CN111199095B - Intelligent ship berthing-alongside algorithm test environment construction method based on three-dimensional simulation - Google Patents

Intelligent ship berthing-alongside algorithm test environment construction method based on three-dimensional simulation Download PDF

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CN111199095B
CN111199095B CN201911350941.7A CN201911350941A CN111199095B CN 111199095 B CN111199095 B CN 111199095B CN 201911350941 A CN201911350941 A CN 201911350941A CN 111199095 B CN111199095 B CN 111199095B
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CN111199095A (en
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王晓原
张慧丽
夏媛媛
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Qingdao University of Science and Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M10/00—Hydrodynamic testing; Arrangements in or on ship-testing tanks or water tunnels
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Abstract

The invention discloses a method for constructing a test environment of an intelligent ship berthing-alongside algorithm based on three-dimensional simulation, which comprises the following steps: s1, acquiring a wind speed mathematical model, wind speed discrete sample information, a sea wave mathematical model and sea wave frequency spectrum information preloaded in a berthing algorithm test environment according to actual harbor basin environment information when an intelligent ship berths; s2, establishing a continuous wind speed loaded in a test environment within a specified time period according to the wind speed mathematical model and the wind speed discrete sample; s3, establishing sea wave information for loading simulation in a test environment according to the mathematical model of the sea wave and the frequency spectrum information of the sea wave; and S4, loading continuous wind speed in a specified time period and simulated sea wave information in the specified time period in the intelligent ship berthing algorithm test environment. The method overcomes the defect that stormy waves in the intelligent ship berthing algorithm test environment cannot be simulated in the prior art.

Description

Intelligent ship berthing-alongside algorithm test environment construction method based on three-dimensional simulation
Technical Field
The invention relates to the field of ship testing, in particular to a method and a system for constructing an intelligent ship berthing-alongside algorithm testing environment based on three-dimensional simulation.
Background
Shipping plays an important role in the development of national economy, and its berthing process is one of the important problems in current marine transportation. In the process of ship berthing and departing, the complexity of environmental information such as weather, hydrology and the like is difficult to estimate, the restriction of port water areas provides great test for the maneuverability of the ship and the technical capability of a pilot, and meanwhile, various uncertain factors from the ship, the environment and other ships become active, and a satisfactory control effect is difficult to obtain by adopting a classical control theory. And with the increase of the water depth, the storm flow of the harbor basin is large, the mooring stability condition is poor, and certain influence is brought to the safety of ship berthing. In order to test the ship berthing algorithm, comprehensively analyze the safety of the ship entering and exiting a port and berthing, and determine the safe berthing condition of the ship, the ship berthing environment needs to be simulated and researched.
Therefore, how to construct a comprehensive intelligent ship berthing and departing test environment and further reasonably simulate the influence of the offshore environment on the ship in the berthing and departing process becomes a technical problem to be solved at present.
Disclosure of Invention
The invention aims to provide a method for constructing a testing environment of an intelligent ship berthing algorithm based on three-dimensional simulation, which is used for solving the defect that storms in the testing environment of the intelligent ship berthing algorithm cannot be simulated in the prior art.
In order to achieve the purpose, the invention adopts the main technical scheme that:
in a first aspect, the invention provides a method for constructing a test environment of an intelligent ship berthing algorithm based on three-dimensional simulation, which comprises the following steps:
s1, acquiring a wind speed mathematical model, wind speed discrete sample information, a sea wave mathematical model and sea wave frequency spectrum information preloaded in a berthing algorithm test environment according to actual harbor basin environment information when an intelligent ship berths;
s2, establishing a continuous wind speed loaded in a test environment within a specified time period according to the wind speed mathematical model and the wind speed discrete sample;
s3, establishing wave information for loading simulation in a test environment according to the mathematical model of the wave and the frequency spectrum information of the wave;
and S4, loading the continuous wind speed in the specified time period and the simulated sea wave information in the specified time period in the intelligent ship berthing algorithm test environment.
