A method and system for intelligent prediction of fishing vessel anchor tension data

Abstract

The present application provides a method and system for intelligently predicting the anchor tension data of a fishing boat, which decomposes the static tension, and then determines the tension deviation index of the target fishing boat anchor according to fatigue data; determines the equilibrium fluctuation ratio of the anchor tension of the target fishing boat by the heel constraint; determines the hydrostatic recovery stiffness of the target fishing boat anchored to maintain the balance of the fishing boat by environmental load information; and then determines the elastic recovery amount of the anchor tension of the target fishing boat by the hydrostatic recovery stiffness; determines the tensile stiffness coefficient of the anchor of the target fishing boat according to the equilibrium fluctuation ratio and the elastic recovery amount, and performs load transfer calculation on the anchor state of the target fishing boat according to the tensile stiffness coefficient, and then obtains the predicted tension data of the anchor of the target fishing boat. The above scheme is based on the tensile characteristics of the anchor tension, and can realize the intelligent prediction of the anchor tension data of the fishing boat under the interference of elastic instability caused by the tension of the anchor chain, thereby improving the sensitivity of the intelligent prediction of the anchor tension data of the fishing boat.

Description

Technical Field

[0001] The present application relates to the technical field of data analysis and prediction, and more specifically, to a method and system for intelligently predicting fishing vessel anchor tension data. Background Art

[0002] Data analysis and prediction refers to the use of data analysis methods and prediction models to extract key features and patterns from historical data and predict future trends or events. These technologies cover a series of steps from data collection, cleaning, processing, to feature extraction, modeling and prediction. Commonly used methods include statistical analysis (such as regression analysis, time series analysis) and machine learning algorithms (such as neural networks, random forests, support vector machines, etc.). In practical applications, data analysis and prediction technology is widely used in various fields, such as market demand forecasting, financial risk management, equipment failure warning, weather forecasting, etc. By analyzing historical data and current conditions, it provides predictions and judgments on future trends.

[0003] In the existing technology, the intelligent prediction of fishing vessel anchor tension data firstly collects and preprocesses the data, including collecting data such as anchor tension and ship parameters, cleaning, denoising and standardizing the data to ensure the accuracy and consistency of the data; then it extracts and selects features, extracts the most critical feature variables for prediction through statistical analysis, correlation analysis and other methods; then, it carries out modeling and training, uses statistical models (such as time series models) or machine learning algorithms (such as random forests, neural networks) to build prediction models, and trains and optimizes them on historical data; then, it verifies and evaluates the model, verifies the accuracy of the model through the test set, and uses indicators such as mean square error (MSE) or root mean square error (RMSE) to evaluate the model performance; finally, prediction and application, using The optimized model predicts the future mooring tension and makes corresponding decision adjustments based on the prediction results, such as optimizing the mooring strategy or performing preventive maintenance. However, in the intelligent prediction of fishing vessel mooring tension data, it is difficult to fully capture the complex relationship between the mooring tension and the changing environment when predicting the mooring tension of fishing vessels, and there is a lack of quantitative analysis of the volatility and resistance of the mooring tension, which makes the prediction of the fishing vessel mooring tension data lose its correlation adaptability, resulting in elastic instability of the anchor chain tension under complex sea conditions, thereby reducing the sensitivity of the intelligent prediction of the fishing vessel mooring tension data. Therefore, how to realize the intelligent prediction of the fishing vessel mooring tension data under the interference of elastic instability caused by the tension of the anchor chain, so as to improve the sensitivity of the intelligent prediction of the fishing vessel mooring tension data, is a problem faced by the industry. Summary of the invention

[0004] The present application provides a method and system for intelligently predicting the anchor tension data of a fishing vessel, which can realize intelligent prediction of the anchor tension data of a fishing vessel under the elastic instability interference generated by the tension of the anchor chain, thereby improving the sensitivity of intelligent prediction of the anchor tension data of a fishing vessel.

