Method for laying large anchor system buoys around complex island reef

Abstract

The invention relates to a method for laying large anchor system buoys around a complex island reef, and aims to improve the stability and laying precision of the buoys in a complex marine environment. The method comprises the steps that a laying area is determined based on multi-source data comprehensive analysis, a reverse catenary anchor system structure is adopted, the total length of the reverse catenary anchor system structure is 1.2-1.5 times of the water depth, and the laying area is determined; the number, the positions and the intervals of the floating balls and the counter weights are optimized through simulation, so that the anchor system is in an inverted-S-shaped loose state after being laid, wind wave impact is effectively buffered, the system adaptability is enhanced, a top wind and top flow sailing route is planned based on real-time wind flow data, cable tension and ship posture are monitored in real time according to coordinate operation of preliminary anchor throwing, and the system performance is improved. According to the buoy laying device, efficient, safe and stable laying of buoys in the deep water area around the complex island reef is achieved.

Description

Technical Field

[0001] This application relates to the field of electrical data processing technology, specifically to a method for deploying large moored buoys around complex islands and reefs. Background Technology

[0002] Deploying large moored hydro-meteorological buoys in deep waters at depths of over 500 meters around complex islands and reefs presents numerous challenges. Existing technologies typically employ traditional taut mooring designs, lacking a systematic analysis of complex seabed topography, sediment conditions, and hydro-meteorological characteristics. This results in problems such as unscientific buoy deployment location selection, unreasonable mooring structure design, and low deployment accuracy. Especially in the waters surrounding islands and reefs, the seabed topography is highly variable, the seabed conditions are complex, and the influence of ocean currents and waves is significant, making it difficult for traditional methods to ensure the long-term stability of buoys and the validity of observation data. In addition, the existing deployment process lacks scientific navigation route planning and anchor point selection methods, which often leads to cable rebound, anchor displacement, and even equipment loss, seriously affecting the deployment success rate and operational safety. Summary of the Invention

[0003] In order to solve the problems existing in the prior art, the purpose of this application is to provide a method for deploying large moored buoys around complex islands and reefs.

[0004] The method for deploying large moored buoys around complex islands and reefs as described in this application includes: S101. Based on high-precision seabed topography data, physical oceanographic features, seabed conditions, and fisheries and shipping activity data, a multi-factor comprehensive analysis is conducted to determine the buoy deployment area; S102. Based on the water depth of the selected deployment area, a reverse catenary mooring structure is adopted, including an anchor chain connected in sequence from the bottom of the buoy, multiple buoys and counterweights connected in series in the middle, and a heavy anchor at the bottom. The total length of the mooring is 1.2-1.5 times the water depth, and the position, number and spacing of the buoys and counterweights are determined through simulation to ensure that the mooring is in a relaxed inverted S-shape after deployment. S103. Based on real-time wind and flow field data, plan the navigation route with the principle of going against the wind and current, set the buoy deployment point as the starting point and the anchor block deployment point as the ending point, and determine the actual anchor deployment point. S104. Sailing against the wind direction along the planned path, and sequentially deploying buoys, anchor chains, polymer cables, buoys and counterweights for deploying heavy anchors; S105. During the deployment process, monitor the cable tension and vessel attitude in real time. If cable rebound or abnormal attitude occurs, adjust the power or suspend the deployment to handle the situation. S106. Record the entire deployment process and conduct a final quality inspection of the deployed buoy system.

[0005] Preferably, in step S101, high-precision seabed topography, physical ocean parameters, seabed characteristics, and fisheries and shipping activity data are integrated, and then cleaned and standardized to form a standardized dataset. K-means clustering is used to identify sets of regions with similar characteristics. For regions that meet the thresholds for topographic and marine parameters, suitability is calculated by weighted scoring based on seabed sediment, and a list of suitable deployment areas is generated. Then, based on the spatial distribution analysis of fisheries and shipping, the activity density is determined, low-density areas are identified, and priority areas are obtained by sorting through optimization algorithms; Finally, the buoy deployment coordinates are generated based on the geographic information system to determine the final deployment point.

[0006] Preferably, in step S102, the length range of the mooring is calculated based on the water depth data of the target sea area, and the number of buoys and the position of the counterweight are determined through simulation to form an initial mooring configuration; An iterative optimization algorithm was used to adjust the interval between the buoy and the counterweight, so that the anchoring system was in an inverted S-shaped relaxed state, thus obtaining the optimal configuration. A spatial layout is generated using a geographic information system to determine the coordinates of the anchor chain connection points and the heavy anchor fixing points; The stability of the anchor system is verified by fluid dynamics simulation. If the threshold is met, all parameters are integrated to generate the final deployment plan. Detailed deployment coordinates and final placement points of the buoy and anchor chain connection are output using automated modeling tools.