Optionally, the step S1 includes:
s11, sampling the wind speed on the sea surface of at least one berth area of a harbor basin when an intelligent ship is close to a berth by selecting a Swemaair300 type hot-wire anemometer and a SWA03 type universal breeze speed hot-wire probe to obtain sampled wind speed information, namely wind speed discrete sample information;
storing sampled wind speed signals, namely wind speed discrete sample information;
wherein the height from the sea surface is 140-180cm during sampling.
Optionally, the step S1 includes:
s12, calculating an average wind speed value according to the sampled wind speed signal;
s13, acquiring fluctuation information of the wind speed within the calculation time according to the characteristic information of the wind speed turbulence, the wind speed average value and the sampled wind speed signal;
and S14, acquiring a wind speed mathematical model according to the fluctuation information of the wind speed, the wind speed average value and the sampled wind speed signal.
Optionally, the average wind speed in step S12
Figure BDA0002334651170000021
Comprises the following steps:
Figure BDA0002334651170000022
wherein a discrete sample of wind speed is collected { v } i } = (i =1, 2.., N) information, N being the sample capacity.
Optionally, the step S13 includes:
the wind speed turbulence degree Tu is:
Figure BDA0002334651170000031
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0002334651170000032
the instantaneous speed v is expressed as the mean wind speed->
Figure BDA0002334651170000033
And the sum of the wind speed fluctuations v', i.e.
Figure BDA0002334651170000034
The skewness S in the wind speed fluctuation information is:
Figure BDA0002334651170000035
the steepness K in the wind speed fluctuation information is as follows:
Figure BDA0002334651170000036
optionally, the step S1 further includes:
s1-1, constructing waves of the sea surface of an area where a berth is located by means of a linear superposition method according to wave shape parameters, and acquiring three-dimensional wave parameters of the waves of the sea surface;
s1-2, inputting preselected harbor basin environment information, a wind speed mathematical model and three-dimensional wave parameters, and generating a random phase angle, amplitudes of various harmonic waves and a sea surface wave height value for simulating waves;
s1-3, generating sea wave information for loading simulation in a test environment according to the random phase angle, the amplitude of each harmonic wave and the sea wave height value.
Optionally, the waveform parameters of the wave in step S1-1 include: wave crest, wave trough, wave height, wave amplitude, wave length, wave number, period, frequency, angular frequency, wave speed, initial phase and wave inclination angle.
Optionally, the step S1-2 includes:
generating a time t, sea surface equilibrium position (x) according to the following formula 0 ,y 0 ,z 0 ) The information of (a);
Figure BDA0002334651170000037
wherein A is the motion radius of amplitude, t is time, μ is waveform control factor, k is the wave number, ω is angular frequency,
Figure BDA0002334651170000038
Is the phase. />
In a second aspect, the present invention further provides a system for constructing a test environment for an intelligent ship berthing-alongside algorithm based on three-dimensional simulation, including:
the device comprises a memory and a processor, wherein at least one computer program for simulating a test environment is stored in the memory, and the processor executes the computer program, and particularly comprises the step of executing the method for constructing the intelligent ship berthing-alongside algorithm test environment based on three-dimensional simulation in the first aspect.
The beneficial effects of the invention are:
the method builds an intelligent ship berthing algorithm test environment based on a three-dimensional simulation scene, simulates the stormy waves in a real environment, and facilitates the test to be carried out in the three-dimensional scene. According to the wind speed discrete sample, the continuous wind speed in a period of time can be obtained through a wind speed mathematical model; according to a mathematical model of sea waves and the spectral characteristics of the sea waves, a linear superposition method is used for simulation, and three-dimensional random sea waves are simulated.
According to the invention, the wind speed and wave mathematical model is established, the influence of the ship berthing-dependent algorithm test environment is comprehensively analyzed, the problem of incomplete influence factors of the ship berthing-dependent algorithm test is avoided, and a more scientific and reasonable test environment is provided for the ship berthing-dependent algorithm test.
