A method for deploying a large mooring buoy around a complex reef

Abstract

The application relates to a large-scale mooring buoy laying method for a complex island reef periphery, aiming at improving the stability and laying precision of the buoy in a complex marine environment. The method comprises the following steps: determining a laying area based on comprehensive analysis of multi-source data, adopting a reverse catenary mooring structure, the total length of which is 1.2-1.5 times of the water depth, and optimizing the number, position and interval of the floating ball and the counterweight through simulation, so that the mooring is in a "inverted S-shaped" relaxed form after being laid, effectively buffers the wind and wave impact, enhances the system adaptability, plans a headwind and headstream sailing route based on real-time wind and current data, carries out preliminary anchor laying operation according to the coordinates, real-time monitors the cable tension and the ship attitude, dynamically adjusts the laying process, records the data throughout the process and carries out quality inspection, and the application realizes efficient, safe and stable laying of the buoy in a complex island reef periphery deep water area.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric data processing, in particular to a large anchor system buoy deployment method in the complex surrounding of reefs. BACKGROUND

[0002] Deploying large anchor system hydro-meteorological buoys in the deep water area with a water depth of more than 500 meters in the complex surrounding of reefs faces many challenges. The existing technology usually adopts the traditional tight anchor system design, lacks systematic analysis of the complex seabed topography, bottom conditions and hydro-meteorological characteristics, resulting in unscientific selection of buoy deployment location, unreasonable design of anchor system structure, low deployment accuracy and other problems.

[0003] Especially in the surrounding sea area of the reefs, the seabed topography changes greatly, the bottom conditions are complex, and the sea current and wave influence is significant. The traditional method is difficult to ensure the long-term stability of the buoy and the effectiveness of the observation data.

[0004] In addition, the existing deployment process lacks scientific navigation route planning and anchor point selection method, often leading to cable rebound, anchor system displacement and even equipment loss, seriously affecting the deployment success rate and operation safety. SUMMARY

[0005] In order to solve the problems existing in the prior art, the present application aims to provide a large anchor system buoy deployment method in the complex surrounding of reefs.

[0006] The large anchor system buoy deployment method in the complex surrounding of reefs described in the present application comprises:

[0007] S101, based on high-precision seabed topography data, physical ocean characteristics, bottom conditions, and fishery and shipping activity data, a multi-factor comprehensive analysis is performed to determine the buoy deployment area;

[0008] S102, according to the water depth of the selected deployment area, a reverse catenary anchor system structure is adopted, including an anchor chain connected in sequence from the lower end of the buoy, a plurality of floating balls and counterweights connected in series in the middle, and a heavy anchor at the bottom, wherein the total length of the anchor system is 1.2-1.5 times the water depth, and the positions, number and intervals of the floating balls and counterweights are determined through simulation simulation, for the anchor system to be in a relaxed inverted S shape after deployment;

[0009] S103, based on real-time wind field and flow field data, a navigation route is planned according to the principle of upwind and upflow, the buoy deployment point is set as the starting point, the anchor block deployment point is set as the ending point, and the actual anchor throwing point is determined;

[0010] S104, sailing along the planned path against the wind and current, and sequentially deploying the buoy body, anchor chain, high molecular cable, floating ball and counterweight for throwing the heavy anchor;

[0011] 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.

[0012] S106. Record the entire deployment process and conduct a final quality inspection of the deployed buoy system.

[0013] 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.

[0014] 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.

[0015] 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;

[0016] Finally, the buoy deployment coordinates are generated based on the geographic information system to determine the final deployment point.

[0017] 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;

[0018] 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.

[0019] 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;

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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;

[0024] 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.

[0025] Finally, the complete navigation route from the deployment point to the anchor block deployment point is generated based on the automatic navigation technology, and the smooth navigation trajectory is output after path smoothing processing.

[0026] Preferably, in step S104, the real-time wind flow field data of the target sea area is obtained through the sensor network, the wind flow field parameters are analyzed, and the initial navigation vector in the direction of the adverse wind flow is calculated by vector analysis;

[0027] If it deviates from the preset threshold, the heading is optimized by A* algorithm to generate an optimized navigation vector;

[0028] Based on this, the path nodes from the buoy deployment point to the heavy anchor deployment point are calculated using geographic information systems, and the segmented navigation trajectory is determined;

[0029] Then, the stability of the trajectory in the wind flow field is verified through fluid mechanics simulation to obtain a stable navigation trajectory;

[0030] Accordingly, the deployment sequence of the buoy, anchor chain, cable, floating ball and counterweight is planned through sequence optimization technology to form the final deployment sequence;

[0031] Finally, the complete navigation path is generated by means of automatic navigation technology.