[0007] Preferably, in step S103, real-time wind and current data of the target sea area are acquired, and the wind and current field distribution and dynamic environmental parameters are determined through environmental monitoring. The initial navigation vector is calculated based on vector analysis against the wind and current. If it deviates from the preset threshold, the course is optimized by the A* algorithm. Based on this, the geographic information system is used to calculate the preset coordinates between the start and end points to determine the coordinates of the initial anchor deployment; The stability of this point in the airflow field was then analyzed by fluid dynamics simulation, and the stable deployment coordinates were obtained after verifying that it met the threshold. Finally, relying on automated navigation technology, a complete route from the deployment point to the anchor block delivery point is generated, and a smooth navigation trajectory is output after path smoothing processing.

[0008] Preferably, in step S104, real-time wind and current field data of the target sea area are acquired through a sensor network, and after analyzing the wind and current field parameters, vector analysis is used to calculate the initial navigation vector in the direction of the reverse wind current. If it deviates from the preset threshold, the course is optimized using the A* algorithm to generate an optimized navigation vector; Based on this, a geographic information system is used to calculate the path nodes from the buoy deployment point to the anchor release point to determine the segmented navigation trajectory. The stability of the trajectory in the airflow field was then verified through fluid dynamics simulation to obtain a stable navigation trajectory; Based on this, the deployment sequence of buoys, anchor chains, cables, buoys and counterweights is planned using sequence optimization technology to form the final deployment sequence; Finally, a complete navigation path is generated using automated navigation technology.

[0009] Preferably, in step S105, cable tension and vessel attitude data are continuously collected through a sensor network, dynamic trends are extracted through time series analysis, and Kalman filtering is used for smoothing and noise reduction. If the data exceeds the preset threshold, the anomaly risk level is calculated through dynamic simulation. Based on this level, a decision tree algorithm is used to generate control commands for dynamic adjustment or suspension of deployment; The adjustment plan is executed through automated control technology, and the stability of the execution is monitored with the help of real-time feedback. If the scheme is stable, record the dynamic data of tension, attitude and control commands to form deployment process data.

[0010] Preferably, in step S106, environmental and buoy status data are acquired through a sensor network, and the data is cleaned to remove anomalies and noise, resulting in cleaned data. Dynamic trends of buoy position, velocity, and environmental parameters were extracted based on time series analysis. If the trend exceeds the threshold, the offset risk level is calculated through kinematic simulation. Based on the risk level, a decision tree algorithm is used to generate position adjustment or pause instructions; Automated control is used to adjust the release speed or angle, resulting in an optimized deployment plan. Real-time monitoring of deployment stability, and storage of dynamic data such as location, speed, environmental parameters and control commands to form a structured deployment process record.

[0011] The method for deploying large moored buoys around complex islands and reefs described in this application has the advantage that it combines systematic site selection assessment, reverse catenary mooring structure design, scientific deployment process, and simulation to achieve efficient, safe, and precise deployment of large moored buoys in deep water areas around complex islands and reefs. Specific beneficial effects include: By employing high-precision seabed topography scanning and multi-factor site selection assessment, deployment points are scientifically selected. Combined with GNSS positioning and a deployment strategy against wind and current, the landing accuracy of buoys and anchor blocks is significantly improved. The reverse catenary design, combined with the dynamic simulation configuration of buoys and counterweights, makes the mooring system form an "inverted S-shape" in a relaxed state, effectively buffering the impact of wind and waves and adapting to complex marine environments. Simulation optimization of the number, position, and counterweight ratio of buoys reduces material waste. At the same time, scientific deployment procedures and safety management measures reduce operational risks, resulting in more stable buoy deployment, more reliable observation data, and a longer service life in harsh marine environments. The introduction of simulation models to support decision-making provides good scalability and adaptability, making it suitable for buoy deployment under different water depths, seabed conditions, and sea states. Attached Figure Description

[0012] Figure 1 This application describes a method for deploying large moored buoys around complex islands and reefs. Figure 1 ; Figure 2 This application describes a method for deploying large moored buoys around complex islands and reefs. Figure 2 . Detailed Implementation

[0013] like Figures 1-2 As shown in the figure, the method for deploying large moored buoys around complex islands and reefs according to this application includes: like Figures 1-2 As shown, in step S101, based on high-precision seabed topography data, physical oceanographic features, seabed conditions, and fisheries and shipping activity data, the buoy deployment area is determined through multi-factor comprehensive analysis.

[0014] Furthermore, in step S101, high-precision seabed topography data, physical oceanographic parameters, seabed characteristics, and fisheries and shipping activity data are acquired and integrated into a unified dataset; Data preprocessing techniques are used to clean and standardize the dataset, resulting in a normalized dataset. The normalized dataset was classified using the K-means clustering algorithm to identify a set of regions with similar topographic features, ocean parameters, and activity distributions. If the topographic features and marine parameters of a certain region in the region set meet the preset threshold, the suitability of the region is calculated by weighted scoring method combined with the characteristics of the seabed, and a list of suitable deployment areas is obtained. Based on the list of suitable deployment areas, spatial analysis techniques are used, combined with the distribution of fishery and shipping activities, to determine the activity density within the area and identify low-density areas; By optimizing the algorithm, low-density areas are sorted to obtain priority areas that meet the deployment conditions; For priority areas, geographic information system (GIS) technology is used to generate the coordinates of the buoy deployment locations and determine the final deployment points.

