A calculation method for the offshore roughness attenuation factor

Abstract

The present invention relates to the field of marine atmospheric duct environment detection, and discloses a calculation method for the roughness attenuation factor at sea, which includes the following steps: measuring the meteorological and hydrological data of the target sea area; installing a microwave link in the target sea area and calculating the true value of the electromagnetic wave propagation loss; calculating the roughness attenuation factor of the target sea area, dividing the wind speed into multiple optimized intervals; constructing a weighted roughness attenuation factor prediction model. For each divided optimized interval, taking the root mean square error between the predicted value and the true value of the electromagnetic wave propagation loss as the fitness function of the particle swarm optimization algorithm, and iteratively obtaining the optimal weight coefficient of the prediction model; using the optimal prediction model combination to calculate the roughness attenuation factor under different wind speeds. The method disclosed by the present invention weights different roughness attenuation factor models and optimizes through the particle swarm algorithm to obtain a roughness attenuation factor prediction model with higher applicability, which can be applicable to the sea surface roughness conditions under different wind speeds.

Description

Technical Field

[0001] The present invention relates to the field of marine atmospheric duct environment detection, and particularly to a calculation method for the roughness attenuation factor at sea. Background Art

[0002] In recent years, radio technology has developed rapidly, and people's lifestyles have also been revolutionized. Understanding the propagation characteristics of radio waves can provide theoretical and technical support for various communication and navigation systems. Electromagnetic wave propagation loss is an important characteristic of radio wave propagation. Electromagnetic wave propagation loss is of great significance for maritime communication and marine resource development. In the tropospheric duct, the propagation loss of radar electromagnetic waves is usually weaker than that in the normal atmospheric environment, resulting in over-the-horizon propagation, thus expanding the detection range of the radar. However, since the electromagnetic waves are trapped in the duct layer, the distribution of electromagnetic wave energy in the upper region of the duct layer is less, resulting in unexpected holes, which reduces the target detection performance of the radar.

[0003] When calculating the propagation loss of electromagnetic waves at sea, the roughness attenuation factor is a very important parameter. Currently, the roughness attenuation factor is mainly calculated by three calculation models, namely the Ament model, the Miller Brown model, and the Shadowed model. Due to the complex and changeable conditions at sea, the sea surface roughness will change according to the change of wind speed. Since different models are applicable to different sea surface roughnesses when calculating the sea surface roughness attenuation factor, the calculation of the roughness attenuation factor using a single model will be inaccurate, thus affecting the calculation of electromagnetic wave propagation loss. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a calculation method for the roughness attenuation factor at sea, which weights different roughness attenuation factor models to obtain a roughness attenuation factor prediction model with higher applicability in the target sea area, and can be applicable to the sea surface roughness conditions under different wind speeds.

[0005] To achieve the above object, the technical solution of the present invention is as follows:

[0006] A calculation method for the roughness attenuation factor at sea, comprising the following steps:

[0007] Step 1, using a floating platform to install meteorological and hydrological sensors to measure the meteorological and hydrological data of the target sea area;

[0008] Step 2, installing a microwave link in the target sea area, and calculating the true value of electromagnetic wave propagation loss through the power loss between the receiving end and the transmitting end;

[0009] Step 3: Calculate the roughness attenuation factor of the target sea area using the Ament model, Miller Brown model, and Shadowed model respectively. Taking the slope of the calculated roughness attenuation factor curve as the standard, divide the wind speed into multiple optimization intervals.

[0010] Step 4: Substitute the meteorological and hydrological data into the evaporation duct prediction model to obtain the evaporation duct height, and then calculate the evaporation duct atmospheric modified refractive index profile using the evaporation duct height.