[0032] Preferably, in step S105, the cable tension and ship attitude data are continuously collected through the sensor network, the dynamic trend is extracted through time series analysis, and Kalman filtering is used for smoothing and denoising;

[0033] If the data exceeds the preset threshold, the abnormal risk level is calculated through dynamic simulation;

[0034] According to the level, the decision tree algorithm is used to generate control instructions for power adjustment or suspension of deployment;

[0035] The adjustment scheme is executed through automatic control technology, and the stability of the execution is monitored through real-time feedback;

[0036] If the scheme is stable, record the dynamic data of tension, attitude and control instructions to form the deployment process data.

[0037] Preferably, in step S106, the environment and buoy state data are obtained through the sensor network, and the cleaned data is obtained after removing outliers and noise through data cleaning;

[0038] Based on time series analysis, the dynamic trend of buoy position, speed and environmental parameters is extracted;

[0039] If the trend exceeds the threshold, the risk level of deviation is calculated through kinematics simulation;

[0040] According to the risk level, the decision tree algorithm is used to generate position adjustment or suspension instructions;

[0041] The release speed or angle is adjusted through automatic control to form an optimized deployment scheme.

[0042] The stability of deployment is monitored in real time, and the position, speed, environmental parameters and dynamic data of control instructions are stored to form a structured deployment process record.

[0043] The complex island reef surrounding large anchor line buoy deployment method has the advantages that the complex island reef surrounding large anchor line buoy deployment method is combined with systematic site selection evaluation, reverse catenary anchor line structure design, scientific deployment process and simulation to achieve efficient, safe and accurate deployment of large anchor line buoys in the deep water area surrounding the complex island reef, and has the following beneficial effects:

[0044] The deployment point is scientifically selected through high-precision seabed topography scanning and multi-factor site selection evaluation, combined with GNSS positioning and upwind and top flow deployment strategies, the accuracy of the falling point of the buoy and the anchor block is significantly improved, the reverse catenary design is adopted, combined with the dynamic simulation configuration of the floating ball and the counterweight, so that the anchor line is in the form of "inverted S" in the relaxed state, effectively buffers the impact of wind and waves, adapts to complex marine environment, the number, position and counterweight ratio of the floating ball are optimized through simulation, material waste is reduced, and through scientific deployment process and safety management measures, operation risk is reduced, buoy deployment is more stable, observation data is more reliable, the system has a longer service life in harsh marine environment, the simulation model is introduced to support decision-making, has good scalability and adaptability, and can be used for buoy deployment under different water depth, bottom material and sea condition conditions. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 is the flow of the complex island reef surrounding large anchor line buoy deployment method Figure 1 .

[0046] Figure 2 is the flow of the complex island reef surrounding large anchor line buoy deployment method Figure 2 . DETAILED DESCRIPTION

[0047] As Figures 1-2 shown, the complex island reef surrounding large anchor line buoy deployment method includes:

[0048] As Figures 1-2 shown, in step S101, based on high-precision seabed topography data, physical oceanographic features, bottom conditions, fishery and shipping activity data, the buoy deployment area is determined through multi-factor comprehensive analysis.

[0049] Further, in step S101, the high-precision seabed topography data, physical oceanographic parameters, bottom characteristics and fishery and shipping activity data are obtained and integrated into a unified data set.

[0050] The data set is cleaned and standardized by using data preprocessing techniques to obtain a standardized data set;

[0051] The standardized data set is classified by using the K-means clustering algorithm to determine a set of regions with similar topographic features, oceanographic parameters, and activity distribution;

[0052] If the topographic features and oceanographic parameters of a region in the region set meet the preset threshold, the region suitability is calculated by using a weighted scoring method combined with the bottom characteristics to obtain a suitable deployment region list;

[0053] According to the suitable deployment region list, the spatial analysis technique is used to combine the fishing activities and shipping activities distribution to judge the activity intensity in the region and determine the low-intensity region;

[0054] The low-intensity region is sorted by using an optimization algorithm to obtain a priority region that meets the deployment conditions;

[0055] For the priority region, the geographic information system technology is used to generate the buoy deployment location coordinates to determine the final deployment point.

