An ecological breakwater impact pressure prediction method based on dimensionless parameter optimization

Abstract

The application discloses an ecological breakwater impact pressure prediction method based on dimensionless parameter optimization, and relates to the technical field of marine ecological engineering. The measured data of wave characteristics, breakwater structure parameters and corresponding impact pressures under different working conditions are collected and fitted to obtain a first functional relationship between the impact pressure and the dimensionless parameter. The dimensionless impact pressure is defined as the ratio of the impact pressure to the wave kinetic energy density, and a second functional relationship between the dimensionless parameter and the dimensionless impact pressure is obtained. An optimization model is constructed, in which the minimization of the dimensionless impact pressure value is taken as the objective function, and the dimensionless parameter is taken as the optimization variable. The optimal dimensionless parameter combination corresponding to the objective function is determined. The wave impact process of the breakwater to be evaluated within a period T is simulated by using CFD, time series data of the dimensionless impact pressure fitting value are obtained, the time series data are described by using a Markov chain model, and the prediction result of the dimensionless impact pressure at the next moment is obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of marine ecological engineering, and in particular to a method for predicting the impact pressure of an ecological breakwater based on dimensionless parameter optimization. Background Art

[0002] With the continuous advancement of marine development, the design and optimization of ecological breakwaters, as coastal engineering structures that combine ecological protection and defense functions, have attracted widespread attention. However, the impact of waves on ecological breakwaters is complex and difficult to accurately predict, which poses challenges to breakwater design and stability analysis.

[0003] Traditional breakwater impact pressure prediction methods are primarily based on empirical formulas or semi-empirical models, such as the modified Goda method. This method estimates impact wave forces by incorporating a shock wave pressure coefficient, but it may underestimate impact forces under certain conditions. Furthermore, while the PROVERBS project methodology provides a relatively systematic process for calculating impact wave forces, its results are inconsistent and the calculation process is relatively complex. These methods often fail to fully account for the unique structural characteristics of ecological breakwaters and the complex nature of waves when predicting impact pressure on breakwaters. In recent years, with the advancement of computing technology, numerical simulation methods have been widely used in the study of wave-breakwater interactions. For example, the multi-material Arbitrary Lagrangian-Eulerian (ALE) method is used to numerically simulate breakwaters subjected to wave impact, accurately capturing the pressure distribution and structural response under wave impact. However, these numerical simulation methods require extensive computing resources and precise input parameters, and their adaptability to diverse operating conditions requires further verification.

[0004] In practical engineering, the design of ecological breakwaters must not only consider the magnitude of wave impact forces but also the realization of ecological protection functions. Therefore, a more scientific, efficient, and applicable method for predicting impact pressure on ecological breakwaters is needed to optimize breakwater structural parameters, reduce impact pressure, and improve breakwater stability and ecological benefits.

[0005] In summary, there is still a lack of an optimization method for predicting the impact pressure of ecological breakwaters that can comprehensively consider wave characteristics and breakwater structural parameters. Therefore, a method for predicting the impact pressure of ecological breakwaters based on dimensionless parameter optimization is proposed. Summary of the Invention

[0006] The main purpose of the present invention is to provide a method for predicting the impact pressure of an ecological breakwater based on dimensionless parameter optimization, which can effectively solve the problems in the background technology.

[0007] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0008] A method for predicting impact pressure of ecological breakwaters based on dimensionless parameter optimization includes:

[0009] The measured data of wave characteristics, breakwater structural parameters and corresponding impact pressure Fp under different working conditions are collected and fitted. The first functional relationship between impact pressure Fp and dimensionless parameters is obtained based on the fitting results.

[0010] Define the dimensionless impact pressure Cp as the ratio of the impact pressure Fp to the wave kinetic energy density Ek, substitute the obtained first functional relationship into the definition of the dimensionless impact pressure Cp, and obtain the second functional relationship between the n dimensionless parameters and the dimensionless impact pressure Cp;

[0011] An optimization model is constructed with minimizing the dimensionless impact pressure min Cp as the objective function, all dimensionless parameters as optimization variables, and wave characteristic parameters, breakwater structural parameters, and the Froude similarity criterion for dimensionless parameters as constraints. A data optimization algorithm is used to determine the optimal dimensionless parameter combination corresponding to the objective function.

