A mooring load prediction method based on wind wave identification

Abstract

The application discloses a mooring load prediction method based on wind wave identification, relates to the technical field of mooring load prediction, and solves the technical problem that the prior art cannot accurately distinguish chaotic wind waves and regular swell, thereby reducing the mooring load prediction accuracy, specifically comprises the following steps: dominant factor identification, dominant factor identification is performed on the current sea area to distinguish whether wind or wave is the dominant factor; multi-type identification control, wind wave types are distinguished based on sea area data analysis, and mooring load prediction control is performed according to the wind wave types; and navigation transportation interference identification, interference identification is performed on navigation transportation in the sea area, which breaks through the technical bottleneck of traditional single static prediction, non-differentiated regulation and control and weak anti-interference capability, greatly improves the mooring load prediction accuracy and risk early warning accuracy under complex sea conditions and navigation interference scenes, effectively reduces mooring cable loss and the probability of safety accidents, and meets the all-weather and high-precision mooring safety control requirements of various offshore floating facilities.

Description

Technical Field

[0001] This invention relates to the field of mooring load prediction technology, specifically a mooring load prediction method based on wind and wave identification. Background Technology

[0002] With the rapid development of marine engineering industries such as offshore oil and gas development, offshore wind power operation and maintenance, near-shore navigation operations, and offshore replenishment construction, the safe and stable operation of mooring systems for facilities such as floating vessels, offshore platforms, and floating work bodies is the core foundation for ensuring the continuity of offshore operations, equipment safety, and the reliability of personnel operations.

[0003] The patent filed on 202511028938.9 discloses a method, device, terminal and storage medium for predicting the mooring drift state of a ship. It can comprehensively evaluate the drift motion of a moored ship from two aspects: motion distance and motion angle, so as to accurately provide mooring safety warnings under various working conditions.

[0004] However, existing technologies cannot accurately distinguish between chaotic wind and waves and regular swells, which reduces the accuracy of mooring load prediction. They also cannot classify the wave-facing and wave-facing scenarios, making it impossible to capture the abnormal risks of ship movement in different scenarios. In addition, they cannot identify interference from surrounding navigating vessels.

[0005] To address the aforementioned technical shortcomings, a solution is proposed. Summary of the Invention

[0006] The purpose of this invention is to solve the problems mentioned above by proposing a mooring load prediction method based on wind and wave identification.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A mooring load prediction method based on wind and wave identification, the steps of which are as follows:

[0009] Step 1: Identification of Dominant Factors. The dominant factors in the current sea area are identified to distinguish between wind and waves as the dominant factors. Moving average wind speeds are collected to obtain turbulence intensity data characterizing the degree of near-surface airflow turbulence. Simultaneously, wavefront dynamic sequences are acquired, and spectral analysis is performed on the acquired wavefront rise time series to identify peak frequencies and spectral widths in the wave spectrum. Turbulence intensity data is coupled with the wavefront dynamic sequences to construct a dimensionless discrimination index, and the dominant factors in the current sea area are determined based on the discrimination threshold. Predictive calculations are then performed for the dominant factors. Based on the predicted calculations, the tension curve of each mooring cable over time is obtained and linearly superimposed to obtain the final total tension time history curve for each cable within a set time period. After determining the total tension time history curve, the peak tension and tension fluctuation amplitude within the sea area monitoring period are obtained. When the peak tension or tension fluctuation amplitude exceeds the corresponding set red line value, a load warning is issued, and load planning adjustments are made for the corresponding cable. The vessel currently adjusting the load is marked as the main body for load adjustment.

[0010] Step 2: Multi-type identification and control. Based on the analysis of sea area data, the wind and wave types are distinguished, and mooring load prediction and control are carried out according to the wind and wave types.

[0011] Step 3: Identify interference with navigation and transport within the sea area.