[0005] In a first aspect, the present application provides a method for intelligently predicting anchor tension data of a fishing vessel, comprising the following steps:

[0006] Monitor the tension of the anchor chain of the moored fishing vessel to obtain the static tension of the anchor chain of the target fishing vessel when it is moored;

[0007] Decomposing the static tension to obtain fatigue data of the anchor chain tension of the target fishing vessel when it is anchored, and determining the tension deviation index of the anchoring of the target fishing vessel according to the fatigue data;

[0008] Determine the horizontal tension load of the target fishing boat when it is anchored under standard wind pressure, determine the heel constraint of the target fishing boat when it is subjected to crosswind according to the horizontal tension load and the tension deviation index, and then determine the equilibrium fluctuation ratio of the anchor tension of the target fishing boat according to the heel constraint;

[0009] Acquire environmental load information of the working environment of the target fishing boat, determine the hydrostatic recovery stiffness of the target fishing boat when it is anchored to maintain the balance of the fishing boat through the environmental load information; and then determine the elastic recovery amount of the anchoring tension of the target fishing boat through the hydrostatic recovery stiffness;

[0010] The tensile stiffness coefficient of the anchoring of the target fishing boat is determined according to the equilibrium fluctuation ratio and elastic recovery of the anchoring tension of the target fishing boat, and the load transfer operation is performed on the anchoring state of the target fishing boat according to the tensile stiffness coefficient to obtain the predicted tension data of the anchoring of the target fishing boat.

[0011] In some embodiments, decomposing the static tension to obtain fatigue data of the anchor chain tension of the target fishing vessel when anchored specifically includes:

[0012] Determining the tension matching amount of the target fishing vessel when anchored according to the static tension;

[0013] Determine the tension loss of the anchor chain of the target fishing vessel when it is anchored;

[0014] The tension matching amount is mapped to the stretch loss amount to obtain fatigue data of the anchor chain tension when the target fishing vessel is anchored.

[0015] In some embodiments, determining the tension deviation index of the anchoring of the target fishing vessel according to the fatigue data specifically includes:

[0016] Determine the tension relaxation data of the anchoring of the target fishing vessel according to the fatigue data;

[0017] Determine the tension deviation coefficient of the target fishing vessel anchor;

[0018] The tension deviation index of the target fishing vessel anchoring is determined according to the tension relaxation data and the tension deviation coefficient.

[0019] In some embodiments, determining the balanced fluctuation ratio of the anchoring tension of the target fishing vessel by the heel constraint specifically includes:

[0020] Determining the tensile strength index of the anchoring tension of the target fishing vessel according to the heel constraint;

[0021] Determine the load failure probability of the anchoring pull of the target fishing vessel;

[0022] The equilibrium fluctuation ratio of the anchoring tension of the target fishing vessel is determined by the tensile strength index and the load failure probability.

[0023] In some embodiments, determining the hydrostatic recovery stiffness of the target fishing boat when anchored to maintain the balance of the fishing boat through the environmental load information specifically includes:

[0024] Determine the hydrostatic resistance coefficient when the anchoring tension of the target fishing vessel is subjected to inertial drag according to the environmental load information;

[0025] Determine the dynamic tension data segment of the target fishing vessel when it is anchored to maintain the balance of the fishing vessel;

[0026] The hydrostatic restoring stiffness of the target fishing boat when anchored to maintain the balance of the fishing boat is determined according to the hydrostatic resistance coefficient and the dynamic tension data segment.

[0027] In some embodiments, determining the elastic recovery amount of the target fishing boat anchoring tension by the hydrostatic recovery stiffness specifically includes:

[0028] Extracting an elastic failure index of the target fishing vessel anchoring tension from the hydrostatic recovery stiffness;

[0029] Determine the hydrostatic load concentration of the target fishing vessel's anchoring pull;

[0030] The elastic recovery amount of the anchoring tension of the target fishing vessel is determined according to the elastic failure index and the static water load concentration.

[0031] In some embodiments, the static tension of the anchor chain of the target fishing vessel when it is anchored is obtained by reading the target fishing vessel anchor tension monitoring data storage unit.