Because the three-dimensional scene is more visual compared with the electronic chart, the built test environment in the three-dimensional scene can judge the berthing accuracy and safety more easily, and the complexity of analyzing and testing the berthing capacity of the ship is reduced.
Drawings
Fig. 1 is a schematic flow chart of a method for constructing a test environment of an intelligent ship berthing-alongside algorithm based on three-dimensional simulation according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating an analysis of the ship berthing safety influencing factors according to another embodiment of the present invention;
FIGS. 3A and 3B are schematic diagrams of waveforms of sinusoidal undulations provided in accordance with an embodiment of the present invention;
fig. 4 is a schematic flow chart of acquiring three-dimensional wave parameters according to an embodiment of the present invention.
Detailed Description
For the purpose of better explaining the present invention and to facilitate understanding, the present invention will be described in detail by way of specific embodiments with reference to the accompanying drawings.
As shown in fig. 1, fig. 1 is a schematic flowchart illustrating a method for constructing a test environment for an intelligent ship berthing-alongside algorithm based on three-dimensional simulation according to an embodiment of the present invention, where the method of the present embodiment may include the following steps:
s1, acquiring a wind speed mathematical model, wind speed discrete sample information, a sea wave mathematical model and sea wave frequency spectrum information preloaded in a berthing algorithm test environment according to actual harbor basin environment information when an intelligent ship berths;
s2, establishing a continuous wind speed loaded in a test environment within a specified time period according to the wind speed mathematical model and the wind speed discrete sample;
s3, establishing sea wave information for loading simulation in a test environment according to the mathematical model of the sea wave and the frequency spectrum information of the sea wave;
and S4, loading the continuous wind speed in the specified time period and the simulated sea wave information in the specified time period in the intelligent ship berthing algorithm test environment.
In the embodiment, wind speed parameters in a period of time are obtained through a wind speed mathematical model in a three-dimensional scene, according to the mathematical model of sea waves and the spectral characteristics of the sea waves, simulation is carried out through a linear superposition method, three-dimensional random sea waves are simulated, a basis is provided for testing the intelligent ship by the berthing algorithm in a more scientific and visual mode, and the ship is safer in the berthing process.
Furthermore, in the embodiment, under the condition that the experimental material is difficult to obtain, only wind speed discrete sampling is performed, and the wind speed parameter within a period of time is obtained according to the wind speed sampling and the mathematical model, so that the difficulty in obtaining the experimental material is reduced, and the complexity of analysis is reduced.
Since wind speed is a determining factor for the formation of sea waves, the wave height of sea waves also changes significantly when the wind speed changes. In the embodiment, the influence of wind speed is combined during sea wave research, so that the obtained wave parameters are more reasonable.
The wind speed simulation and the wave simulation of the embodiment of the present invention are explained in detail with reference to fig. 2 to 4.
As shown in fig. 2, the operation of the ship for going out of the berth is a complex operation process, and an operator needs to master the ship speed, the ship position and the approaching angle before the berth according to the environmental conditions such as the wind wave flow before the berth. The tugboat is a main control means for berthing large ships, and the tugboat must be fully utilized to assist the ship control so as to ensure the berthing safety of the ship. Factors influencing the safety of the ship entering and exiting the port and the safety of berthing are many, and mainly include wind, waves, currents, the size and the number of tugboats, the size and the arrangement of a water area at the front edge of the dock, the handling performance of the ship, and the like. Wind speed and waves in the construction of an offshore environment are mainly studied in the present invention.
A) Wind speed
Compared with mechanical wind, the marine natural wind has the characteristic of changeability, the wind speed is a physical quantity, is a random quantity which changes irregularly at every moment, can obtain a certain wind speed sample, and obtains the wind speed at any moment through the mean value, the variance, the probability distribution, the correlation coefficient, the frequency spectrum function and the like.