[0012] Furthermore, the method for collecting turbulence intensity data in step one is as follows: extract the monitoring period of the sea area, and arbitrarily extract a unit time period within the monitoring period of the sea area, and simultaneously extract the arithmetic mean of the moving average wind speed within the unit time period; within the current monitoring period of the sea area, simultaneously calculate the average of the squares of the difference between the moving average wind speed value and the average wind speed, and then take the square root; the standard deviation of the obtained value is taken as the average wind speed value to obtain the turbulence intensity, which is the turbulence intensity data.

[0013] Furthermore, the wavefront dynamic sequence is based on the wavefront rise time series data measured by the radar altimeter.

[0014] Furthermore, the spectrum analysis process in step one is as follows: The collected wavefront rise time series data is converted to the time-frequency domain using the fast Fourier transform algorithm, and the peak frequency and spectral width are extracted. The peak frequency is the frequency point with the largest amplitude in the converted spectrum curve; the spectral width is the range of energy coverage in the spectrum curve.

[0015] Furthermore, the process of constructing the dimensionless discriminant index in step one is as follows: Discriminant coefficient = turbulence intensity data / (peak frequency × spectral width);

[0016] The threshold is set manually based on the historical control phase and is set as the discrimination threshold;

[0017] If the discrimination coefficient exceeds the discrimination threshold, the sea area is dominated by wind in the current period; if the discrimination coefficient does not exceed the discrimination threshold, the sea area is dominated by waves in the current period.

[0018] Furthermore, the multi-type identification control process in step two is as follows:

[0019] The area is monitored based on the location of the load adjustment body. The main propagation direction of the waves is measured by buoys set up according to the area, and the current wave cycle is recorded. At the same time, the real-time heading of the load adjustment body is identified. The actual heading angle of the waves relative to the load adjustment body is calculated by the formula: actual heading angle = |main propagation direction of the waves - real-time heading | mod 360°. The four mooring lines of the load adjustment body are clearly divided into two groups: the wave-facing side and the wave-avoiding side.

[0020] Furthermore, if the actual angle of approach ∈ [0°, 30°] ∪ [330°, 360°], then the current wave type is set to downstream; if the actual angle of approach ∈ [60°, 120°], then the current wave type is set to cross.

[0021] Furthermore, when processing wave-facing scenarios, the peak sway value and the instantaneous collision velocity of the fender are collected during the mooring monitoring phase. If the peak sway value during the mooring monitoring phase exceeds the maximum peak value of the same type of mooring load configuration cycle, or the instantaneous collision velocity of the fender exceeds the instantaneous velocity red line value, it is inferred that there is a risk to the mooring in the current scenario, and the mooring load is readjusted based on the currently collected parameters.

[0022] When dealing with the back wave side scenario, the amplitude of the sway reciprocating motion and the frequency of periodic bow impacts on the fender are collected during the mooring monitoring phase. If the amplitude of the sway reciprocating motion exceeds the maximum amplitude of the same type of mooring load configuration cycle, or the frequency of periodic bow impacts on the fender exceeds the impact frequency red line value, it is inferred that there is a risk in the current mooring scenario, and the mooring load is readjusted based on the currently collected parameters.

[0023] Furthermore, the real-time planar coordinate offset of the supply ship relative to the floating body, the real-time speed of the supply ship, the real-time heading angle of the supply ship, and the real-time bow angle of the floating body are obtained; the relative heading angle of the supply ship relative to the load adjustment body is calculated; the wake body influence area is a wedge-shaped diffusion area extending backward along the track line with the stern as the apex, and the direction of the wedge centerline is as follows.

[0024] Node extraction is performed on the radar detection grid, and the projection length of the relative vector from the node to the current position of the supply ship along the centerline and the lateral distance perpendicular to the centerline are calculated: this will determine the wake interference area.

[0025] Furthermore, the load adjustment entity within the wake interference area is subjected to mooring detection to identify the wake impact cycle and the supply ship's route trajectory is identified in advance. The current wake interference area and the supply ship's location are used to distinguish the points. When the supply ship navigates according to the route trajectory, the wake interference area is shifted according to the route trajectory, and the grid nodes in the shifted area are marked as future wake interference areas. The load scheduling entity is then used to pre-adjust the mooring load.