[0012] CFD is used to simulate the wave impact process of the breakwater to be evaluated within a period T, and the time series data of the dimensionless impact pressure fitting value under the optimal dimensionless parameter combination scenario is obtained. The time series data is described using the Markov chain model to obtain the prediction result of the dimensionless impact pressure at the next moment.

[0013] Furthermore, the dimensionless parameter includes at least one of dimensionless wave height, dimensionless wave steepness, dimensionless submergence depth, dimensionless bottom rise angle, and Keulegan-Carpenter number Kc, wherein each dimensionless parameter is defined as follows:

[0014] Dimensionless wave height is the ratio of wave height to water depth;

[0015] The dimensionless wave steepness is the ratio of wave height to wavelength;

[0016] The dimensionless inundation depth is the ratio of the inundation depth of the breakwater to the total height of the breakwater;

[0017] The dimensionless base rise angle is the base rise angle of the breakwater, which represents the angle between the bottom of the breakwater and the horizontal plane;

[0018] The Keulegan-Carpenter number Kc is the ratio of the total height of the breakwater to the width of the breakwater.

[0019] Furthermore, the expression of the first functional relationship is: Fp=f(Dp1, Dp2, .., Dp K ), where Dp K is the Kth dimensionless parameter;

[0020] The expression of the second functional relationship is: Cp=Fp / Ek=f(Dp1, Dp2, .., Dp K ) / Ek.

[0021] Furthermore, the calculation formula of wave kinetic energy density Ek is: Ek=0.5ρV 2 ; Where ρ represents the seawater density at the location of the breakwater to be evaluated; V represents the wave velocity.

[0022] Furthermore, in the constraints of the optimization model, the Froude similarity criterion specifies that the ratio of the inertia force to gravity of the optimization model and the breakwater to be evaluated must be equal, that is, their Froude numbers must be equal. The Froude number is defined as: Wherein, Fr is the Froude number; v represents the fluid velocity; g is the acceleration of gravity; L represents the characteristic length; and the characteristics are breakwater structural parameters.

[0023] Furthermore, the Markov chain model describes the time series data as follows: i,j =P(X t+1 =j|X t =i); where X t and X t+1 are the states of dimensionless impact pressure at time t and time t+1 respectively; P(·|·) is the conditional transition probability; p i,j It is expressed as the probability that the dimensionless impact pressure is transferred from state i to state j in one step at time t.

[0024] Furthermore, the process of determining the dimensionless impact pressure state includes the following steps:

[0025] The dimensionless shock pressure states are defined in order from small to large shock pressure, including the first state, the second state, the third state and the fourth state;

[0026] Arrange the time series data of the dimensionless impact pressure fitting values ​​under the optimal dimensionless parameter combination scenario from small to large, and calculate the first quartile, second quartile, and third quartile of the time series data;

[0027] The obtained quartiles are used to divide the state of the dimensionless shock pressure fitting value. The division principle is:

[0028] If the dimensionless shock pressure fitting value is ≤ the first quartile, the state is state 1;

[0029] If the first quartile < dimensionless shock pressure fitting value < second quartile, then its state is state 2;

[0030] If the second quartile ≤ dimensionless shock pressure fitting value < third quartile, then its state is state 3;

[0031] If the dimensionless shock pressure fitting value is ≥ the third quartile, its state is the 4th state.

[0032] Furthermore, at time t, the probability p of the dimensionless shock pressure transferring from state i to state j in one step is i,j The calculation method is: Where, f i,j It is expressed as the frequency of the dimensionless shock pressure transferring from state i to state j in one step in the time series data of the dimensionless shock pressure fitting value under the optimal dimensionless parameter combination scenario.