[0015] Specifically, in step S101, based on high-precision seabed topographic data, firstly, seabed topographic grid data with a resolution of 0.5 meters is obtained through a multibeam echo sounder system, and the data is smoothed using the Kriging interpolation algorithm to generate a three-dimensional topographic model. The slope change rate is calculated, and flat areas with a slope of less than 5 degrees are selected as potential deployment points. Physical oceanographic analysis uses CTD instruments to collect temperature, salinity, and current velocity data within a water depth range of 0-500 meters. Combined with the ROMS ocean model, it simulates the tidal current intensity in the region and selects areas with an average current velocity of less than 0.5 m / s to ensure buoy stability. Substrate conditions were determined by obtaining substrate hardness data using side-scan sonar and combining it with sediment grain size analysis. Areas with sandy or clayey substrates (hardness values ​​between 50 and 100 kPa) were prioritized, while rocky areas were avoided to reduce anchoring difficulty. Fisheries and shipping activity data are obtained from the AIS system to determine vessel density. Combined with fishery logs, the frequency of vessel passage in the area is calculated, and areas with an average annual vessel density of less than 10 vessels per square kilometer are selected to reduce the risk of collisions. By integrating the above data through a GIS platform and using a weighted overlay analysis method, the topography, marine features, seabed sediment, and activity data were assigned weights of 0.3, 0.3, 0.2, and 0.2, respectively. The comprehensive suitability index was calculated, and areas with an index higher than 0.8 were selected as candidate deployment sites. Finally, spatial clustering analysis (K-means algorithm, K=3) was used to determine the center point coordinates (120.5°E, 30.2°N) as the buoy deployment location, ensuring that the deployment area takes into account stability, safety and operability.

[0016] like Figures 1-2 As shown, in step S102, a reverse catenary mooring structure is adopted, including an anchor chain connected to the lower end of the buoy, multiple buoys and counterweights connected in series in the middle, and a heavy anchor connected to the bottom. The total length of the mooring is 1.2-1.5 times the water depth. The position, number and spacing of the buoys and counterweights are determined by simulation to ensure that the mooring is in an inverted S-shape in the relaxed state.

[0017] Further, in step S102, the anchorage length is calculated by measuring the water depth data of the target sea area, and the anchorage length range is obtained; Based on the anchorage length range, simulation technology is used to determine the number of buoys and the position of counterweights to obtain the initial anchorage configuration; If the initial anchorage configuration exhibits an inverted S-shaped relaxed state in the simulation, the interval between the buoy and the counterweight is adjusted through an iterative optimization algorithm to obtain the optimized anchorage configuration. Based on the optimized anchor system configuration, geographic information system technology is used to generate the spatial layout of the anchor system structure and determine the coordinates of the anchor chain connection point and the heavy anchor fixing point. By using fluid dynamics simulation, the stability of the anchorage structure under the action of ocean currents is analyzed, and it is determined whether the preset threshold is met, thus obtaining a stable anchorage design. If the stable anchor system design meets the preset threshold, then the anchor system length, number of buoys, counterweight position and spatial layout are integrated through data fusion technology to generate the final anchor system deployment plan; Based on the final mooring deployment plan, an automated modeling tool was used to generate detailed deployment coordinates of the connection between the lower end of the buoy and the anchor chain, thus determining the final deployment point.

[0018] Specifically, in step S102, based on the design of the reverse catenary mooring structure, the entire system is dynamically simulated using the marine engineering simulation software OrcaFlex to determine the length of the anchor chain connected to the lower end of the buoy, the configuration of the buoy and the counterweight, and the heavy anchor parameters. First, for sea areas with a water depth of 500 meters, the total length of the anchorage was set at 600-700 meters, and determined to be 650 meters through iterative calculations to meet the requirement of 1.3 times the water depth; The anchor chain is made of high-strength steel, with each section being 27.5 meters long, made of high-strength grade III steel, 38mm in diameter, with a tensile load of 812kN and a minimum breaking load of 1160kN. Ensure it can withstand the maximum tidal force; In the simulation, the force distribution of the anchor chain under the action of wind, waves and current was simulated by the finite element analysis method. The current velocity was set to 0.3 m / s. The calculated peak tension of the cable did not exceed 150 kN. The tension range during normal operation was 80-120 kN, and the cable was kept in a relaxed state in an "inverted S-shape". The configuration of buoys and counterweights was determined using a particle swarm optimization (PSO) algorithm. The buoys were set to have a diameter of 17 inches (0.4m, with a net buoyancy of 170N per buoy, and 26-30 buoys were set). Each buoy was spaced 2 meters apart, and a 200kg counterweight was attached to the end of each buoy at a spacing of 15 meters. The optimization objective was to minimize the sway amplitude of the anchor system. Simulation results showed that the sway angle was less than 5 degrees, which met the stability requirements. The mass of the anchor is calculated using the force balance equation and set to 2 tons. The anchor holding force must be greater than the bottom friction force (assuming the bottom friction coefficient is 0.4, the anchor holding force reaches 80kN) to ensure stable anchoring. All parameters were simulated 1000 times in Monte Carlo using OrcaFlex to verify that the anchorage still maintained an "inverted S-shaped" shape under extreme wind and waves (wave height 3 meters, period 8 seconds) with tension fluctuation of less than 10%, confirming the rationality of the design. Finally, the system generates anchorage spatial layout data, outputs the coordinates of the buoy and counterweight, and the stress curve of the anchor chain, for reference during deployment.