[0026] Compared with the prior art, the beneficial effects of the present invention are:

[0027] 1. By combining wave peak frequency and spectral width to characterize wave energy distribution, it can accurately distinguish between chaotic wind waves and regular swells, thereby improving the accuracy of mooring load prediction from the source. At the same time, it can accurately identify critical sea conditions with alternating wind and waves, effectively avoiding misjudgment and omission of overload risk under complex sea conditions, and providing accurate data support and operating condition basis for subsequent load regulation and risk warning.

[0028] 2. It solves the problems of impact load and fatigue loss caused by improper pretensioning, and rationally lays the cable to use the cable's own weight to buffer wave impact, taking into account both the buffering effect and berth space constraints; it accurately captures the abnormal risks of ship movement in different scenarios such as facing waves and back waves, so as to achieve early risk identification and early adjustment, and ensure the continuous stability of the mooring system under different wind and wave conditions.

[0029] 3. Accurately pinpoint the real-time wake interference area, effectively distinguish between natural wind and wave loads and ship navigation interference loads, and fundamentally avoid load misjudgment and incorrect decisions caused by interference; by combining the ship's route trajectory, it realizes the dynamic prediction of future wake interference areas, completes the pre-adjustment of mooring load in advance, completely solves the problem of lagging control of traditional technology, and significantly reduces the probability of abnormal fluctuations in mooring load. Attached Figure Description

[0030] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0031] Figure 1 This is a block diagram illustrating the steps and principles of the present invention.

[0032] Figure 2 This is a flowchart of the method in step one of this invention. Detailed Implementation

[0033] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0035] Please see Figures 1-2 As shown, a mooring load prediction method based on wind and wave identification is presented. The specific steps of the mooring load prediction method are as follows:

[0036] Step 1: Identification of Dominant Factors. The dominant factors in the current sea area are identified, namely, whether wind or waves are the dominant factors. In the existing technology, it is not possible to identify whether the current sea state is dominated by wind (steady wind generates average drift force) or by waves (irregular waves generate high-frequency oscillation force), which leads to large deviations in the prediction of the low-frequency slow-varying tension and high-frequency alternating components of the mooring cable, especially when swells and gusts alternate, misjudging the risk of overload.

[0037] Step 2: Multi-type identification and control. Based on the analysis of sea area data, the wind and wave types are distinguished, and mooring load prediction and control are carried out according to the wind and wave types.

[0038] Step 3: Navigation interference identification. Interference identification is performed on navigation within the sea area. In the existing technology, supply ships and tugboats often berth close to each other at the offshore operation site. When ships turn quickly, they generate strong "ship waves" and local splashes, which cause interference with mooring load identification and are prone to making wrong decisions.

[0039] The process of identifying the dominant factors in step one is as follows:

[0040] The sliding average wind speed per unit time is extracted using a laser anemometer, and the sliding average wind speed per unit time is constructed. In a specific implementation scenario, the unit time can be set to 3 seconds.

[0041] Simultaneously, turbulence intensity data, characterizing the degree of near-sea surface air turbulence, is acquired. The turbulence intensity data is collected as follows: the monitoring period for the sea area is extracted, and within the monitoring period, any unit time interval is selected. The arithmetic mean of the moving average wind speed within the unit time interval is extracted simultaneously. Within the current monitoring period for the sea area, the average of the squares of the difference between the unit time moving average wind speed value and the average wind speed is calculated, and then the square root is taken (i.e., the standard deviation). The standard deviation is then divided by the average wind speed value to obtain the turbulence intensity, which is the turbulence intensity data.

[0042] It should be explained that the turbulence intensity data is a dimensionless value used to intuitively reflect the degree of "chaos" in the current wind speed; the stronger and more turbulent the wind, the higher the value.

[0043] The wavefront dynamic sequence is obtained by capturing the actual movement trajectory of the waves based on the wavefront rise time series data measured by the radar height meter; the obtained wavefront rise time series is subjected to spectral analysis to identify the peak frequency and spectral width in the wave spectrum, so as to depict the energy distribution characteristics of the waves.