[0056] Specifically, in step S101, based on high-precision seabed topographic data, first, the multi-beam sounding system is used to obtain seabed topographic grid data with a resolution of 0.5 meters, the Kriging interpolation algorithm is used to smooth the data, a three-dimensional terrain model is generated, the slope change rate is calculated, and flat areas with a slope less than 5 degrees are selected as potential deployment points;

[0057] The physical oceanographic feature analysis uses CTD instruments to collect temperature, salinity, and flow rate data within a range of 0-500 meters in depth, and combines with the ROMS ocean model to simulate the tidal current intensity in the region, and selects regions with an average flow rate lower than 0.5 meters / second to ensure the stability of the buoy;

[0058] The bottom condition uses side-scan sonar to obtain bottom hardness data, and combines with sediment grain size analysis to preferentially select regions with sandy or clayey bottom (hardness value between 50-100 kilopascals) to avoid rock areas to reduce anchoring difficulty;

[0059] The fishing and shipping activity data obtains ship density from the AIS system, and combines with fishing log to calculate the ship passage frequency in the region, and selects regions with an annual average ship density lower than 10 vessels per square kilometer to reduce the collision risk;

[0060] The above data is integrated through the GIS platform, and the weighted superposition analysis method is used to give the topographic, oceanographic feature, bottom, and activity data weights of 0.3, 0.3, 0.2, and 0.2 respectively, to calculate the comprehensive suitability index, and the regions with an index higher than 0.8 are selected as candidate deployment points;

[0061] Finally, through spatial clustering analysis (K-means algorithm, K=3), the center point coordinates (120.5°E, 30.2°N) are determined as the buoy deployment location, ensuring that the deployment area takes into account stability, safety, and operability.

[0062] As shown in Figures 1-2 step S102, the reverse catenary anchor structure is adopted, including the connection of the buoy lower end to the anchor chain, the middle part connected to multiple floating balls and counterweights, and the bottom connected to the heavy anchor. The total length of the anchor chain is 1.2-1.5 times the water depth, and the position, number, and interval of the floating balls and counterweights are determined through simulation to ensure that the anchor chain is in an inverted S shape in a relaxed state.

[0063] Further, in step S102, by measuring the water depth data of the target sea area, the anchor chain length is calculated to obtain the anchor chain length range;

[0064] According to the anchor chain length range, the simulation simulation technology is used to determine the number of floating balls and the position of counterweights to obtain the initial anchor chain configuration;

[0065] If the initial anchor chain configuration presents an inverted S-shaped relaxed state in the simulation, the interval of the floating balls and counterweights is adjusted through an iterative optimization algorithm to obtain an optimized anchor chain configuration;

[0066] According to the optimized anchor chain configuration, the geographic information system technology is used to generate the spatial layout of the anchor chain structure to determine the anchor chain connection point and heavy anchor fixed point coordinates;

[0067] Through fluid mechanics simulation, the stability of the anchor chain structure under the action of sea current is analyzed to determine whether it meets the preset threshold to obtain a stable anchor chain design;

[0068] If the stable anchor chain design meets the preset threshold, the data fusion technology is used to integrate the anchor chain length, the number of floating balls, the position of counterweights, and the spatial layout to generate a final anchor chain deployment scheme;

[0069] According to the final anchor chain deployment scheme, the automatic modeling tool is used to generate detailed deployment coordinates of the connection between the buoy lower end and the anchor chain to determine the final deployment point.

[0070] Specifically, in step S102, based on the design of the reverse catenary anchor structure, the OrcaFlex ocean engineering simulation software is used for full-system dynamic simulation to determine the anchor chain length connected to the buoy lower end, the configuration of the floating balls and counterweights, and the heavy anchor parameters;

[0071] First, for a sea area with a water depth of 500 meters, the total length of the anchor chain is set to 600-700 meters, and through iterative calculation it is determined to be 650 meters to meet the requirement of 1.3 times the water depth;

[0072] The anchor chain is made of high-strength steel chain, with 27.5 meters of one section, high-strength three-level steel, diameter of 38 mm, tension load of 812 kN, and minimum breaking load of 1160 kN

[0073] Ensure that the maximum tidal force is borne;

[0074] In the simulation, the stress distribution of the anchor chain under the action of wind, wave and tidal current is simulated by finite element analysis method, the tidal current speed is set to 0.3 m / s, the peak value of cable tension is calculated to be not more than 150 kN, the tension range in normal working time is 80-120 kN, and the slack state is in "inverted S shape";