[0033] The present invention has the following beneficial effects:

[0034] Compared with the existing technology, by collecting the measured data of wave characteristics, breakwater structural parameters and corresponding impact pressure Fp under different working conditions and performing fitting processing, the first functional relationship between the impact pressure Fp and the dimensionless parameters is obtained according to the fitting results, the dimensionless impact pressure Cp is defined as the ratio of the impact pressure Fp to the wave kinetic energy density Ek, the second functional relationship between n dimensionless parameters and the dimensionless impact pressure Cp is obtained, and a dimensionless impact pressure value min is constructed to minimize the dimensionless impact pressure value. An optimization model with Cp as the objective function, all dimensionless parameters as optimization variables, and wave characteristic parameters, breakwater structural parameters, and the limitations of the Froude similarity criterion on dimensionless parameters as constraints is proposed. A data optimization algorithm is used to determine the optimal dimensionless parameter combination corresponding to the objective function. CFD is used to simulate the wave impact process of the breakwater to be evaluated within a period T, and the time series data of the dimensionless impact pressure fitting value under the optimal dimensionless parameter combination scenario is obtained. The time series data is described using a Markov chain model to obtain the prediction result of the dimensionless impact pressure at the next moment. This model can comprehensively consider wave characteristics and breakwater structural parameters, use statistical historical data as the basis for numerical simulation, and use the Markov chain model to predict the wave impact process, thereby optimizing the structural parameters of the breakwater, reducing the impact pressure, and improving the operational stability of the breakwater. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 The figure is a flow chart of a method for predicting impact pressure of an ecological breakwater based on dimensionless parameter optimization according to the present invention. DETAILED DESCRIPTION

[0036] The present invention will be further described below in conjunction with specific embodiments. The accompanying drawings are for illustrative purposes only and represent only schematic diagrams rather than actual drawings. They should not be understood as limiting the present invention. In order to better illustrate the specific embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product.

[0037] The specific implementation process of the technical solution of the present invention includes the following steps:

[0038] Step 1: Collect the measured data of wave characteristics, breakwater structural parameters and corresponding impact pressure Fp under different working conditions and perform fitting processing. According to the fitting results, obtain the first functional relationship between the impact pressure Fp and the dimensionless parameters.

[0039] Among them, the expression of the first functional relationship is: Fp=f(Dp1,Dp2,..,Dp K ), where Dp K is the Kth dimensionless parameter; the dimensionless parameters include dimensionless wave height, dimensionless wave steepness, dimensionless submergence depth, dimensionless bottom rise angle and Keulegan-Carpenter number Kc, where the definitions of each dimensionless parameter are as follows:

[0040] Dimensionless wave height is the ratio of wave height to water depth;

[0041] The dimensionless wave steepness is the ratio of wave height to wavelength;

[0042] The dimensionless inundation depth is the ratio of the inundation depth of the breakwater to the total height of the breakwater;

[0043] The dimensionless base rise angle is the base rise angle of the breakwater, which represents the angle between the bottom of the breakwater and the horizontal plane;

[0044] The Keulegan-Carpenter number Kc is the ratio of the total height of the breakwater to the width of the breakwater.

[0045] It should be noted that the ratio of wave height to water depth is a key parameter affecting impact pressure. Higher wave heights or shallower water depths increase relative wave height, leading to greater impact pressure. Wave steepness reflects the steepness of the wave. Greater wave steepness means more concentrated wave energy, significantly increasing the impact pressure. The ratio of the breakwater's submerged depth to water depth affects the wave propagation and breaking process on the breakwater. A smaller submerged depth increases the dissipation of wave energy, thereby reducing the impact pressure. The smaller the bottom rise angle, the higher the peak impact pressure. A smaller bottom rise angle leads to more violent impact phenomena, and the impact pressure and acceleration are very sensitive to the bottom rise angle. The Keulegan-Carpenter number reflects the proportional relationship between the breakwater width and the wavelength. When the breakwater width increases to one-quarter of the wavelength, the rate of increase in the transmission coefficient slows significantly, meaning that more wave energy is dissipated and the impact pressure is reduced.

[0046] Specifically, the following methods can be used to collect wave characteristics, breakwater structural parameters and corresponding impact pressure data under different working conditions:

[0047] 1. Wave characteristics data collection

[0048] Field measurements: Wave height meters (wave meters) are used to record the vertical motion of the water surface, allowing analysis of wave characteristics such as wave height, period, and speed. Field measurements offer the advantages of high data accuracy and temporal resolution, but they also require consideration of wave meter maintenance, the impact of inclement weather, and cost.