[0019] like Figures 1-2 As shown, in step S103, the navigation route is planned based on the principle of going against the wind and current, the buoy deployment point is set as the starting point and the anchor block deployment point is set as the ending point, and the actual anchor deployment point is determined.

[0020] Furthermore, in step S103, by acquiring real-time wind direction and current direction data of the target sea area, environmental monitoring technology is used to determine the wind and current field distribution and obtain the dynamic environmental parameters of the target sea area; Based on dynamic environmental parameters, vector analysis techniques are used to calculate the initial navigation direction under headwind and headwind conditions, and obtain the initial navigation vector. If the initial navigation vector deviates from the preset navigation threshold by more than a set range, the navigation direction is adjusted using Algorithm A to obtain an optimized navigation vector. Based on the optimized navigation vector, geographic information system technology is used to calculate the preset coordinates between the starting point and the ending point, and determine the coordinates of the initial anchor deployment. By using fluid dynamics simulation technology, the stability of the actual buoy deployment point in the airflow field is analyzed to determine whether it meets the preset threshold and obtain the coordinates of the stable deployment point. Based on the coordinates of the stable deployment point, an automated navigation technology is used to generate a complete navigation path from the buoy deployment point to the anchor deployment point, and to determine the final navigation route. By using path smoothing technology, the final navigation route is optimized to obtain a smooth navigation trajectory.

[0021] Specifically, in step S103, the navigation route is planned based on the principle of going against the wind and current, the buoy deployment point is set as the starting point and the anchor block deployment point is set as the ending point, the actual anchor deployment point is determined, and the navigation route is generated by the marine engineering path optimization algorithm. First, set the starting point coordinates as (0,0) and the ending point anchor drop point coordinates as (3000,1000), in meters. The total voyage distance is calculated to be approximately 3162.3 meters using straight-line distance. The anchor drop point coordinates are calculated as (1000,333.3) using linear interpolation. The path planning was performed using the A* algorithm, with a grid resolution of 50m×50m, taking into account a wind speed of 10m / s (direction 270 degrees, westerly wind) and a tidal current speed of 0.5m / s (direction 180 degrees, southerly flow). The A* algorithm aims to minimize the sailing time. The ship's speed is set to 5 m / s. After taking into account the influence of wind and current, the actual speed is calculated to be 4.8 m / s through vector synthesis. In the path optimization, an environmental drag coefficient is introduced and estimated using Bernoulli's equation. The wind drag coefficient is 0.02 and the tidal current drag coefficient is 0.03. The heading angle is dynamically adjusted, with an initial heading angle of 18.4 degrees (calculated using arctangent, atan(1000 / 3000)). The optimal path was generated by iterating the A* algorithm 1000 times, with the total path length optimized to 3200 meters. This is slightly longer than the straight-line distance but avoids high-velocity areas (local tidal current speeds reach 0.7 m / s). To verify the path stability, the Monte Carlo method was used to simulate 1000 times, with input wind speed fluctuations of ±2 m / s and tidal current speed fluctuations of ±0.1 m / s. The calculated average sailing time was 667 seconds (approximately 11.1 minutes), with a deviation of less than 5%. The path point coordinates are smoothed using cubic spline interpolation to generate a continuous curve, ensuring smooth ship navigation. Finally, the system outputs a sequence of waypoint coordinates and heading angle data for use by the automatic navigation system.

[0022] In one embodiment, the formula for calculating the heading angle using the arctangent is: Where θ is the heading angle (in radians or degrees), and Δx and Δy are the coordinate differences; The preset coordinate formula for linear interpolation is: Where (x,y) are preset coordinates, (x1,y1) are the starting coordinates, and (x2,y2) are the ending coordinates.

[0023] like Figures 1-2 As shown, in step S104, the ship sails against the wind direction along the planned path, and sequentially deploys the buoy body, anchor chain, polymer cable, buoy and counterweight, and finally deploys the heavy anchor.

[0024] Further, in step S104, the wind flow field vector of the target sea area is obtained. Environmental monitoring technology is used to collect real-time wind direction and flow direction data through a sensor network to determine the wind flow field distribution characteristics and obtain wind flow field parameters. Based on the wind flow field parameters, vector analysis technology is used to decompose the wind flow field vector, calculate the initial navigation angle in the direction of the reverse wind flow, and obtain the initial navigation vector. If the initial navigation vector deviates from the preset navigation threshold by more than a set range, the A* algorithm is used to adjust the navigation angle, and the path is optimized in combination with the wind flow field parameters to obtain the optimized navigation vector. Based on the optimized navigation vector, geographic information system technology is used to calculate the path nodes from the buoy deployment point to the anchor release point and determine the segmented navigation trajectory. By using fluid dynamics simulation technology, the stability of segmented navigation trajectories in the wind and air field is analyzed, and it is determined whether the trajectory meets the preset threshold to obtain a stable navigation trajectory. Based on the stable navigation trajectory, sequence optimization technology is used to plan the deployment sequence of buoy body, anchor chain, polymer cable, buoy and counterweight, and determine the final deployment sequence; Using automated navigation technology, a complete navigation path is generated from the buoy deployment point to the anchor deployment point based on the final deployment sequence, thus determining the final navigation route.