[0075] The ball and weight configuration is determined by particle swarm optimization algorithm (PSO), the ball diameter is set to 17 inches (0.4 m, a single ball provides a designed net buoyancy of 170 N, and 26-30 balls are set), each ball is spaced 2 meters apart, and a weight of 200 kg is hung at the end, with a spacing of 15 meters, and the optimization target is to minimize the swing amplitude of the mooring system, the simulation results show that the swing angle is less than 5 degrees, meeting the stability requirement;

[0076] The anchor mass is calculated by the force balance equation and is set to 2 tons, and the anchor holding force needs to be greater than the bottom friction (assuming the bottom friction coefficient is 0.4, the anchor holding force reaches 80 kN), to ensure the stability of the anchor;

[0077] All parameters are simulated 1000 times by OrcaFlex to verify that the mooring system still maintains the "inverted S shape" under extreme wind and wave (wave height 3 meters, period 8 seconds), and the tension fluctuation is less than 10%, confirming the rationality of the design;

[0078] Finally, the system generates mooring system spatial layout data, outputs the coordinates of the ball and weight and the stress curve of the anchor chain, and provides reference for deployment operation.

[0079] As shown in Figures 1-2 Step S103, the sailing route is planned based on the principle of top wind and top flow, the buoy deployment point is set as the starting point, the anchor block deployment point is set as the ending point, and the actual anchor deployment position is determined.

[0080] Further, in step S103, by acquiring real-time wind direction and flow direction data of the target sea area, using environmental monitoring technology, the wind flow field distribution is determined, and the dynamic environmental parameters of the target sea area are obtained;

[0081] According to the dynamic environmental parameters, the vector analysis technology is used to calculate the initial sailing direction under the condition of top wind and top flow, and the initial sailing vector is obtained;

[0082] If the deviation of the initial sailing vector from the preset sailing threshold exceeds the set range, the sailing direction is adjusted by A algorithm to obtain the optimized sailing vector;

[0083] According to the optimized sailing vector, the geographic information system technology is used to calculate the preset coordinates between the starting point and the ending point, and the coordinates of the preliminary anchor dropping are determined;

[0084] Through the fluid mechanics simulation technology, the stability of the actual buoy dropping point in the wind flow field is analyzed, whether the preset threshold is met is judged, and the stable dropping point coordinates are obtained;

[0085] According to the stable dropping point coordinates, the automatic navigation technology is used to generate a complete sailing path from the buoy dropping point to the anchor block dropping point, and the final sailing route is determined;

[0086] Through the path smoothing technology, the final sailing route is optimized to obtain a smooth sailing trajectory.

[0087] Specifically, in step S103, the sailing route is planned based on the principle of against wind and against current, the buoy dropping point is set as the starting point, the anchor block dropping point is set as the ending point, the actual anchor dropping point is determined, and the marine engineering path optimization algorithm is used to generate the sailing route;

[0088] Firstly, the starting point coordinates are set as (0, 0), the ending point anchor block dropping point coordinates are set as (3000, 1000), the unit is meter, the total distance is about 3162.3 meters calculated by straight line distance, and the anchor dropping point coordinates are calculated by linear interpolation as (1000, 333.3);

[0089] The A* algorithm is used for path planning, the grid resolution is set as 50 meters x 50 meters, the wind speed is 10 meters / second (direction 270 degrees, west wind), and the tidal current speed is 0.5 meters / second (direction 180 degrees, southward flow);

[0090] The A* algorithm takes minimizing the sailing time as the target, the ship speed is set as 5 meters / second, after comprehensively considering the wind and flow influence, the actual speed is calculated by vector synthesis as 4.8 meters / second;

[0091] In the path optimization, the environmental resistance coefficient is introduced, the wind resistance coefficient is estimated by Bernoulli equation as 0.02, the tidal current resistance coefficient is estimated as 0.03, the heading angle is dynamically adjusted, and the initial heading angle is 18.4 degrees (calculated by arctangent, atan(1000 / 3000));

[0092] The A* algorithm is iterated 1000 times to generate the optimal path, the total length of the path is optimized to 3200 meters, which is slightly increased than the straight line distance but avoids the high flow speed area (the local tidal current speed reaches 0.7 meters / second);

[0093] In order to verify the path stability, the Monte Carlo method is used to simulate 1000 times, the wind speed fluctuation is ±2 meters / second, the tidal current speed fluctuation is ±0.1 meters / second, the average sailing time is calculated as 667 seconds (about 11.1 minutes), and the deviation is less than 5%;

[0094] The path point coordinates are smoothed by cubic spline interpolation to generate a continuous curve, ensuring smooth sailing of the ship.