[0032] In a second aspect, the present application provides an intelligent prediction system for anchor tension data of fishing vessels, comprising:

[0033] A monitoring module is used to monitor the tension of the anchor chain of the moored fishing vessel and obtain the static tension of the anchor chain of the target fishing vessel when it is moored;

[0034] A processing module, for decomposing the static tension to obtain fatigue data of the anchor chain tension of the target fishing vessel when it is anchored, and determining a tension deviation index of the anchored target fishing vessel according to the fatigue data;

[0035] The processing module is further used to determine the horizontal tension load of the target fishing boat when it is anchored under standard wind pressure, determine the heel constraint of the target fishing boat when it is subjected to crosswind according to the horizontal tension load and the tension deviation index, and further determine the equilibrium fluctuation ratio of the anchor tension of the target fishing boat according to the heel constraint;

[0036] The processing module is further used to obtain environmental load information of the working environment of the target fishing boat, and determine the hydrostatic recovery stiffness of the target fishing boat when it is anchored to maintain the balance of the fishing boat through the environmental load information; and then determine the elastic recovery amount of the anchoring tension of the target fishing boat through the hydrostatic recovery stiffness;

[0037] The execution module is used to determine the tensile stiffness coefficient of the anchor of the target fishing boat according to the equilibrium fluctuation ratio and elastic recovery amount of the anchor tension of the target fishing boat, perform load transfer calculation on the anchor state of the target fishing boat according to the tensile stiffness coefficient, and then obtain the predicted tension data of the anchor of the target fishing boat.

[0038] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned intelligent prediction method for fishing vessel anchor tension data.

[0039] In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned intelligent prediction method for fishing vessel anchor tension data.

[0040] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects:

[0041] The intelligent prediction method for fishing vessel anchor tension data provided by the present application first monitors the tension of the anchor chain of the moored fishing vessel to obtain the static tension of the anchor chain when the target fishing vessel is anchored; then, the static tension is decomposed to obtain the fatigue data of the anchor chain tension of the target fishing vessel when anchored, and the tension deviation index of the anchor of the target fishing vessel is determined according to the fatigue data; thirdly, the horizontal tension load of the target fishing vessel anchored under the standard wind pressure is determined, and the heel constraint of the target fishing vessel when subjected to the crosswind is determined according to the horizontal tension load and the tension deviation index, and then the heel constraint of the target fishing vessel when subjected to the crosswind is determined according to the crosswind The equilibrium fluctuation ratio of the mooring tension of the target fishing boat is determined by the tilt constraint; then, the environmental load information of the working environment of the target fishing boat is obtained, and the hydrostatic recovery stiffness of the target fishing boat when anchored to maintain the balance of the fishing boat is determined through the environmental load information; and then the elastic recovery amount of the mooring tension of the target fishing boat is determined from the hydrostatic recovery stiffness; finally, the tensile stiffness coefficient of the mooring of the target fishing boat is determined according to the equilibrium fluctuation ratio and the elastic recovery amount of the mooring tension of the target fishing boat, and the load transfer operation is performed on the mooring state of the target fishing boat according to the tensile stiffness coefficient, so as to obtain the predicted tension data of the mooring of the target fishing boat.

[0042] It can be seen that in the present application, it is possible to realize intelligent prediction of the fishing vessel mooring tension data under the interference of elastic instability caused by the tension of the anchor chain, thereby improving the sensitivity of intelligent prediction of the fishing vessel mooring tension data; firstly, by calculating the equilibrium fluctuation ratio, the robustness and anti-fluctuation ability of the mooring system are comprehensively evaluated to ensure that the tension of the fishing vessel mooring system is kept within a safe range in a dynamic environment, and the resistance of the mooring system to environmental fluctuations is quantified; then, the hydrostatic recovery stiffness is determined according to the environmental load information, which can accurately evaluate the response ability of the mooring system to environmental changes under static conditions. In order to enable the anchor chain tension to be effectively adjusted and adapted under different environmental conditions, the elastic recovery amount is determined. To evaluate the elastic characteristics of the mooring system when maintaining balance; then, the tensile stiffness coefficient of the target fishing vessel anchor is determined according to the equilibrium fluctuation ratio and the elastic recovery amount, which can accurately reflect the actual tensile state of the mooring system under different environmental conditions, so that the tension prediction of the fishing vessel anchoring in a complex environment can maintain good performance and stability; finally, the load transfer operation is performed on the mooring state of the target fishing vessel according to the tensile stiffness coefficient, and then the predicted tension data of the target fishing vessel anchoring is obtained; in summary, the technical solution provided by the present application can realize the intelligent prediction of the fishing vessel anchoring tension data under the elastic instability interference generated by the tension of the anchor chain, thereby improving the sensitivity of the intelligent prediction of the fishing vessel anchoring tension data. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is an exemplary flow chart of a method for intelligently predicting fishing vessel anchor tension data according to some embodiments of the present application;