[0044] The specific spectrum analysis process is as follows:

[0045] The Fast Fourier Transform (FFT) algorithm was used to perform time-frequency domain conversion on the acquired wavefront elevation time series data.

[0046] Extraction Target: Peak Frequency: In the converted spectrum curve, find the frequency point with the largest amplitude; the horizontal axis corresponding to this frequency point is the peak frequency. This represents the period when wave energy is most concentrated.

[0047] Spectral width: The range of energy coverage in the spectral curve (e.g., defined as the full width at half the peak of the spectrum, i.e., half-height width). Spectral width reflects the degree of concentration or dispersion of wave energy; the narrower the spectral width, the more homogeneous the swell composition, and the wider the spectral width, the more complex the wind and wave composition.

[0048] It should be explained that these two parameters quantify the "energy distribution characteristics" of the waves, providing a basis for subsequent judgments on whether they are regular swells (wave-dominated) or chaotic wind waves (wind-dominated);

[0049] By combining turbulence intensity data and wave peak frequency, a dimensionless discriminant index is constructed to distinguish between wind and wave dominance. The turbulence intensity data is coupled with the wave surface dynamic sequence. Specifically, the discriminant coefficient = turbulence intensity data / (peak frequency × spectral width).

[0050] The threshold is set manually based on the historical control phase and is set as the discrimination threshold;

[0051] If the discrimination coefficient exceeds the discrimination threshold, the sea area is dominated by wind in the current period, and the average wind force and the residual wave force after low-pass filtering are predicted and calculated.

[0052] If the discrimination coefficient does not exceed the discrimination threshold, then the current sea area is dominated by waves, and the first-order wave force and second-order average drift force are calculated by directly integrating the wave spectrum.

[0053] It should be explained that the current prediction calculations are all based on existing technology. Specifically, the wind load is calculated based on the average wind speed of the steady wind, and the average wind force is calculated based on the wind-receiving area of ​​the ship and the wind pressure coefficient. The wave load calculation method is as follows: the measured or simulated wave force time history is filtered by low-pass filtering to remove the first-order wave frequency oscillation component to obtain the slowly varying residual force, which is superimposed with the average wind force for the calculation of quasi-static mooring tension.

[0054] Based on the predicted calculations, the tension curve of each mooring cable over time is obtained and then linearly superimposed. Linear superposition means that the tension values ​​at each moment are directly added together to obtain the total tension time history curve of each cable within a set time period. The purpose is that the forces generated under different sea state modes (such as wind force, first-order wave force, second-order drift force, etc.) can be regarded as independent in time, and the superposition can approximately reflect the total response.

[0055] After determining the total tension time history curve, the peak tension and tension fluctuation amplitude within a specific sea area monitoring period are used for load monitoring. When the peak tension or tension fluctuation amplitude exceeds the corresponding set red line value, a load warning is issued and load planning adjustments are made for the corresponding cable. The vessel currently adjusting the load is marked as the main body of the load adjustment.

[0056] Step two, the multi-type identification and control process, is as follows:

[0057] The system monitors the area based on the location of the load adjustment body, sets up buoys to measure the main propagation direction of waves according to the area, and records the current wave cycle; it also identifies the real-time heading of the load adjustment body; and calculates the actual angle of arrival of the waves relative to the load adjustment body using the formula: Actual angle of arrival = |Main propagation direction of waves - Real-time heading | mod 360°; and clearly divides the four mooring lines of the load adjustment body into two groups: the wave-facing side (bow port / bow star) and the wave-avoiding side (stern port / stern star).

[0058] If the actual angle of approach ∈ [0°, 30°] ∪ [330°, 360°], then the current wave type is set to downstream; at this time, the waves mainly come from directly in front, and the wave-facing side (bow cable) bears most of the tension;

[0059] If the actual angle of approach is [60°, 120°], then the current wave type is set to cross waves. At this time, the waves come from the side, and the difference in force on the port and starboard sides of the load adjustment body is the greatest.