[0095] Finally, the system outputs the path point coordinate sequence and the heading angle data for use by the automatic navigation system.

[0096] In one embodiment, the arctangent calculation formula for calculating the heading angle is:

[0097] where θ is the heading angle (radians or degrees), and Δx, Δy are the coordinate differences.

[0098] The linear interpolation calculation formula for the preset coordinates is:

[0099] where (x, y) are the preset coordinates, (x1, y1) are the starting point coordinates, and (x2, y2) are the end point coordinates.

[0100] As shown in FIG. 10, in step S104, the ship sails along the planned path in the direction of the wind flow, and the buoy body, anchor chain, polymer cable, floating ball, and counterweight are sequentially deployed, and finally the heavy anchor is dropped. Figures 1-2

[0101] Further, in step S104, the wind flow field vector of the target sea area is obtained, the real-time wind direction and flow direction data are collected through a sensor network using environmental monitoring technology, the wind flow field distribution characteristics are determined, and the wind flow field parameters are obtained.

[0102] According to the wind flow field parameters, the wind flow field vector is decomposed using vector analysis technology, the initial angle of sailing in the direction of the wind flow is calculated, and the initial sailing vector is obtained.

[0103] If the deviation between the initial sailing vector and the preset sailing threshold exceeds the set range, the A* algorithm is used to adjust the sailing angle, the path is optimized in combination with the wind flow field parameters, and the optimized sailing vector is obtained.

[0104] According to the optimized sailing vector, the path nodes from the buoy deployment point to the heavy anchor dropping point are calculated using geographic information system technology, and the segmented sailing trajectory is determined.

[0105] The stability of the segmented sailing trajectory in the wind flow field is analyzed using fluid mechanics simulation technology, it is determined whether the trajectory meets the preset threshold, and the stable sailing trajectory is obtained.

[0106] According to the stable sailing trajectory, the deployment sequence of the buoy body, anchor chain, polymer cable, floating ball, and counterweight is planned using sequence optimization technology, and the final dropping sequence is determined.

[0107] Based on the final dropping sequence, the complete sailing path from the buoy deployment point to the heavy anchor dropping point is generated using automatic navigation technology, and the final sailing route is determined. ​

[0108] Specifically, in step S104, taking a specific operation as an example, based on the deployment requirements of the ocean engineering buoy system, the buoy body, anchor chain, high molecular cable, floating ball and counterweight, and heavy anchor are sequentially deployed along the upwind and adverse current path, and an automatic control system is used to ensure accurate implementation;

[0109] The system takes the starting coordinate (500, 200) as the buoy body deployment point, and the end point heavy anchor deployment point coordinate (4000, 1500) in meters. The path planning uses an improved Dijkstra algorithm, with a grid resolution of 100 meters x 100 meters. The wind speed is 8 meters / second (direction 300 degrees, northwest wind) and the tidal current speed is 0.4 meters / second (direction 150 degrees, southeast current);

[0110] The ship speed is set to 6 meters / second, the actual speed is calculated by vector synthesis to be 5.7 meters / second, the environmental resistance coefficient is estimated by a fluid mechanics model, the wind resistance is 0.015, and the tidal current resistance is 0.025;

[0111] The buoy body is deployed at the starting point, and the control system automatically adjusts the deployment depth based on depth sensor data (water depth 50 meters), with a target depth of 45 meters and an error of ±0.5 meters. The speed curve is v(t)=0.2t+0.5 meters / second (t is time, seconds) using a PID algorithm to adjust the winch speed;

[0112] The anchor chain is deployed at a distance of 1 / 4 of the path, with coordinates (1375, 575). The length of the anchor chain at the bottom of the buoy is calculated by geometric interpolation to be 37.5 meters. The system monitors the tension sensor to ensure that the tension is within the normal operating range of 80-120 kN;

[0113] The high molecular cable is gradually deployed at the midpoint of the path (2250, 850), with a cable length of 530 meters and a density of 0.95 grams / cubic centimeter. The system calculates the stress distribution of the cable through finite element analysis, with a maximum tension of no more than 150 kN;