[0049] Satellite remote sensing: Satellite remote sensing technology transmits and receives signals reflected from waves to estimate sea wave heights. Satellite remote sensing technology can cover a wide area and obtain continuous data, which has obvious advantages for collecting large-scale and long-term wave data series.

[0050] Wave spectrum analysis: The wave spectrum describes the distribution of wave energy at different frequencies and directions. By analyzing the wave spectrum, we can obtain the main characteristics of the wave, such as wave height, period, and propagation direction.

[0051] Experimental measurements: Regular and irregular wave tests were conducted in a wave tank, with sensors collecting wave height data. Regular wave heights were determined by directly measuring the maximum and minimum wave heights within each cycle, while the significant irregular wave height was determined by sorting all collected wave heights and taking the arithmetic average of the first one-third of the largest waves.

[0052] 2. Breakwater structural parameter data collection

[0053] Unmanned vessel and drone measurement: The unmanned vessel acquisition unit is used to collect underwater and above-water three-dimensional point cloud data of the breakwater. The unmanned vessel is equipped with a lidar above water and a multi-beam detector underwater; the drone acquisition unit is used to collect two-dimensional orthophoto images of the breakwater top structure.

[0054] Sensor monitoring: Various sensors are installed on the breakwater, such as wave sensors, water level sensors, wind speed sensors, temperature and humidity sensors, flow rate sensors, etc., to monitor the environmental parameters around the breakwater and the structural response of the breakwater.

[0055] Design and Historical Data: Collect the design parameters and historical monitoring data of the breakwater, including the breakwater's geometric dimensions, material properties, construction records, etc. This data can be used as a benchmark for assessing the health of the breakwater structure.

[0056] 3. Impact Pressure Data Collection

[0057] Pressure sensor measurement: Pressure sensors are installed on the breakwater surface to directly measure the pressure generated by waves impacting the breakwater. The data collected by the sensors is uploaded to the control software via a data logger for data processing and analysis.

[0058] Data preprocessing: The collected pressure data is preprocessed, including filtering, denoising, and validity testing. For example, adaptive filtering is used to filter noise, and the validity of the measurement data is tested based on the presence of a step waveform within the shock wave arrival time range.

[0059] Model test: A model test is conducted in a wave tank to simulate the impact pressure on the breakwater under different wave conditions. Pressure sensors are installed on the surface of the model breakwater to measure and record the impact pressure data.

[0060] Through the above method, wave characteristics, breakwater structural parameters and corresponding impact pressure data under different working conditions can be systematically collected.

[0061] Step 2: Define the dimensionless impact pressure Cp as the ratio of the impact pressure Fp to the wave kinetic energy density Ek, substitute the obtained first functional relationship into the definition of the dimensionless impact pressure Cp, and obtain the second functional relationship between the n dimensionless parameters and the dimensionless impact pressure Cp;

[0062] The expression of the second functional relationship is: Cp=Fp / Ek=f(Dp1,Dp2,..,Dp K ) / Ek;

[0063] It should be noted that wave kinetic energy density refers to the kinetic energy of a wave per unit mass or volume. For waves, kinetic energy density is usually related to the speed and amplitude of the wave. In wave dynamics, the kinetic energy density of a wave can be expressed as: Ek = 0.5ρV 2 ; Where ρ represents the seawater density at the location of the breakwater to be evaluated; V represents the wave velocity.

[0064] Step 3: Construct an optimization model with minimizing the dimensionless impact pressure min Cp as the objective function, all dimensionless parameters as optimization variables, and wave characteristic parameters, breakwater structural parameters, and the Froude similarity criterion for dimensionless parameters as constraints. Use a data optimization algorithm to determine the optimal dimensionless parameter combination corresponding to the objective function.

[0065] It should be noted that the constraints include the following:

[0066] Wave characteristic constraints: wave height and wavelength should meet the actual sea conditions; wave steepness should be within a reasonable range to avoid excessive wave steepness that causes wave breaking.