[0011] Step 5: Use the Ament model, Miller Brown model, and Shadowed model to construct a weighted roughness attenuation factor prediction model. For each divided optimization interval, calculate the predicted value of the electromagnetic wave propagation loss by combining the roughness attenuation factor calculated by the prediction model with the evaporation duct atmospheric modified refractive index profile. Take the root mean square error between the predicted value of the electromagnetic wave propagation loss and the true value of the electromagnetic wave propagation loss as the fitness function of the particle swarm optimization algorithm, and iteratively obtain the optimal weight coefficient of the prediction model; Use the same method for the multiple divided optimization intervals to iteratively find the optimal weight coefficient for each optimization interval to obtain the optimal prediction model combination.

[0012] Step 6: Calculate the roughness attenuation factor at different wind speeds using the optimal prediction model combination.

[0013] In the above solution, in Step 1, the measured meteorological and hydrological data include the wind speed, temperature, humidity, atmospheric pressure, sea surface skin temperature, and wavelength of the target sea area.

[0014] In the above solution, in Step 2, install the receiving end of the microwave link on the floating platform, and install the transmitting end of the microwave link on the shore 150 km away from the floating platform.

[0015] In the above solution, in Step 3, the true value of the electromagnetic wave propagation loss is calculated by the following formula:

[0016] ;

[0017] where is the transmit power, is the transmit antenna gain, is the receive power, is the receive antenna gain, is the loss of the feeder.

[0018] In the above solution, in Step 5, the constructed roughness attenuation factor prediction model is as follows:

[0019] ;

[0020] where represents the roughness attenuation factor calculated by the Ament model, represents the roughness attenuation factor calculated by the Miller Brwon model, represents the roughness attenuation factor calculated by the Shadowed model, represents the weighted roughness attenuation factor.

[0021] In the above solution, in step 5, the method for calculating the predicted value of the electromagnetic wave propagation loss is as follows:

[0022] (1) Calculate the modified reflection coefficient using the roughness attenuation factor calculated by the prediction model : :

[0023] ;

[0024] where, is the Fresnel reflection coefficient, representing the influence of the rough sea surface on the scattering of electromagnetic waves;

[0025] (2) Calculate the surface impedance coefficient :

[0026] ;

[0027] where, is the local surface grazing angle, is the free space wave number, i is a complex number;

[0028] (3) Determine the impedance boundary condition of the parabolic equation:

[0029] ;

[0030] The expression of the parabolic equation under the given evaporation duct condition is as follows:

[0031] ;

[0032] where, represents the horizontal polarization electric field component and the vertical polarization electric field component, is the height, is the horizontal distance of the sea surface, is the evaporation duct atmospheric modified refractive index profile;

[0033] (4) Use the split-step Fourier algorithm to solve the parabolic equation. In each step, perform the Fourier transform operation in combination with the impedance boundary condition, and then multiply by the atmospheric refractive index to finally obtain , and finally obtain the predicted value of the electromagnetic wave propagation loss :

[0034] ;

[0035] Among them, is the radio wave frequency.

[0036] In a further technical solution, the calculation method of the atmospheric refraction index is as follows:

[0037] First, the atmospheric refractive index N is obtained according to the following relationship between the evaporation duct atmospheric modified refractive index and the atmospheric refractive index N:

[0038] ;

[0039] Among them, is the evaporation duct atmospheric modified refractive index at height , a is the average earth radius; z is the height;

[0040] Then, the atmospheric refraction index n is calculated according to the following relationship between the atmospheric refractive index N and the atmospheric refraction index n:

[0041] .

[0042] In the above solution, in step 5, the method for iteratively obtaining the optimal weight coefficient of the prediction model by using the particle swarm optimization algorithm is as follows:

[0043] S1: Initialize the particle swarm parameters: In the roughness attenuation factor prediction model, the weight coefficient is 3, and N 3D particles are randomly generated in the search space as the initial particle swarm;

[0044] S2: Calculate the fitness value:

[0045] ;

[0046] Among them, is the fitness value, is the true value of the electromagnetic wave propagation loss of the th particle, is the predicted value of the electromagnetic wave propagation loss of the th particle, is the number of particles;