[0060] If the actual heading angle does not fall within the above range, it indicates that the wind direction is unstable and the forecast time window needs to be shortened; and the current mooring cable load ratio should be maintained.

[0061] Mooring load adjustment is performed in various scenarios, specifically on the wave-facing side and the wave-avoiding side. Mooring load adjustment includes adjusting the pretension of individual cables (the core adjustment factor) using existing technology: adjusting the initial tension by winding and unwinding the cable using a winch. Insufficient pretension leads to frequent slackening and sudden tightening of the cable during strong winds and waves, generating a huge peak load. Excessive pretension results in high steady-state load, increased daily fatigue wear, and direct exceeding of breaking force during strong winds and waves. When identifying misaligned waves or sudden impact conditions, priority is given to uniformly increasing / decreasing the pretension of the entire vessel, or differentiated adjustments are made to the bow, stern, and port / port cables.

[0062] Cable release length (catcher length):

[0063] Bonded to pretension: The longer the cable is laid, the more underwater catenary sections there are. Relying on the cable's own weight to buffer wave impact, the peak load is significantly reduced.

[0064] If the cable is laid too short, making it almost straight, the buffering capacity is lost, and even a small sway will result in a high impact load.

[0065] Limitations: The berth space at the dock is limited and cannot be extended indefinitely, taking into account the drift range of ships.

[0066] Tension distribution balance of each cable

[0067] When a ship has multiple mooring lines, it is easy for some lines to experience extremely high stress while the others experience almost no stress.

[0068] Adjustment methods: Separately raise and lower the head and tail, horizontal cables, and reverse cables to make the pretension of all cables similar, disperse the total wind and wave load, and avoid overloading of a single cable.

[0069] And conduct mooring risk detection based on different types of scenarios;

[0070] When dealing with wave-facing scenarios, the peak sway and the instantaneous collision velocity of the fender are collected during the mooring monitoring phase. If the peak sway exceeds the maximum peak value of the same type of mooring load configuration cycle, or the instantaneous collision velocity of the fender exceeds the instantaneous velocity red line value, it is inferred that there is a risk to the mooring in the current scenario, and the mooring load is readjusted based on the currently collected parameters.

[0071] Conversely, if the sway peak value during the mooring monitoring phase does not exceed the maximum peak value of the same type of mooring load configuration cycle, and the instantaneous collision speed of the fender does not exceed the instantaneous speed redline value, then it is inferred that the mooring is stable under the current scenario, and continuous mooring monitoring is carried out.

[0072] When dealing with the back wave side scenario, the amplitude of the sway and the frequency of periodic bow impacts on the fender are collected during the mooring monitoring phase. If the amplitude of the sway and the frequency of periodic bow impacts on the fender exceed the maximum amplitude of the same type of mooring load configuration cycle, or if the frequency of periodic bow impacts on the fender exceeds the impact frequency red line value, it is inferred that there is a risk in the current mooring scenario, and the mooring load is readjusted based on the current collected parameters.

[0073] If the sway amplitude does not exceed the maximum amplitude of the same type of mooring load configuration cycle during the mooring monitoring phase, and the frequency of periodic bow impacts on the fender does not exceed the impact frequency red line value, then it is inferred that the current mooring is stable and continuous mooring monitoring is carried out.

[0074] Step 3, the process for identifying interference with air transport, is as follows:

[0075] Navigation surveys were conducted in the surrounding sea area, using the supply ship as a reference; the real-time planar coordinate offset (ΔX, ΔY) of the supply ship relative to the buoy and the real-time speed V of the supply ship were obtained after synchronization and coordinate transformation. s Real-time heading angle θ of the supply ship s Real-time heading angle ψ of the floating body ship ;

[0076] Calculate the relative heading angle ϕ of the supply ship relative to the load adjustment body. s =θ s −ψ ship Its range is normalized to between −π and π;

[0077] The main area of ​​influence of the wake is a wedge-shaped diffusion zone extending backward along the track line with the stern as its apex, and the direction of the wedge's central axis is ϕ. axis =ϕ s +π.

