, second-order wave drift force and friction damping force.

6. The method for rapidly predicting the dynamic response of harbor waves and moored ships as claimed in claim 5, characterized in that: The parameters of the preset ship type are obtained, and the numerical model of the mooring model is constructed using the floating body equation in combination with the numerical model of the wave in the port.

7. The method for rapidly predicting the dynamic response of waves in a harbor and moored ships according to claim 1, characterized in that: The nearshore wave condition parameters and the nearshore wind field condition parameters include nearshore wave height, wave period, wave direction, wind speed, and wind direction.

8. The method for rapidly predicting the dynamic response of harbor waves and moored ships as claimed in claim 1, characterized in that: The wave parameters in the port and the dynamic response data of the moored ship include the six degrees of freedom motion of the ship, the mooring force, and the fender reaction force.

9. A device for rapidly predicting the dynamic response of waves in a port and moored ships, characterized in that: include: The first building module is used to build a numerical model of waves in the harbor; The second building module builds a mooring numerical model of a preset ship type; An establishment module is used to establish a training set using the port wave numerical model and the mooring numerical model, wherein the training set includes nearshore wave condition parameters, nearshore wind field condition parameters, port wave parameters, and moored ship dynamic response data; A training module, for training and obtaining a neural network model by taking the nearshore wave condition parameters and the nearshore wind field condition parameters as inputs and the in-port wave parameters and the moored ship dynamic response data as outputs; The prediction module is used to input the nearshore wave observation data and the nearshore wind field observation data into the neural network model to obtain the prediction data of the port waves and the dynamic response of the moored ship.

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