[0067] Breakwater structure constraints: The submergence depth should ensure the stability of the breakwater; the breakwater width and bottom rise angle should be within the achievable range of the project.

[0068] Physical model constraints: Dimensionless parameters must satisfy the Froude similarity criterion. The Froude similarity criterion specifies that the ratio of the inertia force to the gravity of the optimized model and the breakwater to be evaluated must be equal, that is, their Froude numbers must be equal. The Froude number is defined as: Among them, Fr is the Froude number; v represents the fluid velocity; g is the acceleration of gravity; L represents the characteristic length; among them, the characteristic is the breakwater structural parameter, and for all breakwater structural parameters, it is necessary to make the ratio of the inertia force to gravity of the optimization model and the breakwater to be evaluated equal, that is, the Froude numbers of the two are equal.

[0069] Step 4: Use CFD to simulate the wave impact process of the breakwater to be evaluated within a period T, and obtain the time series data of the dimensionless impact pressure fitting value under the optimal dimensionless parameter combination scenario.

[0070] The process of CFD simulation of wave impact pressure involves several key steps, including model establishment, meshing, boundary condition setting, solver configuration, and result analysis. The following are the detailed steps:

[0071] 1. Model Building

[0072] Geometric model: A 3D geometric model should be constructed based on the actual dimensions of the breakwater and wave tank. The model should include the breakwater, tank boundaries, and possible obstacles.

[0073] Physical model: Select an appropriate physical model, such as the multiphase flow model (Volume of Fluid, VOF) to simulate the interaction between water and air.

[0074] 2. Grid division

[0075] Mesh Generation: Use professional meshing tools (such as ANSYS Meshing) to generate high-quality computational meshes. The mesh should be fine enough to capture the complex flow of waves and breakwater surfaces.

[0076] Mesh optimization: Check the mesh quality to ensure the orthogonality, smoothness, and adaptability of the mesh and avoid mesh distortion.

[0077] 3. Boundary condition setting

[0078] Inlet Boundary: Set the wave inlet boundary conditions, such as using Open Channel Wave BC (open channel wave boundary conditions), and specify the wave theory (such as fifth-order solitary wave theory).

[0079] Outlet boundary: Set appropriate outlet boundary conditions, such as specifying the water level height.

[0080] Breakwater Surface: Set the breakwater surface as a wall boundary condition to ensure that the interaction between waves and the breakwater can be accurately captured.

[0081] 4. Solver Configuration

[0082] Solver Type: Select the Pressure-Based Solver suitable for transient simulation and activate the transient calculation option.

[0083] Turbulence Model: Select an appropriate turbulence model, such as the SST k-ω model, and activate the turbulence damping option.

[0084] Multiphase flow model: Activate the VOF model to simulate the interface between water and air.

[0085] 5. Numerical simulation

[0086] Initialization: Use Hybrid Initialization to set initial conditions, such as initial velocity, pressure, etc.

[0087] Solution process: Start the solver, set the appropriate time step and number of iterations to ensure the stability and accuracy of the calculation.

[0088] Monitor residuals: Use Fluent's Plot function to monitor residual changes and ensure that the residuals remain below the preset target value.

[0089] 6. Results Analysis

[0090] Pressure distribution: Extract pressure distribution data on the breakwater surface and analyze the pressure peak and distribution characteristics when waves hit.

[0091] Velocity field analysis: Observe the changes in velocity field after wave impact and understand the flow pattern of water.

[0092] Result verification: Compare the simulation results with experimental data or verified numerical results to verify the accuracy of the simulation.

[0093] 7. Optimization and Verification

[0094] Parameter optimization: By changing wave parameters (such as wave height and wavelength) or breakwater structure parameters (such as submergence depth and bottom rise angle), observe their impact on the impact pressure.

[0095] Sensitivity analysis: Perform parameter sensitivity analysis to understand the contribution of different parameters to the impact pressure.

[0096] Through the above steps, CFD technology can be used to effectively simulate the impact pressure of waves on the ecological breakwater.