[0078] The lateral spread half-width W is determined by the speed, while the longitudinal influence length L is constrained by both speed and distance.

[0079] ;

[0080] ;

[0081] The constant 30 represents the characteristic time (seconds) for the wake to fully develop, 0.15 and 3.0 are empirical coefficients fitted based on measured data of ship hydrodynamics, and 50 is an upper limit constant in meters.

[0082] Extract the radar detection grid and set the nodes (x i y i ), where i is a natural number greater than 1;

[0083] Calculate the projected length d of the relative vector from this node to the current position of the supply ship along the central axis.proj and the lateral distance d perpendicular to the central axis. perp :

[0084] ;

[0085] ;

[0086] Only when 0 < d proj <L and d perp When W < W, the node falls into the wake ripple suspicion zone;

[0087] Mark the corresponding sea area of ​​the grid node as the wake interference area;

[0088] The load adjustment body within the wake interference area is moored and detected to identify the wake impact cycle and the supply ship's route trajectory in advance. The current wake interference area and the supply ship's location are used to distinguish the points. When the supply ship sails according to the route trajectory, the wake interference area is translated according to the route trajectory, and the grid nodes in the translated area are marked as the future wake interference area.

[0089] The load scheduling entity pre-adjusts the mooring load and identifies the timing of changes in the wake interference area based on the changes in the wake impact cycle corresponding to the current wake interference area. It then infers the wake change interval based on the wake change interval and pre-adjusts the load for future wake interference areas accordingly. This avoids untimely adjustments that result in excessive load costs and reduced mooring efficiency. Simultaneously, it further determines whether future wake interference areas are aligned based on wake impact cycle detection, preventing erroneous scheduling decisions when the wake impact cycle of the current wake interference area changes but the wake impact cycle of adjacent future wake interference areas has not yet occurred. In such scenarios, it is necessary to further analyze the supply vessel's route trajectory to re-determine the wake interference area within the trajectory area and use this information for regional predictive control.

[0090] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A mooring load prediction method based on wind and wave identification, characterized in that, The steps of the mooring load prediction method are as follows: Step 1: Identification of Dominant Factors. This step involves identifying the dominant factors in the current sea area to distinguish between wind and waves as the dominant factors. Moving average wind speeds are collected to obtain turbulence intensity data characterizing the degree of near-surface airflow disturbance. Simultaneously, wavefront dynamic sequences are acquired, and spectral analysis is performed on the acquired wavefront rise time series to identify peak frequencies and spectral widths in the wave spectrum. The turbulence intensity data is coupled with the wavefront dynamic sequences to construct a dimensionless discrimination index, and the dominant factor in the current sea area is determined based on the discrimination threshold. And make predictions and calculations for the dominant factors; based on the predictions and calculations, obtain the tension curve of each mooring cable over time, and perform linear superposition to obtain the final total tension time history curve of each cable within a set time period. After determining the total tension time history curve, the peak tension and tension fluctuation amplitude during the sea area monitoring period are obtained. When the peak tension or tension fluctuation amplitude exceeds the corresponding set red line value, a load warning is issued and load planning adjustment is carried out for the corresponding cable. The vessel currently adjusting the load is marked as the main body of the load adjustment. Step 2: Multi-type identification and control. Based on the analysis of sea area data, the wind and wave types are distinguished, and mooring load prediction and control are carried out according to the wind and wave types. Step 3: Identify interference with navigation and transport within the sea area.

2. The mooring load prediction method based on wind and wave identification according to claim 1, characterized in that, The method for collecting turbulence intensity data in step one is as follows: extract the monitoring period of the sea area, and arbitrarily extract a unit time period within the monitoring period of the sea area, and simultaneously extract the arithmetic mean of the moving average wind speed within the unit time period; During the current sea area monitoring period, simultaneously calculate the average of the squares of the difference between the unit time moving average wind speed value and the average wind speed, and then take the square root. The standard deviation obtained is taken as the average wind speed value to obtain the turbulence intensity, which is the turbulence intensity data.