[0044] Figure 2is a schematic diagram of a fishing boat anchoring model according to some embodiments of the present application;

[0045] Figure 3 is a schematic diagram of an exemplary process of determining the heel constraint of a target fishing boat when it is subjected to a crosswind according to some embodiments of the present application;

[0046] Figure 4 is a schematic diagram of exemplary hardware and / or software of a fishing vessel anchor tension data intelligent prediction system according to some embodiments of the present application;

[0047] Figure 5 It is a structural schematic diagram of a computer device for realizing an intelligent prediction method for fishing vessel anchor tension data according to some embodiments of the present application. DETAILED DESCRIPTION

[0048] The core of the present application is to first monitor the tension of the anchor chain of the moored fishing boat to obtain the static tension of the anchor chain when the target fishing boat is anchored; then, decompose the static tension to obtain the fatigue data of the anchor chain tension of the target fishing boat when it is anchored, and determine the tension deviation index of the anchoring of the target fishing boat according to the fatigue data; thirdly, determine the horizontal tension load of the target fishing boat anchored under the standard wind pressure, determine the heel constraint of the target fishing boat when it is affected by the crosswind according to the horizontal tension load and the tension deviation index, and then determine the heel constraint of the target fishing boat according to the heel constraint. The balance fluctuation ratio of the anchoring tension of the target fishing boat is obtained; then, the environmental load information of the working environment of the target fishing boat is obtained, and the hydrostatic recovery stiffness of the target fishing boat when anchoring to maintain the balance of the fishing boat is determined by the environmental load information; and then the elastic recovery amount of the anchoring tension of the target fishing boat is determined by the hydrostatic recovery stiffness; finally, the tensile stiffness coefficient of the anchoring of the target fishing boat is determined according to the balance fluctuation ratio and elastic recovery amount of the anchoring tension of the target fishing boat, and the load transfer operation is performed on the anchoring state of the target fishing boat according to the tensile stiffness coefficient, and then the predicted tension data of the anchoring of the target fishing boat is obtained. The above scheme is based on the tensile characteristics of the anchoring tension, and can realize the intelligent prediction of the anchoring tension data of the fishing boat under the interference of elastic instability caused by the tension of the anchor chain, thereby improving the sensitivity of the intelligent prediction of the anchoring tension data of the fishing boat.

[0049] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 , which is an exemplary flow chart of a method for intelligently predicting anchor tension data of a fishing vessel according to some embodiments of the present application. The method 100 for intelligently predicting anchor tension data of a fishing vessel mainly includes the following steps:

[0050] In step 101, the tension of the anchor chain of the moored fishing vessel is monitored to obtain the static tension of the anchor chain when the target fishing vessel is anchored.

[0051] In specific implementation, a tension sensor or data recording device is installed on the anchor chain of the fishing boat to continuously monitor the mooring tension of the fishing boat when moored, and the data is transmitted to the data processing center by wireless or wired means for real-time or offline analysis. For example, a sensor can be set to record data once per second to obtain a tension change curve of the anchor chain under the influence of wind and waves, and all data on the tension change curve are stored in the target fishing boat anchor tension monitoring data storage unit as the static tension of the target fishing boat when moored. The static tension of the anchor chain of the target fishing boat when moored can be obtained by reading the target fishing boat anchor tension monitoring data storage unit. In other embodiments, other methods can also be used to determine the static tension of the target fishing boat when anchored, which is not limited here.

[0052] It should be noted that the static tension in the present application is data describing the trend of the force change of the anchor chain during the mooring process over time, and is used to monitor and analyze the changes in the mooring tension state.

[0053] In step 102, the static tension is decomposed to obtain fatigue data of the anchor chain tension of the target fishing vessel when it is anchored, and the tension deviation index of the anchoring of the target fishing vessel is determined based on the fatigue data.

[0054] In some embodiments, decomposing the static tension to obtain fatigue data of the anchor chain tension of the target fishing vessel when it is anchored can be achieved by the following steps:

[0055] Determining the tension matching amount of the target fishing vessel when anchored according to the static tension;

[0056] Determine the tension loss of the anchor chain of the target fishing vessel when it is anchored;

[0057] The tension matching amount is mapped to the stretch loss amount to obtain fatigue data of the anchor chain tension when the target fishing vessel is anchored.