[0047] S3: Update the particle velocity and position according to the fitness value. The position of the th particle is , represents the position of the th particle in the second dimension; the velocity of the th particle is , represents the The velocity of a particle in the second dimension; the optimal position searched by the th particle is ; represents the optimal position of the th particle in the second dimension; the optimal position searched by the particle swarm is ;

[0048] The velocity and position of each particle after iteration are updated by the following formulas:

[0049] ;

[0050] ;

[0051] where k is the number of iterations, is the inertia weight, ; is the learning factor, and its value range is [0, 2]; is a random number between 0 and 1; represents the optimal position of the th particle searched in the second dimension after k iterations; represents the velocity of the th particle in the second dimension after k + 1 iterations, represents the position of the th particle in the second dimension at the kth iteration, represents the position of the th particle in the second dimension at the (k + 1)th iteration; the calculation methods of the positions and velocities of the particles in the first and third dimensions after each iteration are the same as those in the second dimension;

[0052] S4: Repeat steps S2 and S3 until the maximum number of iterations is reached, and obtain the optimal positions searched by the th particle and the optimal position searched by the particle swarm ; in correspond to the three optimal weight coefficients of the prediction model.

[0053] In the above solution, the specific method of step 6 is as follows: First, obtain the wind speed in the target sea area, then determine which optimization interval it belongs to according to the wind speed, and then select the optimized prediction model of this optimization interval to calculate the roughness attenuation factor.

[0054] Through the above technical solution, a calculation method for the sea roughness attenuation factor provided by the present invention has the following beneficial effects:

[0055] 1. The present invention weights different roughness attenuation factor models to obtain a roughness attenuation factor prediction model with higher applicability in the target sea area. This model combines different models with weights and can be applicable to different sea surface roughness situations caused by different wind speeds.

[0056] 2. The present invention uses a floating platform to measure the air temperature, air relative humidity, wind speed, sea skin temperature, and wavelength in the target sea area, obtains the evaporation duct atmospheric correction refractive index profile using the evaporation duct prediction model, determines the parabolic equation under given conditions, and calculates the predicted value of the electromagnetic wave propagation loss in different wind speed intervals according to the weighted model. It can measure and calculate the roughness attenuation factor in different wind speed situations in real time, thereby calculating the sea surface electromagnetic wave propagation loss value.

[0057] 3. The measurement area of the present invention is at sea, and the sea evaporation duct is a very common abnormal phenomenon, which has a great influence on the propagation of electromagnetic waves. Therefore, the electromagnetic wave propagation loss in the case of the evaporation duct is considered when calculating the electromagnetic wave propagation loss, improving the applicability of the model at sea.

[0058] 4. The present invention iterates the weighted and fused roughness attenuation factor prediction model through the particle swarm optimization algorithm for multiple rounds, and finally obtains the roughness attenuation factor prediction model with the optimal weight combination, improving the accuracy of the model in calculating the roughness attenuation factor in the target sea area, thereby improving the accuracy of the electromagnetic wave propagation loss calculation result.

[0059] 5. Since the roughness attenuation factor cannot be directly measured, the present invention uses the calculation of the electromagnetic wave propagation loss by the roughness attenuation factor as the predicted value in the fitness function of the particle swarm optimization algorithm, calculates the true value of the electromagnetic wave propagation loss using the power loss between the transmitting end and the receiving end of the microwave link, and optimizes using the root mean square error between the two as the fitness, avoiding the situation where it cannot be directly optimized due to the inability to measure the true value of the roughness attenuation factor, and realizing the optimization of the roughness attenuation factor using an intermediate quantity. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art.

[0061] Figure 1 It is a schematic flowchart of a calculation method for the sea roughness attenuation factor disclosed in an embodiment of the present invention;

[0062] Figure 2 It is a schematic diagram of the roughness attenuation factor within the wind speed of 0 - 15M / S disclosed in an embodiment of the present invention;

[0063] Figure 3A schematic diagram of a particle swarm optimization algorithm disclosed in an embodiment of the present invention. Detailed implementation manners

[0064] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention.