[0025] Specifically, in step S104, taking a specific operation as an example, based on the deployment requirements of the marine engineering buoy system, the buoy body, anchor chain, polymer cable, buoy and counterweight and heavy anchor are deployed sequentially along the counter-wind and counter-current path, and an automated control system is used to ensure precise implementation; The system uses the initial coordinates (500, 200) as the buoy deployment point and the final anchor deployment point coordinates (4000, 1500) in meters. The path planning adopts an improved Dijkstra algorithm with a grid resolution of 100 meters × 100 meters, taking into account a wind speed of 8 meters per second (300 degrees, northwest wind) and a current speed of 0.4 meters per second (150 degrees, southeast flow). The ship's speed was set to 6 m / s, and the actual speed calculated by vector synthesis was 5.7 m / s. The environmental resistance coefficient was estimated by the fluid dynamics model, with wind resistance of 0.015 and tidal resistance of 0.025. The buoy is placed at the starting point. The control system automatically adjusts the deployment depth based on the depth sensor data (water depth 50 meters). The target depth is 45 meters with an error of ±0.5 meters. The PID algorithm is used to adjust the winch speed, and the speed curve is v(t) = 0.2t + 0.5 m / s (t is time, seconds). The anchor chain is deployed at 1 / 4 of the path distance, at coordinates (1375, 575). Through geometric interpolation, the length of the anchor chain at the bottom of the buoy is calculated to be 37.5 meters. The system uses a tension sensor to monitor and ensure that the tension is within the normal working range of 80-120kN. The polymer cable is gradually deployed at the midpoint of the path (2250, 850). The cable is 530 meters long and has a density of 0.95 g / cm³. The system calculates the force distribution of the cable through finite element analysis, and the maximum tensile force does not exceed 150 kN. During the deployment of the polymer cable, the buoy and counterweight are deployed at 3 / 4 of the path, at coordinates (3125, 1125). The total buoyancy provided by the buoy is 4420N (calculated based on 26 170N buoys). The position of the buoy is dynamically adjusted during deployment, and the acceleration a=F / m is calculated based on Newton's second law to keep the buoy stable. The heavy anchor is deployed to the endpoint, and the system uses sonar ranging to ensure that the error of the deployment point is less than 1 meter, with a deployment speed of 0.1 meters per second; The entire process involved real-time data acquisition and feedback. The system automatically adjusted the ship's heading angle (initial 22.6 degrees, atan(1000 / 3500)), optimizing the total path length to 4200 meters. After 500 Monte Carlo simulations, wind speed fluctuations were ±1.5 m / s, tidal current speed fluctuations were ±0.08 m / s, and the actual speed calculated by vector synthesis was 5.7 m / s. The average sailing time was 736.8 seconds (approximately 12.28 minutes), with a deviation of 3%. Finally, a sequence of continuous waypoints and delivery parameters is generated for use by the automatic navigation and delivery system.

[0026] In one embodiment, the formula for calculating the vector-synthetic speed is: , where v 实际 For actual speed, v 船 For the ship's speed, v 流 Let θ be the flow velocity. 差 The angle between the direction of the boat and the direction of the current.

[0027] like Figures 1-2 As shown in step S105, the cable tension and vessel attitude are monitored in real time during the deployment process. If cable rebound or abnormal attitude occurs, the deployment is handled by adjusting the power or pausing the deployment.

[0028] Furthermore, in step S105, cable tension and vessel attitude data are continuously collected through a sensor network. Time series analysis technology is used to process the collected data, extract the changing trends of tension and attitude, and obtain dynamic trend data. Based on the dynamic trend data, the Kalman filter algorithm is used to smooth the tension and attitude data, eliminate noise interference, and obtain smoothed trend data. If the cable tension or vessel attitude in the smoothed trend data exceeds a preset threshold, the probability of cable rebound or attitude abnormality is calculated using dynamic simulation technology to determine the level of abnormality risk. Based on the level of abnormal risk, a decision tree algorithm is used to generate control commands for dynamic adjustment or suspension of deployment, thus obtaining a deployment control scheme. By using automated control technology, the parameters of the vessel's power system are adjusted or the deployment operation is paused according to the deployment control plan, thus obtaining an optimized execution plan. Based on the optimized execution plan, real-time feedback control technology is used to monitor changes in cable tension and vessel attitude during execution to determine whether the execution plan is stable. If the execution plan is stable, dynamic data of tension, attitude, and control commands are stored through data recording technology to obtain deployment process data.