[0058] In specific implementation, first, collect and analyze the static tension during anchoring, and use pattern recognition or statistical analysis methods (such as dynamic time warping DTW or Pearson correlation coefficient) to determine the similarity between the fishing vessel's anchoring tension and the established standard or historical data. The tension matching amount can be determined by calculating the difference between the current tension data and the reference standard (such as the historical average tension curve). For example, the tension matching amount is defined as the deviation value between the actual tension and the standard tension, and the deviation value is used as the tension matching amount of the target fishing vessel when anchored; then, the tensile loss of the anchor chain of the target fishing vessel when anchored can be determined in the following way, that is, by multiplying the tension and the initial length of the anchor chain. The ratio is calculated by multiplying the cross-sectional area of ​​the anchor chain and the elastic modulus, and the ratio is used as the tensile loss of the anchor chain when the target fishing boat is anchored. Alternatively, the corresponding loss value is directly extracted from the known tension-deformation curve to obtain the tensile loss, wherein the tension, the initial length of the anchor chain, the cross-sectional area of ​​the anchor chain and the elastic modulus can be obtained from the standard catalog of anchor chain manufacturing and production, which are not limited here; finally, by establishing a mathematical model, the tension matching amount and the tensile loss amount are mapped, and a linear or nonlinear regression method can usually be used to determine the relationship between the two. This process can be achieved by constructing a functional relationship between the tension matching amount and the tensile loss amount, and finally the fatigue data is obtained. For example, there is a linear relationship between the tension matching amount and the tensile loss amount, and the parameters of the optimal fitting line can be determined by regression analysis to define the fatigue data, which are not limited here.

[0059] It should be noted that the tension matching amount in the present application represents an indicator for measuring the similarity between the current mooring tension and the standard or historical reference tension, and is used to judge the deviation of the mooring tension state; the stretch loss amount represents the tension loss caused by the deformation of the anchor chain under stress, material fatigue and other reasons, and is an indicator for measuring the performance degradation of the anchor chain under the actual tension; the fatigue data represents the limit for judging whether the mooring tension is in a normal and safe working state during the anchoring of the target fishing vessel.

[0060] In some embodiments, determining the tension deviation index of the anchoring of the target fishing vessel according to the fatigue data can be achieved by the following steps:

[0061] Determine the tension relaxation data of the anchoring of the target fishing vessel according to the fatigue data;

[0062] Determine the tension deviation coefficient of the target fishing vessel anchor;

[0063] The tension deviation index of the target fishing vessel anchoring is determined according to the tension relaxation data and the tension deviation coefficient.

[0064] In specific implementation, first, the tension relaxation data can be obtained by monitoring the reduction or relaxation of the tension during the anchoring process, and combined with the fatigue data during anchoring, the relaxation change of the anchor tension under different sea conditions, wind speeds and other environmental factors is calculated. Usually, a strain sensor or a pressure sensor is used to collect the tension data in real time, and compared with the fatigue data, to extract the characteristic data of the tension relaxation. For example, the time and amplitude of the anchor tension falling from the peak value to a certain set threshold value can be recorded, and the characteristic data of the tension relaxation can be used as the tension relaxation data; then, the tension deviation coefficient can be calculated by comparing the actual tension data with the fatigue data, for example, using the standardized deviation or the percentage. The deviation of the tension ratio is used to quantify the degree of tension deviation, that is, tension deviation coefficient = (actual tension - expected tension) / expected tension * 100%, where the expected tension is set based on the standard value of the associated boundary; finally, the tension relaxation data is combined with the tension deviation coefficient to comprehensively calculate the tension deviation index. The tension deviation index can integrate the tension relaxation data and the deviation coefficient through weighted average, linear combination or nonlinear combination. For example, the tension deviation index = weight coefficient 1 * tension relaxation data + weight coefficient 2 * tension deviation coefficient, where weight coefficient 1 and weight coefficient 2 can be determined based on expert experience or tension prediction simulation experiments, and are not limited here.

[0065] It should be noted that the tension relaxation data in the present application is data describing the reduction or relaxation of tension during the anchoring process due to external interference or internal regulation, and is used to evaluate the buffering and recovery capacity of the anchor chain in anchoring; the tension deviation coefficient represents a numerical value that measures the degree of difference between the actual mooring tension and the expected normal tension, and this coefficient reflects the degree of difference between the current tension state and the expected normal state; the tension deviation index represents a numerical indicator of the degree of deviation of the mooring tension relative to the normal state, and is an indicator for evaluating the deviation degree and stability of the anchor chain in anchoring.