1) Wind speed sampling
The air flow characteristic analysis is based on wind speed sampling, so the selection of the sampling object and the determination of the sampling parameters affect the analysis result. Selecting a sea surface near a certain berth for sampling wind speed, selecting a Swemaair300 type hot-wire anemometer and a SWA03 type universal breeze speed hot-wire probe by wind speed sampling equipment, sampling at a distance of 160cm from the sea surface during sampling, and automatically recording and storing the collected wind speed signals through connection of the hot-wire anemometer and a computer.
2) Mean wind speed
The instantaneous speed of any point in the air flow movement is changed at all times, and the average wind speed
Figure BDA0002334651170000061
Reflects the average level of wind speed over a period of time T, which is calculated as follows:
Figure BDA0002334651170000062
one discrete sample { v } collected for wind speed i } = (i =1, 2.. Times, N), where N is the sample capacity and the average wind speed may be calculated using the following equation:
Figure BDA0002334651170000071
3) Degree of turbulence
The instantaneous velocity v is expressed as the mean wind speed
Figure BDA0002334651170000072
And the sum of the wind speed fluctuations v', i.e.
Figure BDA0002334651170000073
The turbulence Tu reflects the relative fluctuation of the wind speed over time, which is defined as follows:
Figure BDA0002334651170000074
for discrete wind speed samples, the variance is:
Figure BDA0002334651170000075
4) Probability distribution of wind speed
The wind speed probability distribution describes distribution information of wind speed, and can be represented by making a probability distribution map according to the data frequency of the wind speed value falling in a certain interval (delta x). The probability distribution is normal for a truly completely random homogenous isotropic physical quantity, but biased for turbulent motion in which wind speed pulsations are present. This skewness can be described by skewness S and steepness K.
For discrete wind speed samples, the calculation formula is as follows: a (c)
Figure BDA0002334651170000076
Figure BDA0002334651170000077
Skewness and steepness describe the difference between the probability distribution and the normal distribution of random variables. When the skewness S is greater than zero, the probability distribution is biased to the side where the numerical value is small, which means that the time taken by a small wind speed is long for the wind speed. Steepness is the degree of smoothness or steepness of the probability distribution curve, reflecting the magnitude of the data frequency number falling in a certain interval (Δ x).
5) Autocorrelation function of wind speed
In this embodiment, the autocorrelation function of the wind speed is used to verify the wind speed.
The autocorrelation function describes a relationship between a value x (t) at a certain time t and a value x (t + τ) at another time t + τ in a random process x (i), and is defined as:
Figure BDA0002334651170000081
where N is the sample volume. If R (0) is divided by R (τ), the normalized autocorrelation function, i.e., the autocorrelation coefficient, is obtained:
Figure BDA0002334651170000082
for a time series x (i) of discrete samples, the autocorrelation function is:
Figure BDA0002334651170000083
normalizing the autocorrelation function to obtain an autocorrelation coefficient:
Figure BDA0002334651170000084
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0002334651170000085
regarding wind speed, the wind speed autocorrelation function describes the correlation between the wind speed at one moment and the wind speed at another moment, is irrelevant to the waveform of the wind speed, is only relevant to the amplitude frequency of a wind speed signal, and reflects the influence of data at any moment on future data.
The method comprises the following specific steps:
step M1: discrete samples are obtained by sampling the wind speed near the berth.
Step M2: and obtaining the average value of the wind speed through discrete samples.
Step M3: and combining the discrete samples and the wind speed average value to obtain the fluctuation and the relative fluctuation degree of the wind speed in a period of time according to the turbulence degree.
Step M4: and combining the discrete samples and the wind speed average value, obtaining a wind speed probability distribution diagram, namely a wind speed mathematical model, according to a calculation formula of skewness and steepness of the wind speed probability distribution, and outputting continuous wind speed in a period of time by using the root probability distribution diagram, namely the wind speed mathematical model.
Step M5: and verifying the improper wind speed by combining the autocorrelation function and the output instantaneous wind speed in a period of time. Taking the wind speeds x (t) and x (t + tau) at two moments with the time interval tau in N pairs of discrete samples, obtaining the autocorrelation function of the wind speed according to a formula, wherein the autocorrelation function describes the correlation between the wind speed at one moment and the wind speed at the other moment, and can be used for eliminating unrealistic wind speed parameters.