[0097] Step 5: Use the Markov chain model to describe the time series data. The description of the time series data by the Markov chain model is: i,j =P(X t+1 =j|X t =i); where X t and X t+1 are the states of dimensionless impact pressure at time t and time t+1 respectively; P(·|·) is the conditional transition probability; p i,j It is expressed as the probability that the dimensionless impact pressure is transferred from state i to state j in one step at time t. The calculation method is: Where, f i,j It is expressed as the frequency of the dimensionless shock pressure transferring from state i to state j in one step in the time series data of the dimensionless shock pressure fitting value under the optimal dimensionless parameter combination scenario.

[0098] The process for determining the dimensionless shock pressure state includes the following steps:

[0099] The dimensionless shock pressure states are defined in order from small to large shock pressure, including the first state, the second state, the third state and the fourth state;

[0100] Arrange the time series data of the dimensionless impact pressure fitting values ​​under the optimal dimensionless parameter combination scenario from small to large, and calculate the first quartile, second quartile, and third quartile of the time series data;

[0101] The obtained quartiles are used to divide the state of the dimensionless shock pressure fitting value. The division principle is:

[0102] If the dimensionless shock pressure fitting value is ≤ the first quartile, the state is state 1;

[0103] If the first quartile < dimensionless shock pressure fitting value < second quartile, then its state is state 2;

[0104] If the second quartile ≤ dimensionless shock pressure fitting value < third quartile, then its state is state 3;

[0105] If the dimensionless shock pressure fitting value is ≥ the third quartile, its state is the 4th state.

[0106] Step 6: Use the constructed Markov chain model to obtain the prediction result of the dimensionless impact pressure at the next moment.

[0107] The specific process includes the following steps:

[0108] Get the dimensionless impact pressure value Fp at the current time t t , and determine the current dimensionless shock pressure state X according to step 5 t ;

[0109] Generate a random number δ that obeys uniform distribution according to the dimensionless impact pressure value and the state of dimensionless impact pressure at the current time t t , and there is δ t ∈[0,1];

[0110] Calculate the cumulative transfer probability matrix of dimensionless shock pressure transfer, and the calculation formula is: Assume the dimensionless impact pressure state at time t+1 is X t+1 , if any Then X t+1 =1, that is, the dimensionless impact pressure state at time t+1 is the first state; if Then X t+1 =r+1, that is, the dimensionless impact pressure state at time t+1 is the r+1 state, where r=1, 2, 3;

[0111] According to the state X of the dimensionless impact pressure at time t+1 t+1 , generate another random number δ that follows a uniform distribution t+1 , and there is δ t+1 ∈[0,1];

[0112] According to the generated random number δ t+1 Calculate the predicted value Fp of the dimensionless impact pressure at time t+1 t+1 , the calculation formula is: Fp t+1 =Fpt+1,min +δ t+1 ×(Fp t+1,max -Fp t+1,min ); where Fp t+1,min Expressed as the predicted value of dimensionless impact pressure Fp t+1 The lower limit of the dimensionless shock pressure range corresponding to the state; Fp t+1,max Expressed as the predicted value of dimensionless impact pressure Fp t+1 The upper limit of the dimensionless shock pressure range corresponding to the state.

[0113] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for predicting the impact pressure of an ecological breakwater based on dimensionless parameter optimization, characterized in that: include: The measured data of wave characteristics, breakwater structural parameters and corresponding impact pressure Fp under different working conditions are collected and fitted. The first functional relationship between impact pressure Fp and dimensionless parameters is obtained based on the fitting results. Define the dimensionless impact pressure Cp as the ratio of the impact pressure Fp to the wave kinetic energy density Ek, substitute the obtained first functional relationship into the definition of the dimensionless impact pressure Cp, and obtain the second functional relationship between the n dimensionless parameters and the dimensionless impact pressure Cp; An optimization model is constructed with minimizing the dimensionless impact pressure value minCp as the objective function, all dimensionless parameters as optimization variables, and wave characteristic parameters, breakwater structural parameters, and the Froude similarity criterion for dimensionless parameters as constraints. A data optimization algorithm is used to determine the optimal dimensionless parameter combination corresponding to the objective function. CFD is used to simulate the wave impact process of the breakwater to be evaluated within a period T, and the time series data of the dimensionless impact pressure fitting value under the optimal dimensionless parameter combination scenario is obtained. The time series data is described using the Markov chain model to obtain the prediction result of the dimensionless impact pressure at the next moment.