[0029] Specifically, in step S105, during the deployment of the marine engineering buoy system, the cable tension and ship attitude are monitored in real time, and abnormal situations are handled automatically to ensure the stability and accuracy of the deployment operation. The system uses a tension sensor to collect cable tension data every second at a sampling frequency of 10Hz, keeping the tension within the normal operating range of 80-120kN, with a peak value not exceeding 150kN; If the tension exceeds 150kN or falls below 80kN, the system identifies a risk of cable rebound and automatically triggers a dynamic adjustment algorithm. This algorithm adjusts the winch speed using a proportional-integral-derivative (PID) controller with a proportional coefficient Kp = 0.8, an integral coefficient Ki = 0.05, and a derivative coefficient Kd = 0.1. The calculation formula is as follows: Where e(t) is the tension error, u(t) is the control output (such as the winch speed adjustment), and K p K is the proportionality coefficient. i K is the integral coefficient. d These are the differential coefficients; Assuming the current tension detection value is 160kN, which exceeds the peak threshold of 150kN, the error e(t) = 160 - 150 = 10kN. The system calculates a winch deceleration command, adjusts the speed to 0.15 m / s, and reassesses the tension after 5 seconds. If the tension returns to 100kN, continue laying. If the problem persists, pause the winch operation and wait 10 seconds before testing again. Meanwhile, the ship's attitude is monitored in real time by an inertial measurement unit (IMU), which collects the ship's pitch and roll angles, with a normal range of ±3 degrees. If the pitch angle exceeds 3 degrees, for example, 4.5 degrees is detected, the system uses a Kalman filter algorithm to fuse IMU and GPS data to predict the ship's attitude trend. The calculation formula is as follows: The system automatically adjusts the thruster power, increasing the output by 15%, and the target will restore the pitch angle to within 2 degrees, with the adjustment time controlled within 20 seconds; If the attitude is still not recovered, the system pauses deployment, activates the backup stabilization mode, applies a 2kN correction force through the lateral thrusters, calculates the ship's acceleration based on Newton's second law F=ma, assumes the ship's mass is 500 tons, and the acceleration a=2 / 500=0.004 m / s², and gradually corrects the attitude. All data is recorded through a central control system, generating time-series logs for subsequent analysis and optimization of deployment strategies.

[0030] In one embodiment, the Kalman filter state prediction formula is as follows: , where xk Let A be the current state vector (ship attitude), and let A be the state transition matrix. k−1 Let B be the state vector from the previous time step, and let B be the control input matrix. k To control the input vector, w k This is process noise; Where A is the state transition matrix, which is obtained by pre-calibration based on the ship's kinematics model. It is usually an identity matrix or calculated based on the sampling period and the ship's dynamic characteristics. B is the control input matrix, which reflects the influence of the thruster control quantity on the attitude and is obtained through system identification or experimental calibration. uk is the control input vector, representing the thrust or torque command of the thruster at the current moment, which is generated in real time by the control system.

[0031] like Figures 1-2 As shown, in step S106, the entire deployment process is recorded, and the buoy system undergoes a final quality inspection to ensure it meets the design requirements.

[0032] Furthermore, in step S106, environmental data and buoy device status data are acquired from the deployment environment through a sensor network. Data cleaning technology is used to process the acquired data, remove outliers and noise, and obtain cleaned deployment data. Based on the deployment data after cleaning, time series analysis technology was used to analyze the changing patterns of buoy position, velocity and environmental parameters, extract dynamic trend data, and obtain the buoy movement trend. Based on the buoy's movement trend, if the buoy's position or speed exceeds a preset threshold, kinematic simulation technology is used to calculate the probability and magnitude of the buoy's deviation and determine the level of deviation risk. Based on the risk level of the deviation, a decision tree algorithm is used to generate control commands for adjusting the buoy position or pausing deployment, thus obtaining a deployment adjustment plan. Based on the deployment adjustment plan, the release speed or angle parameters of the buoys are adjusted through automated control technology to generate an optimized deployment plan; Based on the optimized deployment plan, real-time monitoring technology is used to continuously collect data on changes in buoy position, speed, and environmental parameters to determine whether the deployment process is stable and obtain deployment status data. Based on the deployment status data, data storage technology is used to structure and store the dynamic data of buoy position, speed, environmental parameters and control commands, thus obtaining the deployment process record data.