B) Wave shape
1) Linear wave theory
The linear wave theory considers that the waves are formed by overlapping infinite waves with different amplitudes, different frequencies and independent waves randomly, and the overlapping result is a steady state process with ergodicity. The most obvious characteristic of waves is represented by the periodic fluctuation of the water surface: for a certain moment, one peak and one valley appear at regular intervals; for a fixed location, there is one peak and one valley at every certain time.
In mathematics, the general expression of a simple harmonic sine wave is:
Figure BDA0002334651170000091
the waveform of the abbreviated sinusoidal fluctuation is shown in fig. 3A.
The various waveform parameters referred to in the above expressions and figures include:
a1, peak (valley): when the sea wave fluctuates, the wave surface reaches the highest (low) point;
a2, wave height H and amplitude A: the wave height is the vertical distance from the wave crest to the wave trough; the wave amplitude is half of the wave height;
a3, wavelength λ: the horizontal distance between two adjacent wave crests (or wave troughs);
a4, wave number k: represents the number of waves contained within a length of 2 pi, k =2 pi/λ;
a5, period T and frequency f: the cycle is the time required for two adjacent peaks (or valleys) to pass through a fixed point in succession; the frequency is the reciprocal of the period;
a6, angular frequency ω: represents the number of vibrations in 2 pi seconds, ω =2 pi/T =2 pi f;
a7, wave speed c: refers to the propagation velocity (also called phase velocity) of the waveform, i.e. the horizontal displacement of the peak (or trough) in unit time, c = ω/k = λ T;
a8, initial phase
Figure BDA0002334651170000101
a9, wave inclination angle a:
Figure BDA0002334651170000102
2) Linear superposition method
The construction of sea waves is achieved using a linear superposition method. The linear superposition method is that sea wave is regarded as being formed by superposition of infinite cosine waves with different amplitudes, different periods (or frequencies) and different random phases, and the calculation of the randomly changed ocean surface state is based on multiple summation of different wave frequency and different wave propagation directions. After the characteristic parameters (amplitude, period, random phase, etc.) of each component wave are obtained, the random process can be implemented, and in this embodiment, a linear iterative method is used to numerically simulate the frequency spectrum of the multi-directional irregular wave.
3) Three-dimensional sea wave simulation analysis
The energy of sea waves is mainly concentrated in a narrow frequency domain: when the wind speed is small, the wave energy distribution is wide, and the frequency spectrum range is wide; on the contrary, when the wind speed is high, the wave energy distribution is relatively concentrated, and the spectrum range is narrow. The energy of the component waves with particularly high and low frequencies is small, and the influence of these component waves can be disregarded in the calculation.
According to the convention of modeling coordinate system, the sea level at rest is assumed to be xoz plane, and the y-axis is vertically upward. At time t, the water particle wave-propagates with a as the motion radius (i.e. amplitude), and the motion equation is described as follows:
Figure BDA0002334651170000103
wherein x is 0 、y 0 The coordinates of the equilibrium position of the water particles on the x and y axes when the sea level is static
In order to better control the waveform, a waveform control factor mu is introduced, and the steepness of the wave is adjusted and controlled by changing the value of mu, so that the formula can be rewritten as follows:
Figure BDA0002334651170000111
4) Selection and division of spectral ranges
According to the selected target spectrum S (omega), frequency is divided by a frequency division method and an energy division method, the selection of the size of the frequency division number M influences the simulation precision and efficiency because the energy of the sea wave spectrum is generally concentrated near the peak frequency, if the size of M is not large enough, only a few sampling points are positioned near the peak frequency, and thus the component waves play a leading role in superposition. When the superposition result is applied to some specific problems (such as wave refraction problems), a large error is caused, so that an energy bisection method based on the power density of the waves is adopted in the actual calculation. Firstly, a sampling point of the frequency is selected, so that the energy of each frequency interval is equal, namely, the sub-areas under the spectral density curve are equal, and then the central frequency of each sub-area is further determined.