[0114] During the deployment of the high molecular cable, the floating ball and counterweight are deployed at a distance of 3 / 4 of the path, with coordinates (3125, 1125). The total buoyancy provided by the floating ball is 4420 N (based on 26 floating balls with a buoyancy of 170 N each). The position of the floating ball is dynamically adjusted during deployment, and the acceleration a=F / m is calculated based on Newton's second law to maintain the stability of the floating ball;

[0115] The heavy anchor is deployed at the end point, and the system ensures that the deployment point error is less than 1 meter through sonar ranging, with a deployment speed of 0.1 meters / second;

[0116] 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%.

[0117] Finally, a sequence of continuous waypoints and delivery parameters is generated for use by the automatic navigation and delivery system.

[0118] In one embodiment, the formula for calculating the vector-synthetic speed is:

[0119] , 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] If the execution scheme is stable, the dynamic data of tension, posture and control instructions are stored through data recording technology to obtain the laying process data.

[0128] Specifically, in step S105, during the laying process of the ocean engineering buoy system, the cable tension and the ship posture are monitored in real time, and the abnormal situation is automatically handled to ensure the stability and accuracy of the laying operation;

[0129] The system collects cable tension data every second with a sampling frequency of 10 Hz, keeps the tension within the normal working range of 80-120 kN, and the peak value does not exceed 150 kN;

[0130] If the tension exceeds 150 kN or is lower than 80 kN, the system judges that there is a risk of cable rebound, automatically triggers the dynamic adjustment algorithm, adjusts the winch speed through the proportional-integral-derivative (PID) controller, the proportional coefficient Kp=0.8, the integral coefficient Ki=0.05, and the differential coefficient Kd=0.1, and the calculation formula is:

[0131] Where e(t) is the tension error, u(t) is the control output (such as the winch speed adjustment amount), K p is the proportional coefficient, K i is the integral coefficient, and K d is the differential coefficient;

[0132] Assuming that the current tension detection value is 160 kN, which has exceeded the peak threshold of 150 kN, the error e(t)=160-150=10 kN, the system calculates the winch speed reduction instruction, and the speed is adjusted to 0.15 m / s, and after 5 seconds, the tension is re-evaluated;

[0133] If the tension returns to 100 kN, the laying continues;

[0134] If it is still abnormal, the winch is paused, and after 10 seconds, it is detected again;

[0135] At the same time, the ship posture is monitored in real time by the inertial measurement unit (IMU), the ship pitch angle and roll angle are collected, and the normal range is ±3 degrees;

[0136] If the pitch angle exceeds 3 degrees, for example, 4.5 degrees is detected, the system fuses IMU and GPS data based on Kalman filtering algorithm to predict the ship posture trend, and the calculation formula is:

[0137] The system automatically adjusts the propeller power, increases the output by 15%, and restores the pitch angle to within 2 degrees, and the adjustment time is controlled within 20 seconds;

[0138] If the posture is still not restored, the system suspends the deployment, starts the backup stabilization mode, applies a correction force of 2kN through the lateral thrusters, calculates the ship acceleration based on Newton's second law F=ma, the ship mass is assumed to be 500 tons, the acceleration a=2 / 500=0.004 m / s², and gradually corrects the posture;

[0139] All data are recorded by the central control system to generate time series logs for subsequent analysis and optimization of the deployment strategy.

[0140] In one embodiment, the Kalman filter state prediction formula is:

[0141] where x k is the state vector (ship posture) at the current time, A is the state transition matrix, x k−1 is the state vector at the previous time, B is the control input matrix, u k is the control input vector, and w k is the process noise.

[0142] where A is the state transition matrix, which is obtained by pre-calibration according to the ship kinematics model, and is usually a unit matrix or calculated based on the sampling period and ship dynamics characteristics;

[0143] B is the control input matrix, reflecting the influence of thruster control on the posture, obtained by system identification or experimental calibration;

[0144] ukis the control input vector, representing the thrust or torque command of the thruster at the current time, generated in real time by the control system.

[0145] As shown in Figures 1-2 step S106, the entire deployment process is recorded, and the final quality inspection of the buoy system is performed to ensure compliance with design requirements.