2. The method for predicting the impact pressure of an ecological breakwater based on dimensionless parameter optimization according to claim 1 is characterized in that: The dimensionless parameters include at least one of dimensionless wave height, dimensionless wave steepness, dimensionless submergence depth, dimensionless bottom rise angle, and Keulegan-Carpenter number Kc, wherein each dimensionless parameter is defined as follows: Dimensionless wave height is the ratio of wave height to water depth; The dimensionless wave steepness is the ratio of wave height to wavelength; The dimensionless inundation depth is the ratio of the inundation depth of the breakwater to the total height of the breakwater; The dimensionless base rise angle is the base rise angle of the breakwater, which represents the angle between the bottom of the breakwater and the horizontal plane; The Keulegan-Carpenter number Kc is the ratio of the total height of the breakwater to the width of the breakwater.

3. The method for predicting the impact pressure of an ecological breakwater based on dimensionless parameter optimization according to claim 1 is characterized in that: The expression of the first functional relationship is: Fp=f(Dp1, Dp2, .., Dp K ), where Dp K is the Kth dimensionless parameter; The expression of the second functional relationship is: Cp=Fp / Ek=f(Dp1, Dp2, .., Dp K ) / Ek.

4. The method for predicting the impact pressure of an ecological breakwater based on dimensionless parameter optimization according to claim 3 is characterized in that: The calculation formula of wave kinetic energy density Ek is: Ek=0.5ρV 2 ; Where ρ represents the seawater density at the location of the breakwater to be evaluated; V represents the wave velocity.

5. The method for predicting the impact pressure of an ecological breakwater based on dimensionless parameter optimization according to claim 1 is characterized in that: Among the constraints of the optimization model, the Froude similarity criterion specifies the following for dimensionless parameters: the ratio of the inertia force to gravity of the optimization model and the breakwater to be evaluated must be equal, that is, their Froude numbers must be equal. The Froude number is defined as: Wherein, Fr is the Froude number; v represents the fluid velocity; g is the acceleration of gravity; L represents the characteristic length; and the characteristics are breakwater structural parameters.

6. The method for predicting the impact pressure of an ecological breakwater based on dimensionless parameter optimization according to claim 1, characterized in that: The description of the Markov chain model for time series data is: i,j =P(X t+1 =j|X t =i); where X t and X t+1 are the states of dimensionless impact pressure at time t and time t+1 respectively; P(·|·) is the conditional transition probability; p i,j It is expressed as the probability that the dimensionless impact pressure is transferred from state i to state j in one step at time t.

7. The method for predicting the impact pressure of an ecological breakwater based on dimensionless parameter optimization according to claim 6, characterized in that: The process for determining the dimensionless shock pressure state includes the following steps: The dimensionless shock pressure states are defined in order from small to large shock pressure, including the first state, the second state, the third state and the fourth state; Arrange the time series data of the dimensionless impact pressure fitting values ​​under the optimal dimensionless parameter combination scenario from small to large, and calculate the first quartile, second quartile, and third quartile of the time series data; The obtained quartiles are used to divide the state of the dimensionless shock pressure fitting value. The division principle is: If the dimensionless shock pressure fitting value is ≤ the first quartile, the state is state 1; If the first quartile < dimensionless shock pressure fitting value < second quartile, then its state is state 2; If the second quartile ≤ dimensionless shock pressure fitting value < third quartile, then its state is state 3; If the dimensionless shock pressure fitting value is ≥ the third quartile, its state is the 4th state.

8. The method for predicting the impact pressure of an ecological breakwater based on dimensionless parameter optimization according to claim 6 is characterized in that: At time t, the probability p that the dimensionless shock pressure transfers from state i to state j in one step is i,j The calculation method is: Where, f i,j It is expressed as the frequency of the dimensionless shock pressure transferring from state i to state j in one step in the time series data of the dimensionless shock pressure fitting value under the optimal dimensionless parameter combination scenario.

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