First the spectral range (omega) min ～ω max ) Divided into m intervals of Δ ω i ＝ω i -ω i-1 By accumulating the energy spectrum formula:
Figure BDA0002334651170000112
wherein, S (ω) is the power spectral density of the sea wave, and the total energy of the obtained frequency spectrum is:
Figure BDA0002334651170000113
then:
Figure BDA0002334651170000114
wherein m is 0 Is the zero-order moment of the spectrum,
Figure BDA0002334651170000115
will only be higher than the sense wave
Figure BDA0002334651170000116
Associated P-M frequency equation>
Figure BDA0002334651170000117
Substituting the above formula to obtain the boundary frequency omega i The expression of (c) is: />
Figure BDA0002334651170000121
Representative angular frequency of
Figure BDA0002334651170000122
5) Selection and division of range of direction angles
The range of the direction angle is (pi, pi) theoretically, but as the energy of the sea wave is collected in a narrow frequency band, the main energy is actually distributed in the range of pi/2 on both sides of the main wave direction, only the range of [ -pi/2, pi/2 is considered]And (4) finishing. The division of the range of the direction angle adopts a method of 'equal interval division', namely, the range of the direction angle is averagely divided into n intervals, and the interval is delta theta = pi/n, so that the direction angle theta is divided j The expression of (a) is:
Figure BDA0002334651170000123
in the formula (I), the compound is shown in the specification,
Figure BDA0002334651170000124
is the main wave direction.
Representing angle of direction
Figure BDA0002334651170000125
6) Calculation of amplitude
For a component wave for which the angular frequency interval Δ ω and the angular directional moment Δ θ have been determined, the energy of the component wave is
Figure RE-GDA0002406900390000126
The following relationship exists with the direction spectrum function S (ω, θ) of sea waves:
Figure RE-GDA0002406900390000127
after substituting the angular frequency and the direction angle, the unit wave amplitudes of the component waves corresponding to different frequencies and different direction angles can be approximately solved according to the relational expression of the wave spectrum and the wave amplitudes:
Figure BDA0002334651170000128
through the analysis and calculation of the sea wave parameter sampling method, the final formula is obtained
Figure BDA0002334651170000131
Solving t moment, the sea surface balance position is (x) 0 ,y 0 ,z 0 ) Amplitude, wavenumber, angular frequency, and initial phase.
Therefore, the energy of the sea waves is concentrated in a narrow frequency band, the energy of the component waves with particularly high frequency and particularly low frequency is very small, and the influence of the component waves can be not considered in calculation. When the wind speed is different, the frequency of the spectral peak changes, the spectral value curve moves, and according to the energy equal division method and the principle of the analysis, after the frequency interval number N is determined, the frequency sampling point is also determined accordingly. When sea surface wind speeds are different values, adopting a sampling frequency range of an energy equal division method, W min To sample the lower frequency limit, W max Upper limit of sampling frequency, W p Is the spectral peak frequency.
Figure BDA0002334651170000132
The method for obtaining the three-dimensional wave parameters based on the foregoing description in this embodiment is shown in fig. 4.
Because the wind speed is a determining factor for the formation of sea waves, when a wind field acts on the sea surface, sea surface wind can continuously convey energy to the sea surface, and wind waves are generated. When the wind speed changes, the wave height of the sea waves also changes obviously. Meanwhile, the pressure of wind on the wave surface and continuous energy supply can enable wind waves to continuously grow, so that the sea surface condition is directly influenced by the action of sea surface wind, and the fluctuation of the sea surface wave height is determined by the height of the sea surface wind. When the wind speed of the sea surface is low and the sea condition is low, the sea waves are stable and have small fluctuation, and the generated sea surface disturbance is mainly capillary waves; when the wind speed is high and the sea state is high, large fluctuation changes of wave height are generated. The sea surface has obvious and big wave crests and wave troughs, and the big wave height is an obvious characteristic of the sea surface.