[0065] The present invention provides a calculation method for the sea roughness attenuation factor, as Figure 1 shown, including the following steps:

[0066] Step 1: Use a floating platform to install meteorological and hydrological sensors to measure the meteorological and hydrological data of the target sea area.

[0067] Install meteorological and hydrological sensors on the buoy platform to measure the meteorological and hydrological data of the target sea area; install a wind speed sensor to measure the wind speed of the target sea area, install a temperature and humidity sensor to measure the temperature and relative humidity of the target sea area, install an atmospheric pressure sensor to measure the atmospheric pressure of the target sea area, install an infrared sensor to measure the sea surface temperature, and use the wave sensor of the buoy itself to measure the wavelength.

[0068] Step 2: Install a microwave link in the target sea area, and calculate the true value of the electromagnetic wave propagation loss through the power loss between the receiving end and the transmitting end.

[0069] In the present invention, a receiving end of a microwave link is installed on a floating platform, and a receiving end of a microwave link is installed on the shore within 150 km. The true electromagnetic wave propagation loss is calculated through the power loss between the receiving end and the transmitting end.

[0070] True value of electromagnetic wave propagation loss Is calculated by the following formula:

[0071] ;

[0072] Wherein, Is the transmit power, Is the transmit antenna gain, Is the receive power, Is the receive antenna gain, Is the loss of the feeder.

[0073] Step 3: Use the Ament model, the Miller Brown model, and the Shadowed model to calculate the roughness attenuation factor of the target sea area respectively. Based on the slope of the calculated roughness attenuation factor curve, the wind speed is divided into multiple optimization intervals.

[0074] The three roughness attenuation factor calculation models are respectively:

[0075] Roughness attenuation factor calculation model Ament model:

[0076] ;

[0077] Roughness attenuation factor calculation model, Miller Brown model:

[0078] ;

[0079] Roughness attenuation factor calculation model, Shadowed model:

[0080] ;

[0081] ;

[0082] Among them, is the surface Rayleigh roughness parameter, represents the wave number, , is the wavelength; is the grazing angle, which is estimated using the method of geometric optics and is the angle between the radar transmitting wave and the sea level, is the root mean square height deviation of the sea surface, which can be calculated using the wind speed. The formula is , among which, represents the wind speed measured by the wind speed sensor on the floating platform, is the modified Bessel function of order zero.

[0083] Substitute the wind speed, wavelength, and grazing angle into the above formula to obtain the roughness attenuation factor curve as shown in Figure 2 . The optimization interval can be divided into multiple intervals. Among them, when the wind speed is 0 m / s - 4 m / s, the slope is not large. Therefore, the wind speed range of 0 m / s - 4 m / s can be divided into one optimization interval. When the wind speed is 4 m / s - 11 m / s, the slope of the roughness attenuation factor curve is very large, and each wind speed can be used as an optimization interval. When the wind speed is 11 m / s - 15 m / s, the slope of the roughness attenuation curve is very small. Therefore, 11 m / s - 15 m / s is used as one optimization interval.

[0084] Step 4: Substitute the meteorological and hydrological data into the evaporation duct prediction model to obtain the evaporation duct height, and then calculate the evaporation duct atmospheric modified refractive index profile using the evaporation duct height.

[0085] Substitute the meteorological and hydrological parameters measured in Step 1, namely wind speed, temperature, humidity, atmospheric pressure, and sea surface skin temperature, into the evaporation duct prediction model to obtain the evaporation duct height , and then the calculated evaporation duct height Substitute it into the mathematical expression of the evaporation duct atmospheric modified refractive index profile to obtain the evaporation duct atmospheric modified refractive index profile. The mathematical expression of the evaporation duct atmospheric modified refractive index profile is shown as follows:

[0086] ;

[0087] where, is the atmospheric modified refractive index of the sea surface, generally regarded as 350, is the height where the evaporation duct atmospheric modified refractive index is located, is the evaporation duct height, and the roughness length . The evaporation duct atmospheric modified refractive index profile in the measured target sea area can be expressed as , is the horizontal distance of the measured target sea area.