[0066] In step 103, the horizontal tension load of the target fishing boat when anchored under standard wind pressure is determined, and the heel constraint of the target fishing boat when subjected to crosswind is determined based on the horizontal tension load and the tension deviation index, and then the equilibrium fluctuation ratio of the mooring tension of the target fishing boat is determined based on the heel constraint.

[0067] It should be noted that the reference Figure 2 As shown in the figure, this is a schematic diagram of the fishing boat anchoring model. The target fishing boat is anchored under standard wind pressure. The mooring line mainly provides mooring force through the tension generated by the deadweight of the catenary and the elastic elongation of the mooring line. The mooring line is suspended under the action of the tension inside the mooring line and its own weight, forming a state that is neither completely tensioned nor completely relaxed. The shape formed in this state is called a catenary, and the tension generated on the catenary is used as the fishing boat anchoring tension.

[0068] In specific implementation, the determination of the horizontal tensile load of the target fishing vessel under the standard wind pressure can be achieved in the following manner, namely: first, according to the meteorological data of the sea area where the target fishing vessel is located, the standard wind pressure data of the area is obtained. These data usually include parameters such as wind speed, wind direction and wind pressure distribution. The data provided by the meteorological station can be used or measured in real time by meteorological sensors installed on the ship. Then, the wind load on the fishing vessel is calculated based on the principles of fluid mechanics using the collected wind pressure data. Then, the calculated wind load is compared with the characteristics of the mooring system (such as the length of the anchor chain, The static load of the fishing boat at anchor under wind pressure is analyzed by combining it with the ship's hull posture. The static load refers to the constant load generated by the fishing boat under wind pressure when there is no dynamic disturbance (such as waves, swells, etc.). According to the load conditions under different wind pressure conditions, a horizontal tension load chart is drawn; finally, the static load data in the horizontal tension load chart is sorted into a horizontal tension load, usually with wind pressure change as the horizontal axis and static load as the vertical axis, to form a static load-wind pressure relationship curve, that is, the horizontal tension load of the target fishing boat anchored under standard wind pressure is obtained.

[0069] It should be noted that the horizontal tensile load in this application refers to a set of continuous static load data borne by the mooring system of the target fishing vessel under specific environmental conditions (such as no waves, no flowing water, and only affected by static factors such as wind pressure). This sequence reflects the load change trend of the mooring system under different static forces (such as different wind pressures), characterizes the stability and safety of the mooring state, and is used to judge the bearing capacity and adaptability of the mooring system under static conditions.

[0070] In addition, it should be noted that different anchoring methods have different effects on the degree of heel of a fishing boat. When a fishing boat is affected by a crosswind, the anchoring force needs to counteract the wind force to prevent the fishing boat from drifting away.

[0071] In some embodiments, reference Figure 3 As shown, this figure is an exemplary flow chart of determining the heel constraint of a target fishing boat when it is subjected to a crosswind according to some embodiments of the present application. In this embodiment, determining the heel constraint of a target fishing boat when it is subjected to a crosswind according to the horizontal tension load and the tension deviation index can be implemented by the following steps:

[0072] In step 1031, the heeling load of the target fishing boat when it is subjected to the crosswind is determined by the horizontal tensile load;

[0073] In step 1032, the heel constraint coefficient of the target fishing boat when it is subjected to the crosswind is determined according to the tension deviation index;

[0074] In step 1033, the heeling constraint of the target fishing boat when it is subjected to the crosswind is determined according to the heeling load and the heeling constraint coefficient.