[0033] Specifically, in step S106, during the deployment of the marine engineering buoy system, the entire deployment process must be recorded, and the buoy system must undergo a final quality inspection to ensure that it meets the design requirements. The system records key parameters during the deployment process through a high-precision data acquisition module, including buoy movement, ambient water flow velocity, and the buoy's own vibration frequency. The depth sensor collects buoy movement data at a sampling frequency of 5Hz, with a recording range of 0-200 meters and an accuracy of ±0.1 meters; For example, if the buoy moves 150.2 meters, the system calculates the buoy's speed using depth data. The formula is v = Δd / Δt, where Δd is the change in movement and Δt is the time interval. Assuming Δt = 2 seconds and Δd = 0.4 meters, the calculated speed is v = 0.2 meters per second. If the moving speed exceeds the set range of 0.15-0.25 m / s, the system will automatically adjust the winch motor power, reducing the output by 10%, and the target speed will be restored to 0.2 m / s. The system will then re-detect after 3 seconds. Meanwhile, the ambient water flow velocity was measured using a Doppler current meter with a sampling frequency of 2Hz, and the normal range was 0.1-0.5 m / s; If a water flow velocity of 0.6 m / s is detected, the system analyzes the periodicity of the water flow based on the Fourier transform algorithm, as shown in the formula: Where F(ω) is the frequency domain signal, f(t) is the time domain signal, ω is the angular frequency, and j is the imaginary unit. The frequency characteristics of the water flow are extracted to determine whether there is eddy current interference. If a vortex is confirmed, the system will pause deployment for 5 seconds and continue once the water flow velocity drops to 0.4 m / s. The buoy vibration frequency is monitored by an accelerometer, and the normal range is 0.5-2Hz; If the detected vibration frequency is 2.5Hz, the system uses a wavelet transform algorithm to decompose the signal, as shown in the formula: Where ψ is the wavelet basis function, s is the scale, τ is the translation parameter, f(t) is the signal, and W(s,τ) are the wavelet coefficients; If the vibration originates from an external disturbance, the system adds 0.5 kg of counterweight through the counterweight adjustment module. Based on the mass-spring system model F=-kx, the buoy stabilizing force is calculated, where F is the spring restoring force (N), k is the spring constant (N / m), and x is the displacement (m). Assuming the spring constant k=100 N / m and the displacement x=0.01 m, F=1 N is obtained, and the vibration frequency is stabilized to 1.5 Hz. In the final quality inspection stage, the system tests the buoyancy of the buoy, which is designed to be 1000±50 Newtons. The buoyancy sensor measured 980 Newtons, which meets the requirements. The system generates a log file containing depth, water flow, vibration, and buoyancy data, which is stored in a cloud database for subsequent deployment optimization analysis.

[0034] One embodiment of the method for deploying large moored buoys around complex islands and reefs also includes: Based on high-precision multibeam echo sounder data on seabed topography and water depth, combined with physical oceanographic features (waves, currents), seabed conditions (sand or clay, hardness 50–100 kPa), fishing activities (annual vessel density less than 10 vessels / km²), and shipping data, a multi-factor weighted overlay analysis was performed using a GIS platform to calculate the comprehensive suitability index. Areas with an index higher than 0.8 were selected as buoy deployment points, and the final deployment coordinates were determined to be 120.5°E, 30.2°N. The reverse catenary mooring structure is adopted, with a total length of 1.3 times the water depth (for example, the mooring length is 130 meters when the water depth is 100 meters). The mooring system consists of the following components from top to bottom: two 38mm three-stage stoppered anchor chains (each 27.5 meters) connected to the lower end of the buoy, followed by a 50mm eight-strand polymer cable; 26-30 170N buoys and a 200kg counterweight connected in series in the middle, with the buoys and counterweight spaced 10 meters and 15 meters apart, respectively; and two 38mm anchor chains and a 2-ton anchor connected to the bottom. Dynamic simulation is performed using OrcaFlex simulation software to ensure that the mooring system maintains an "inverted S-shaped" slack state with a swing angle of less than 5 degrees under tidal current conditions of 0.3 m / s and wave height of 3 meters. Based on real-time wind field (wind speed 10 m / s, wind direction 270°) and flow field (flow speed 0.5 m / s, direction 180°) data, and adhering to the principle of facing the wind and current, the A* algorithm is used to plan the navigation path. The buoy deployment point is set as the starting point (0,0), the anchor block deployment point is set as the ending point (3000,1000), and the anchor drop point is (1000,333.3). The stability of the path is verified by hydrodynamic simulation, and a continuous navigation trajectory is generated by smoothing through cubic spline interpolation. The vessel travels at a speed of 5 m / s against the wind direction, and sequentially deploys the buoy body, anchor chain, polymer cable, buoy and counterweight, and finally drops the heavy anchor. During the deployment process, the cable tension (normal range 80-120kN, peak value not exceeding 150kN) and the vessel attitude (pitch angle ±3°) are monitored in real time by tension sensors (sampling frequency 10Hz) and IMU unit. If the threshold is exceeded, the winch speed is dynamically adjusted or the deployment is paused by the PID controller. During deployment, parameters such as the buoy's movement depth, water flow velocity, and vibration frequency are continuously recorded. The data is stored after being denoised by Kalman filtering. Finally, the buoyancy of the buoy is detected by a buoyancy sensor (design value 1000±50 N). If the requirements are met, a structured log file is generated and stored in a cloud database for subsequent analysis. This embodiment achieves efficient, safe, and stable deployment of large moored buoys in deep-water areas surrounding complex islands and reefs through systematic site selection, simulation optimization of mooring structure, scientific path planning, and real-time dynamic control.

[0035] For those skilled in the art, various other corresponding changes and modifications can be made based on the technical solutions and concepts described above, and all such changes and modifications should fall within the protection scope of the claims of this application.