[0088] Step 5: Use the Ament model, Miller Brown model, and Shadowed model to construct a weighted roughness attenuation factor prediction model. For each optimized interval divided, use the roughness attenuation factor calculated by the prediction model to combine with the evaporation duct atmospheric modified refractive index profile to calculate the predicted value of the electromagnetic wave propagation loss. Take the root mean square error between the predicted value of the electromagnetic wave propagation loss and the true value of the electromagnetic wave propagation loss as the fitness function of the particle swarm optimization algorithm, and iteratively obtain the optimal weight coefficient of the prediction model; use the same method for multiple optimized intervals divided to iteratively find the optimal weight coefficient of each optimized interval to obtain the optimal prediction model combination.

[0089] The first step: Construct a roughness attenuation factor prediction model:

[0090] The constructed roughness attenuation factor prediction model is as follows:

[0091] ;

[0092] where, represents the roughness attenuation factor calculated by the Ament model, represents the roughness attenuation factor calculated by the Miller Brwon model, represents the roughness attenuation factor calculated by the Shadowed model, represents the weighted roughness attenuation factor.

[0093] The second step: Calculate the predicted value of the electromagnetic wave propagation loss:

[0094] The method for calculating the predicted value of the electromagnetic wave propagation loss is as follows:

[0095] (1) Roughness attenuation factor calculated using the prediction model Calculate the corrected reflection coefficient :

[0096] ;

[0097] Among them, is the Fresnel reflection coefficient, indicating the influence of the rough sea surface on the scattering of electromagnetic waves;

[0098] (2) Calculate the surface impedance coefficient :

[0099] ;

[0100] Among them, is the local surface grazing angle, is the free space wave number, i is a complex number;

[0101] (3) Determine the impedance boundary condition of the parabolic equation:

[0102] ;

[0103] The expression of the parabolic equation under the given evaporation duct condition is as follows:

[0104] ;

[0105] Among them, represents the horizontal polarization electric field component and the vertical polarization electric field component, is the height, is the horizontal distance of the sea surface, is the evaporation duct atmospheric correction refractive index profile;

[0106] (4) Use the split-step Fourier algorithm to solve the parabolic equation. In each step, perform the Fourier transform operation in combination with the impedance boundary condition, and then multiply by the atmospheric refractive index to finally obtain , and finally obtain the predicted value of the electromagnetic wave propagation loss :

[0107] ;

[0108] Among them, is the radio wave frequency.

[0109] The calculation method of the atmospheric refractive index is as follows:

[0110] First, obtain the atmospheric refractive index N according to the following relationship between the evaporation duct atmospheric correction refractive index and the atmospheric refractive index N:

[0111] ;

[0112] Among them, is the evaporation duct atmospheric modified refractive index at height , a is the average Earth radius; z is the height;

[0113] Then, calculate the atmospheric refractive index n according to the following relationship between the atmospheric refractive index N and the atmospheric refraction index n:

[0114] .

[0115] Step 3: Use the particle swarm optimization algorithm to iteratively obtain the optimal weight coefficients of the prediction model:

[0116] As Figure 3 shown, the method for using the particle swarm optimization algorithm to iteratively obtain the optimal weight coefficients of the prediction model is as follows:

[0117] S1: Initialize the particle swarm parameters: In the roughness attenuation factor prediction model, the weight coefficient is 3, and N 3D particles are randomly generated in the search space as the initial particle swarm;

[0118] S2: Calculate the fitness value:

[0119] ;

[0120] Among them, is the fitness value, is the true value of the electromagnetic wave propagation loss of the th particle, is the predicted value of the electromagnetic wave propagation loss of the th particle, is the number of particles;