[0075] In specific implementation, first, the horizontal tension load includes the static load information of the fishing vessel's mooring system under different wind pressure conditions. According to the sequence, the stress condition of the fishing vessel when the crosswind acts on the lateral side of the fishing vessel can be identified. The heeling load can be calculated by simulating the force distribution of the fishing vessel under the crosswind and calculating its influence on the lateral stability of the fishing vessel. It usually includes the analysis of the crosswind moment, that is, calculating the lateral moment generated under the action of wind pressure, and combining it with the geometric parameters of the hull (such as the center of gravity height, cross-sectional shape). For example, the heeling load can be calculated by the following formula, that is, the heeling load = the wind load corresponding to the wind pressure in the horizontal tension load * the center of gravity height of the fishing vessel or the distance from the wind pressure action point to the bottom of the ship. In other In the embodiment, other methods can also be used to determine the heel load, which is not limited here; then, by comparing the changes in the tension deviation index under different wind pressures, the heel constraint coefficient is calculated using regression analysis or curve fitting method. The specific calculation can be determined by coefficient adjustment method or numerical simulation, so that the coefficient can accurately reflect the stress state of the fishing boat under the action of crosswind and the constraint capacity of the mooring system, which will not be repeated here; finally, the heel constraint of the target fishing boat when subjected to the crosswind is determined according to the heel load and the heel constraint coefficient. The following formula can be used, namely: heel constraint = heel load * heel constraint coefficient. In other embodiments, other antique methods can also be used to determine the heel constraint, which is not limited here.

[0076] It should be noted that the heel load in the present application refers to the force or moment that causes the hull to tilt laterally when the fishing boat is subjected to the action of crosswind, and the heel load is an indicator to measure the influence of crosswind on the lateral stability of the hull; the heel constraint coefficient refers to the ability of the mooring system to restrain the lateral tilt of the fishing boat under the action of crosswind, and this coefficient can quantify the effect of the mooring system in suppressing the heel of the fishing boat under different tension offset conditions, so as to ensure that the fishing boat can maintain stability under crosswind conditions; the heel constraint refers to a quantitative indicator of the ability of the mooring system to suppress and limit the lateral tilt of the target fishing boat under the action of crosswind, which reflects how the mooring system resists the lateral tilt caused by crosswind through tension and supporting structure, thereby maintaining the balance and stability of the fishing boat; the heel constraint is used to evaluate the protective effect of the mooring system on the stability of the hull under wind pressure environment, so as to ensure that the fishing boat can maintain a safe posture under different crosswind conditions.

[0077] In some embodiments, determining the equilibrium fluctuation ratio of the anchoring tension of the target fishing vessel by the heel constraint can be achieved by the following steps:

[0078] Determining the tensile strength index of the anchoring tension of the target fishing vessel according to the heel constraint;

[0079] Determine the load failure probability of the anchoring pull of the target fishing vessel;

[0080] The equilibrium fluctuation ratio of the anchoring tension of the target fishing vessel is determined by the tensile strength index and the load failure probability.

[0081] In the specific implementation, first, the relationship between the mooring tension and the heeling load is calculated, and the tensile force index is determined in combination with the design parameters of the mooring system (such as the tensile strength of the anchor chain and the configuration of the anchor point). For example, through experiments or numerical simulations, a mapping model from the heeling constraint to the tensile force index can be established to obtain the specific value of the tensile force index, which will not be repeated here; then, the probability of load failure can be calculated by statistically analyzing the historical data of the mooring tension under various environmental conditions and combining it with simulation. For example, using Monte Carlo simulation or fault tree analysis, based on different tension conditions (including heeling constraint and tensile force) ) calculates the probability of system failure, and factors that need to be considered include the impact of extreme weather conditions, anchor chain wear, hull movement, etc. on the mooring system, and then obtains the load failure probability of the mooring tension of the target fishing vessel. In other embodiments, other methods can also be used to determine the load failure probability of the mooring tension of the target fishing vessel, which is not limited here; finally, determining the balance fluctuation ratio of the mooring tension of the target fishing vessel by the anti-tensile force index and the load failure probability can be achieved in the following manner, namely: balance fluctuation ratio=anti-tensile force index / load failure probability. In other embodiments, other methods can also be used to determine the balance fluctuation ratio, which is not limited here.

[0082] It should be noted that the tensile strength index in the present application represents a quantitative index of the tensile strength required for the mooring system to resist lateral tilt under the action of crosswind, and the tensile strength index reflects the ability of the fishing vessel mooring system to resist tensile fluctuations under the action of crosswind; the load failure probability represents the probability of failure or overload of the mooring system under specific load conditions (such as crosswind); the balanced fluctuation ratio represents a quantitative index of the ability of the mooring system to resist these fluctuations and maintain stability when facing dynamic loads (such as wind and wave fluctuations), and is used to evaluate the resistance of the mooring system to load fluctuations in actual operation. The higher the balanced fluctuation ratio, the stronger the resistance of the mooring system to load fluctuations, and the higher the stability and safety of the system.