[0146] Further, in step S106, environmental data and buoy equipment state data are obtained from the deployment environment through a sensor network, data cleaning techniques are used to process the collected data, and outliers and noise are removed to obtain cleaned deployment data;

[0147] According to the cleaned deployment data, time series analysis techniques are used to analyze the change rules of buoy position, velocity and environmental parameters, extract dynamic trend data, and obtain buoy motion trends;

[0148] According to the buoy motion trend, if the buoy position or velocity exceeds the preset threshold, the kinematics simulation technology is used to calculate the possibility and amplitude of the buoy deviation, and the deviation risk level is determined;

[0149] According to the offset risk level, a decision tree algorithm is used to generate control instructions for adjusting the position of the buoy or suspending the deployment, and a deployment adjustment scheme is obtained;

[0150] According to the deployment adjustment scheme, an automatic control technology is used to adjust the release speed or angle parameters of the buoy, and an optimized deployment scheme is generated;

[0151] According to the optimized deployment scheme, real-time monitoring technology is used to continuously collect changes in buoy position, speed and environmental parameters, to determine whether the deployment process is stable, and to obtain deployment state data;

[0152] According to the deployment state data, a data storage technology is used to structure the dynamic data of buoy position, speed, environmental parameters and control instructions, and deployment process record data is obtained.

[0153] Specifically, in step S106, during the deployment of the ocean engineering buoy system, the entire deployment process needs to be recorded, and the final quality inspection of the buoy system is performed to ensure that it meets the design requirements;

[0154] The system records key parameters during the deployment process through a high-precision data acquisition module, including buoy movement, environmental flow speed and buoy vibration frequency;

[0155] 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;

[0156] For example, if the current buoy moves 150.2 meters, the system calculates the buoy movement speed based on the depth data, using the formula 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 calculation gives v=0.2 meters / second;

[0157] If the movement speed exceeds the set range of 0.15-0.25 meters / second, the system automatically adjusts the winch motor power by 10% output, and the target speed returns to 0.2 meters / second. After 3 seconds of adjustment, it is detected again;

[0158] At the same time, the environmental flow speed is measured by a Doppler flowmeter with a sampling frequency of 2Hz and a normal range of 0.1-0.5 meters / second;

[0159] If the flow speed is detected to be 0.6 meters / second, the system analyzes the periodicity of the flow based on the Fourier transform algorithm, using the formula:

[0160] 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 flow frequency characteristics are extracted to determine whether there is vortex interference;

[0161] If vortex is confirmed, the system pauses for 5 seconds, and continues after the water flow speed drops to 0.4 m / s;

[0162] The buoy vibration frequency is monitored by an acceleration sensor, and the normal range is 0.5-2 Hz;

[0163] If the vibration frequency is detected to be 2.5 Hz, the system uses a wavelet transform algorithm to decompose the signal, and the formula is:

[0164] Where ψ is the wavelet base function, s is the scale, τ is the translation parameter, the vibration source is analyzed, f(t) is the signal, and W(s, τ) is the wavelet coefficient;

[0165] If the vibration source is external disturbance, the system increases 0.5 kg of counterweight through the counterweight adjustment module, and calculates the buoy stable force based on the mass-spring system model F=-kx, 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 Newton / meter, and the displacement x=0.01 meter, it is obtained that F=1 Newton, and the stable vibration frequency is 1.5 Hz;

[0166] In the final quality inspection stage, the system detects the buoyancy of the buoy, and the designed buoyancy is 1000±50 Newton;

[0167] The buoyancy sensor measurement is 980 Newton, which meets the requirements, and 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.

[0168] In one embodiment of the complex island reef surrounding large anchor line buoy deployment method, it also includes:

[0169] Based on the high-precision multi-beam echo sounder to obtain the seabed topography and water depth data, combined with physical oceanographic characteristics (waves, currents), bottom conditions (sandy or clay, hardness 50-100 kPa), fishing activities (annual average ship density less than 10 ships per square kilometer) and shipping data, through the GIS platform to carry out multi-factor weighted superposition analysis, calculate the comprehensive suitability index, select the area with index higher than 0.8 as the buoy deployment point, and finally determine the deployment coordinates as East longitude 120.5°, North latitude 30.2°;

[0170] The reverse catenary anchor structure has a total length of 1.3 times of the water depth (for example, when the water depth is 100 meters, the anchor length is 130 meters), and the anchor is sequentially connected from top to bottom as follows: the lower end of the buoy is connected with two sections of 38 mm three-stage anchor chains (each section is 27.5 meters long), then connected with a 50 mm eight-strand high polymer cable, the middle part is connected with 26-30 170N floating balls and a 200 kg counterweight in series, the floating balls and the counterweight are spaced apart by 10 meters and 15 meters respectively, and the bottom is connected with two sections of 38 mm anchor chains and a 2 ton anchor. The OrcaFlex simulation software is used for dynamic simulation to ensure that the anchor is in a "reverse S-shaped" relaxed state under the condition of 0.3 m / s tidal current and 3 m wave height, and the swing angle is less than 5 degrees.