[0121] S3: Update the particle velocity and position according to the fitness value. The position of the th particle is , represents the position of the th particle in the second dimension; the velocity of the th particle is , represents the velocity of the th particle in the second dimension; the optimal position searched by the th particle is , represents the optimal position of the th particle in the second dimension; the optimal position searched by the particle swarm is , represents the optimal position of the particle swarm in the second dimension;

[0122] The velocity and position after each particle iteration are updated by the following formulas:

[0123] ;

[0124] ;

[0125] where k is the number of iterations, is the inertia weight, , are the learning factors, and their value ranges are [0, 2], is a random number between 0 and 1, represents the optimal position in the second dimension searched by the -th particle after k iterations; represents the velocity of the -th particle in the second dimension after k + 1 iterations, represents the position of the -th particle in the second dimension at the k-th iteration, represents the position of the -th particle in the second dimension at the k + 1-th iteration; the calculation methods of the positions and velocities of the first and third dimensions of the particle after each iteration are the same as those of the second dimension;

[0126] S4: Repeat steps S2 and S3 until the maximum number of iterations is reached, and obtain the optimal position searched by the -th particle and the optimal position searched by the particle swarm , in , which correspond to the three optimal weight coefficients of the prediction model.

[0127] Step 6, use the optimal prediction model combination to calculate the roughness attenuation factor under different wind speeds.

[0128] The specific method is as follows: First, obtain the wind speed of the target sea area, then determine which optimization interval it belongs to according to the wind speed, and then select the optimized prediction model of this optimization interval to calculate the roughness attenuation factor.

[0129] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for calculating the marine roughness attenuation factor, characterized in that: The steps include: Step 1: Use a floating platform to install meteorological and hydrological sensors to measure meteorological and hydrological data of the target sea area; Step 2: Install a microwave link in the target sea area and calculate the true value of electromagnetic wave propagation loss through the power loss between the receiving end and the transmitting end; Step 3, using the Ament model, Miller Brown model, and Shadowed model to calculate the roughness attenuation factor of the target sea area, and dividing the wind speed into multiple optimization intervals based on the slope of the calculated roughness attenuation factor curve; Step 4, substituting the meteorological and hydrological data into the evaporation duct prediction model to obtain the evaporation duct height, and then using the evaporation duct height to calculate the evaporation duct atmospheric correction refractive index profile; Step 5, using the Ament model, Miller Brown model, and Shadowed model to weight and construct a roughness attenuation factor prediction model, for each divided optimization interval, using the roughness attenuation factor calculated by the prediction model combined with the atmospheric correction refractive index profile of the evaporation waveguide to calculate the predicted value of electromagnetic wave propagation loss, the root mean square error between the predicted value of electromagnetic wave propagation loss and the true value of electromagnetic wave propagation loss is used as the fitness function of the particle swarm optimization algorithm, and the optimal weight coefficient of the prediction model is iteratively obtained; for the multiple divided optimization intervals, the same method is used to iterate to find the optimal weight coefficient of each optimization interval, and the optimal prediction model combination is obtained; Step 6: Calculate the roughness attenuation factor at different wind speeds using the optimal prediction model combination.

2. The method for calculating the marine roughness attenuation factor according to claim 1, characterized in that: In step 1, the measured meteorological and hydrological data include wind speed, temperature, humidity, atmospheric pressure, sea surface temperature, and wavelength of the target sea area.

3. The method for calculating the marine roughness attenuation factor according to claim 1, characterized in that: In step 2, a receiving end of the microwave link is installed on the floating platform, and a transmitting end of the microwave link is installed on the shore 150 km away from the floating platform.

4. The method for calculating the marine roughness attenuation factor according to claim 1, characterized in that: In step 3, the true value of electromagnetic wave propagation loss Calculated by the following formula: ； in, is the transmission power, is the transmit antenna gain, is the received power, is the receiving antenna gain, is the loss of the feeder line.