Claims

1. A method for deploying large moored buoys around complex islands and reefs, characterized in that, include: Based on high-precision seabed topography data, physical oceanographic features, seabed conditions, and fisheries and shipping activity data, a multi-factor comprehensive analysis was conducted to determine the buoy deployment area; Based on the water depth of the selected deployment area, a reverse catenary mooring structure is adopted, including an anchor chain connected in sequence from the bottom of the buoy, multiple buoys and counterweights connected in series in the middle, and a heavy anchor at the bottom. The total length of the mooring is 1.2-1.5 times the water depth, and the position, number and spacing of the buoys and counterweights are determined through simulation to ensure that the mooring is in a relaxed inverted S-shape after deployment. Based on real-time wind and flow field data, the navigation route is planned according to the principle of going against the wind and current, the buoy deployment point is set as the starting point and the anchor block deployment point is set as the ending point, and the actual anchor deployment point is determined. Sailing against the wind direction along the planned path, and sequentially deploying buoys, anchor chains, polymer cables, buoys, and counterweights for deploying heavy anchors; During the deployment process, the cable tension and vessel attitude are monitored in real time. If cable rebound or abnormal attitude occurs, the deployment is handled by adjusting the power or pausing the deployment. The entire deployment process is recorded, and the deployed buoy system undergoes a final quality inspection.

2. The method for deploying large moored buoys around complex islands and reefs according to claim 1, characterized in that, The step of determining the buoy deployment area includes: By integrating high-precision seabed topography data, physical oceanographic parameters, seabed characteristics, and fisheries and shipping activity data, a standardized dataset is formed. Clustering algorithms are used to identify regions with similar characteristics. Weighted scores are then applied based on topography, oceanographic parameters, and seabed conditions to generate a list of suitable deployment areas. Based on the spatial distribution of fisheries and shipping activities, the activity density is analyzed to identify low-density areas. Priority deployment areas are obtained by ranking the data using optimization algorithms. Geographic information systems are used to generate buoy deployment coordinates to determine the final deployment points.

3. The method for deploying large moored buoys around complex islands and reefs according to claim 1, characterized in that, The steps of the reverse catenary anchorage structure include: The length range of the mooring is determined based on the water depth of the target sea area. The number of buoys and the position of the counterweights are determined through simulation to form an initial mooring configuration. The interval between the buoys and the counterweights is adjusted to make the mooring form an inverted S shape. A spatial layout is generated using a geographic information system to obtain the coordinates of the anchor chain connection point and the anchor fixing point. The stability of the mooring is verified through hydrodynamic simulation to obtain the deployment plan.

4. The method for deploying large moored buoys around complex islands and reefs according to claim 1, characterized in that, The step of sequentially placing each component includes: Real-time wind flow field data is acquired, the navigation vector in the direction of the reverse wind flow is calculated, segmented navigation trajectories are generated through path optimization algorithms, the stability of the trajectories is verified by fluid dynamics simulation, the deployment sequence of buoys, anchor chains, polymer cables, buoys and counterweights is planned, a complete navigation path is obtained and automated deployment is implemented.

5. The method for deploying large moored buoys around complex islands and reefs according to claim 1, characterized in that, The real-time monitoring and processing steps include: The system continuously collects cable tension and vessel attitude data, performs time series analysis and filtering. If the data exceeds a preset threshold, it calculates the abnormal risk level through dynamic simulation, generates control commands for dynamic adjustment or suspension of deployment based on the risk level, executes the adjustment plan through an automated control system, and provides real-time feedback to monitor the execution effect.

6. The method for deploying large moored buoys around complex islands and reefs according to claim 1, characterized in that, The steps for complete recording and quality inspection include: Collect environmental and buoy status data, perform data cleaning and trend analysis. If the buoy position or speed exceeds the threshold, calculate the offset risk through kinematic simulation, generate position adjustment or pause commands, and adjust release parameters through automated control. This is used to monitor deployment stability in real time and store all dynamic data in a structured manner.

7. The method for deploying large moored buoys around complex islands and reefs according to claim 1, characterized in that, The multi-factor comprehensive analysis also includes: using a multibeam echo sounder to acquire high-resolution seabed topographic data, combining CTD instruments and ocean models to simulate tidal current intensity, determining seabed hardness through side-scan sonar and sediment analysis, analyzing vessel traffic density based on the AIS system and fisheries logs, and calculating the comprehensive suitability index using a weighted overlay analysis method.

8. The method for deploying large moored buoys around complex islands and reefs according to claim 3, characterized in that, The simulation was performed using marine engineering dynamic simulation software. The force distribution of the anchor chain was analyzed by finite element method, the configuration of the buoy and counterweight was determined by optimization algorithm, and the stability of the anchoring system under extreme sea conditions was verified by Monte Carlo simulation.

9. The method for deploying large moored buoys around complex islands and reefs according to claim 4, characterized in that, The path optimization algorithm is either the A* algorithm or the Dijkstra algorithm. It combines the wind resistance model with the principle of vector synthesis to obtain the optimal navigation path, and then performs path smoothing through spline interpolation.

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