3. The mooring load prediction method based on wind and wave identification according to claim 1, characterized in that, The wavefront dynamic sequence is based on the wavefront rise time series data measured by the radar altimeter.

4. The mooring load prediction method based on wind and wave identification according to claim 1, characterized in that, The spectrum analysis process in step one is as follows: The collected wavefront rise time series data is converted into the time-frequency domain using the fast Fourier transform algorithm, and the peak frequency and spectral width are extracted. The peak frequency is the frequency point with the largest amplitude in the converted spectrum curve; the spectral width is the range of energy coverage in the spectrum curve.

5. The mooring load prediction method based on wind and wave identification according to claim 1, characterized in that, Step 1: The dimensionless discriminant index construction process is as follows: Discriminant coefficient = turbulence intensity data / (peak frequency × spectral width); The threshold is set manually based on the historical control phase and is set as the discrimination threshold; If the discrimination coefficient exceeds the discrimination threshold, then the sea area is dominated by wind in the current period. If the discrimination coefficient does not exceed the discrimination threshold, then the sea area is dominated by waves in the current period.

6. The mooring load prediction method based on wind and wave identification according to claim 1, characterized in that, Step two, the multi-type identification and control process, is as follows: The area is monitored based on the location of the load adjustment body. The main propagation direction of the waves is measured by buoys set up according to the area, and the current wave cycle is recorded. At the same time, the real-time heading of the load adjustment body is identified. The actual heading angle of the waves relative to the load adjustment body is calculated by the formula: actual heading angle = |main propagation direction of the waves - real-time heading | mod 360°. The four mooring lines of the load adjustment body are clearly divided into two groups: the wave-facing side and the wave-avoiding side.

7. The mooring load prediction method based on wind and wave identification according to claim 6, characterized in that, If the actual angle of approach ∈ [0°, 30°] ∪ [330°, 360°], then the current wave type is set to downstream; if the actual angle of approach ∈ [60°, 120°], then the current wave type is set to cross.

8. The mooring load prediction method based on wind and wave identification according to claim 7, characterized in that, When dealing with wave-facing scenarios, the peak sway and the instantaneous collision velocity of the fender are collected during the mooring monitoring phase. If the peak sway exceeds the maximum peak value of the same type of mooring load configuration cycle, or the instantaneous collision velocity of the fender exceeds the instantaneous velocity red line value, it is inferred that there is a risk to the mooring in the current scenario, and the mooring load is readjusted based on the currently collected parameters. When dealing with the back wave side scenario, the amplitude of the sway reciprocating motion and the frequency of periodic bow impacts on the fender are collected during the mooring monitoring phase. If the amplitude of the sway reciprocating motion exceeds the maximum amplitude of the same type of mooring load configuration cycle, or the frequency of periodic bow impacts on the fender exceeds the impact frequency red line value, it is inferred that there is a risk in the current mooring scenario, and the mooring load is readjusted based on the currently collected parameters.

9. The mooring load prediction method based on wind and wave identification according to claim 1, characterized in that, Obtain the real-time planar coordinate offset of the supply ship relative to the floating body, the real-time speed of the supply ship, the real-time heading angle of the supply ship, and the real-time bow angle of the floating body; calculate the relative heading angle of the supply ship relative to the load adjustment body. The main area of ​​influence of the wake is a wedge-shaped diffusion zone extending backward along the track line with the stern as the apex, in the direction of the central axis of the wedge; Node extraction is performed on the radar detection grid, and the projection length of the relative vector from the node to the current position of the supply ship along the centerline and the lateral distance perpendicular to the centerline are calculated: this will determine the wake interference area.

10. A mooring load prediction method based on wind and wave identification according to claim 9, characterized in that, The load adjustment entity within the wake interference area is moored and detected to identify the wake impact cycle and the supply ship's route trajectory in advance. The current wake interference area and the supply ship's location are used to distinguish the points. When the supply ship sails according to the route trajectory, the wake interference area is shifted according to the route trajectory, and the grid nodes in the shifted area are marked as future wake interference areas. The load scheduling entity is then pre-adjusted for mooring load.

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