5. The method for calculating the marine roughness attenuation factor according to claim 1, characterized in that: In step 5, the roughness attenuation factor prediction model constructed is as follows: ； in, Represents the roughness attenuation factor calculated by the Ament model, Represents the roughness attenuation factor calculated by the Miller Brown model, Represents the roughness attenuation factor calculated by the Shadowed model. Represents the weighted roughness attenuation factor.

6. The method for calculating the marine roughness attenuation factor according to claim 1, characterized in that: In step 5, the method for calculating the predicted value of electromagnetic wave propagation loss is as follows: (1) Roughness attenuation factor calculated using the prediction model Calculate the corrected reflection coefficient : ； in, is the Fresnel reflection coefficient, which represents the effect of the rough sea surface on the scattering of electromagnetic waves; (2) Calculation of surface impedance coefficient : ； in, is the local surface grazing angle, is the free space wave number, i is plural; (3) Determine the impedance boundary conditions of the parabolic equation: ； The parabolic equation expression under given evaporation waveguide conditions is as follows: ； in, represents the horizontally polarized electric field component and the vertically polarized electric field component, is the height, is the horizontal distance above sea level, Corrected refractive index profile for evaporative waveguide atmosphere; (4) The parabolic equation is solved using the step-by-step Fourier algorithm. In each step, the Fourier transform operation is performed in combination with the impedance boundary condition, and then multiplied by the atmospheric refractive index to obtain Finally, the predicted value of electromagnetic wave propagation loss is obtained : ； in, The frequency of the radio wave.

7. A method for calculating the marine roughness attenuation factor according to claim 6, characterized in that: The atmospheric refractive index is calculated as follows: First, the atmospheric refractive index N is calculated based on the following relationship between the atmospheric corrected refractive index of the evaporation waveguide and the atmospheric refractive index N: ； in, For height The atmospheric corrected refractive index of the evaporation waveguide at a is the average radius of the Earth; z is the height; Then, the atmospheric refractive index n is calculated according to the following relationship between the atmospheric refractive index N and the atmospheric refractive index n: 。 8. The method for calculating the offshore roughness attenuation factor according to claim 1, characterized in that: In step 5, the method of iteratively obtaining the optimal weight coefficient of the prediction model using the particle swarm optimization algorithm is as follows: S1: Initialize particle swarm parameters: In the roughness attenuation factor prediction model, the weight coefficient is 3, and N 3D particles are randomly generated in the search space as the initial particle swarm; S2: Calculate the fitness value: ； in, is the fitness value, For the The true value of the electromagnetic wave propagation loss of each particle, For the The predicted value of electromagnetic wave propagation loss for each particle, is the number of particles; S3: Update particle speed and position according to fitness value. The position of a particle is , Indicates The position of a particle in the second dimension; The speed of a particle is , Indicates The speed of a particle in the second dimension; The optimal position searched by a particle is , Indicates The optimal position of a particle in the second dimension; the optimal position searched by the particle swarm is , Indicates the optimal position of the particle swarm in the second dimension; The speed and position of each particle after iteration are updated using the following formula: ； ； Where k is the number of iterations, is the inertia weight, , is the learning factor, and its value range is [0,2]. is a random number between 0 and 1, Representative The particle iterates k times to search for the optimal position in the second dimension; Representative The speed of a particle iterating k+1 times in the second dimension, Representative The position of a particle in the second dimension after k iterations, Representative The position of a particle in the second dimension after iterating k+1 times; the calculation method of the position and velocity of the particle in the first and third dimensions after each iteration is the same as that of the second dimension; S4: Repeat steps S2 and S3 until the maximum number of iterations is reached and the The optimal position searched by a particle and the optimal position searched by a particle swarm , In Corresponding to the 3 optimal weight coefficients of the prediction model.

9. The method for calculating the marine roughness attenuation factor according to claim 1, characterized in that: The specific method of step 6 is as follows: first, obtain the wind speed of the target sea area, then determine which optimization interval it belongs to based on the wind speed, and then select the optimized prediction model of the optimization interval to calculate the roughness attenuation factor.

Images