[0171] 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, the A* algorithm is used to plan the navigation path according to the principle of sailing against the wind and the current, the buoy deployment point is set as the starting point (0, 0), the anchor block deployment point is set as the end point (3000, 1000), the anchor throwing point is (1000, 333.3), the path is verified for stability through fluid mechanics simulation, and is smoothed through cubic spline interpolation to generate a continuous navigation trajectory.

[0172] The ship sails in the opposite direction of the wind and current at a speed of 5 m / s, and the buoy body, anchor chain, high polymer cable, floating ball and counterweight are sequentially deployed, and finally the heavy anchor is deployed. During the deployment process, the cable tension (normal range 80-120 kN, peak value not more than 150 kN) and the ship attitude (pitch angle ±3°) are monitored in real time through the tension sensor (sampling frequency 10 Hz) and the IMU unit. If the threshold is exceeded, the winch speed is dynamically adjusted or the deployment is paused through the PID controller.

[0173] During the deployment process, the parameters of the buoy movement depth, flow speed and vibration frequency are continuously recorded, the data is denoised through Kalman filtering and stored, and finally the buoyancy of the buoy is detected through the buoyancy sensor (design value 1000±50 N). If it meets the requirements, a structured log file is generated and stored in the cloud database for subsequent analysis.

[0174] Through systematic site selection, simulation optimization of anchor structure, scientific path planning and real-time dynamic regulation, the efficient, safe and stable deployment of large anchor floating buoys in complex island surrounding deep water areas is realized.

[0175] For those skilled in the art, various corresponding changes and modifications can be made to the above described technical solutions and concepts, and all these changes and modifications should belong to the protection scope of the claims of the present 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 sequentially 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 system is 1.2-1.5 times the water depth, and the position, number, and spacing of the buoys and counterweights are determined through simulation. This ensures that the mooring system forms a relaxed inverted S-shape after deployment. The steps of constructing the reverse catenary mooring system also 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 fluid dynamics simulation to obtain the deployment plan. 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. The vessel navigates against the wind current along the planned route, and sequentially deploys the buoy, anchor chain, polymer cable, buoy, and counterweight for anchoring. The sequential deployment of the buoy, anchor chain, polymer cable, buoy, and counterweight also includes: Real-time wind flow field data is acquired, the navigation vector in the direction of the reverse wind flow is calculated, a segmented navigation trajectory is generated through a path optimization algorithm, the trajectory stability is verified by fluid dynamics simulation, the deployment sequence of buoy body, anchor chain, polymer cable, buoy and counterweight is planned, a complete navigation path is obtained and automated deployment is implemented. During deployment, the cable tension and vessel attitude are monitored in real time. If cable rebound or abnormal attitude occurs, the deployment is addressed by power adjustment or suspension. The entire deployment process is recorded, and a final quality inspection is conducted on the deployed buoy system. The real-time monitoring and handling steps also 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.

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 features, seabed conditions, and fisheries and shipping activity data, a standardized dataset is formed. Clustering algorithms are used to identify sets of regions with similar characteristics. Weighted scores are then applied based on high-precision seabed topography data, physical oceanographic features, and seabed conditions to generate a list of suitable deployment areas. Based on the spatial distribution analysis of fisheries and shipping activities, activity density is determined to identify low-density areas. Priority deployment areas are obtained by ranking them 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 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.

4. 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: High-resolution seabed topographic data were acquired using a multibeam echo sounder. Tidal current intensity was simulated using a CTD instrument and a marine model. Seabed hardness was determined by side-scan sonar and sediment analysis. Vessel traffic density was analyzed based on an AIS system and fisheries logs. A weighted overlay analysis method was used to calculate the comprehensive suitability index.

5. The method for deploying large moored buoys around complex islands and reefs according to claim 1, 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.

6. The method for deploying large moored buoys around complex islands and reefs according to claim 1, 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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