The above description of the embodiments of the present invention is provided for the purpose of illustrating the technical lines and features of the present invention and is provided for the purpose of enabling those skilled in the art to understand the contents of the present invention and to implement the present invention, but the present invention is not limited to the above specific embodiments. It is intended to cover in the appended claims all such changes and modifications that are within the scope of this invention.

Claims (5)

1. A method for constructing an intelligent ship berthing and departing algorithm test environment based on three-dimensional simulation is characterized by comprising the following steps:
s1, acquiring a wind speed mathematical model, wind speed discrete sample information, a sea wave mathematical model and sea wave frequency spectrum information preloaded in a berthing algorithm test environment according to actual harbor basin environment information when an intelligent ship berths; the S1 comprises:
s11, sampling the wind speed on the sea surface of at least one berth area of a harbor basin when an intelligent ship is close to a berth by selecting a Swemaair300 type hot-wire anemometer and a SWA03 type universal breeze speed hot-wire probe to obtain sampled wind speed information, namely wind speed discrete sample information;
storing sampled wind speed signals, namely wind speed discrete sample information;
wherein the height from the sea surface is 140-180cm during sampling;
s12, calculating an average wind speed value according to the sampled wind speed signal;
s13, acquiring fluctuation information of the wind speed within the calculation time according to the characteristic information of the wind speed turbulence, the wind speed average value and the sampled wind speed signal;
the S13 comprises:
the wind speed turbulence degree Tu is:
Figure FDA0004115585780000011
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0004115585780000012
the instantaneous speed v is expressed as the mean wind speed->
Figure FDA0004115585780000013
And the sum of the wind speed fluctuations v', i.e.
Figure FDA0004115585780000014
The skewness S in the wind speed fluctuation information is:
Figure FDA0004115585780000015
the steepness K in the wind speed fluctuation information is as follows:
Figure FDA0004115585780000016
s14, acquiring a wind speed mathematical model according to the fluctuation information of the wind speed, the wind speed average value and the sampled wind speed signal;
s2, establishing a continuous wind speed loaded in a test environment within a specified time period according to the wind speed mathematical model and the wind speed discrete sample;
s3, establishing wave information for loading simulation in a test environment according to the mathematical model of the wave and the frequency spectrum information of the wave;
and S4, loading continuous wind speed in a specified time period and simulated sea wave information in the specified time period in the intelligent ship berthing algorithm test environment.
2. The method of claim 1, wherein the average wind speed in S12
Figure FDA0004115585780000021
Comprises the following steps:
Figure FDA0004115585780000022
whereinOne discrete sample of wind speed acquisition { v } i } = (i =1, 2.., N) information, N being the sample capacity.
3. The method of claim 1, wherein S1 further comprises:
s1-1, constructing waves of the sea surface of an area where a berth is located by means of a linear superposition method according to wave shape parameters of the waves, and acquiring three-dimensional wave parameters of the waves of the sea surface;
s1-2, inputting preselected harbor basin environment information, a wind speed mathematical model and three-dimensional wave parameters, and generating a random phase angle for simulating waves, amplitudes of various harmonic waves and a sea surface wave height value;
and S1-3, generating sea wave information for loading simulation in a test environment according to the random phase angle, the amplitude of each harmonic wave and the sea wave height value.
4. The method of claim 3, wherein the waveform parameters of the waves in S1-1 comprise: wave crest, wave trough, wave height, wave amplitude, wave length, wave number, period, frequency, angular frequency, wave speed, initial phase and wave inclination angle.
5. An intelligent ship berthing and departing algorithm test environment construction system based on three-dimensional simulation is characterized by comprising the following steps:
the intelligent ship berthing algorithm test environment construction method based on three-dimensional simulation comprises the following steps of storing at least one computer program for simulating the test environment in the memory, and executing the computer program by the processor, wherein the method specifically comprises the step of executing the intelligent ship berthing algorithm test environment construction method based on three-dimensional simulation according to any one of claims 1 to 4.
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