[0083] In step 104, the environmental load information of the working environment of the target fishing boat is obtained, and the hydrostatic recovery stiffness of the target fishing boat when anchored to maintain the balance of the fishing boat is determined based on the environmental load information; and then the elastic recovery amount of the anchoring tension of the target fishing boat is determined based on the hydrostatic recovery stiffness.

[0084] In specific implementation, the environmental load information of the working environment of the target fishing vessel can be obtained in the following manner, namely: first, the environmental load information is collected in real time through sensors and monitoring equipment installed on the fishing vessel. These sensors include anemometers, wind vanes, wave sensors, current meters, etc., which can measure environmental parameters such as wind speed, wind direction, wave height, and current velocity. Relevant environmental data can also be obtained through external meteorological stations or satellite meteorological services; then, the obtained environmental data is preprocessed, usually including data cleaning (removing noise and outliers), data conversion (such as converting wind speed into wind load), etc.; then, the environmental load is calculated based on the processed environmental data. The environmental load includes the force or pressure generated by factors such as wind, waves, and current velocity. These loads affect the structure and mooring system of the fishing vessel. Fluid mechanics and mechanical models can be used to calculate these loads. For example, through wind speed and wind direction data, the wind load formula can be used to calculate the impact of wind on the fishing vessel; through wave data, the impact force of waves on the hull is calculated, that is, the environmental load information of the working environment of the target fishing vessel is obtained.

[0085] It should be noted that the environmental load information in this application represents the quantitative data of the load or pressure generated by various external factors in the working environment of the target fishing vessel. The environmental load information is used to evaluate and analyze the carrying capacity and stability of the fishing vessel under actual environmental conditions, ensuring that the fishing vessel can operate safely and reliably under different environmental conditions.

[0086] In some embodiments, determining the hydrostatic recovery stiffness of the target fishing boat when anchored to maintain the balance of the fishing boat through the environmental load information can be achieved by the following steps:

[0087] Determine the hydrostatic resistance coefficient when the anchoring tension of the target fishing vessel is subjected to inertial drag according to the environmental load information;

[0088] Determine the dynamic tension data segment of the target fishing vessel when it is anchored to maintain the balance of the fishing vessel;

[0089] The hydrostatic restoring stiffness of the target fishing boat when anchored to maintain the balance of the fishing boat is determined according to the hydrostatic resistance coefficient and the dynamic tension data segment.

[0090] In the specific implementation, first, according to the environmental load information (such as wind speed, wave height, flow velocity), the static water resistance coefficient is calculated through physical models or experimental data, which specifically includes: recording the wind speed, flow velocity and other data in the environmental load information, using the fluid mechanics model to calculate the resistance of the mooring system under static water conditions, and then inputting the density data of the water area where the fishing boat is located, the frontal surface area of ​​the mooring system and the relative flow rate of the water in the water area into the resistance model, and using the output result as the static water resistance coefficient, wherein the resistance model can be queried through resistance simulation experiments or fluid resistance reference books, which is not limited here; then, using sensors and data recording equipment, real-time monitoring and recording of the tension data of the mooring system under different environmental conditions, and then analyzing the tension data under different environmental conditions to obtain the static water resistance coefficient. Identify the tension change trend and amplitude of the mooring system in the process of maintaining balance. Data processing may include calculating the mean, standard deviation and range of tension, and then generating a dynamic tension data segment, which will not be repeated here; finally, determine the hydrostatic recovery stiffness of the target fishing boat anchored to maintain the balance of the fishing boat based on the hydrostatic resistance coefficient and the dynamic tension data segment. This can be achieved in the following way, namely: hydrostatic recovery stiffness = hydrostatic resistance coefficient * tension change in the dynamic tension data segment, wherein the tension change in the dynamic tension data segment can be determined by the slope of the tension data in the dynamic tension data segment; the hydrostatic recovery stiffness of the target fishing boat anchored to maintain the balance of the fishing boat in various cases is used as the hydrostatic recovery stiffness of the target fishing boat anchored to maintain the balance of the fishing boat, which will not be repeated here.

[0091] It should be noted that the hydrostatic resistance coefficient in the present application represents the ratio between the resistance generated by the fluid (such as water flow, wind) of the mooring system and the fluid dynamics in a still water environment. The hydrostatic resistance coefficient quantifies the mooring system's resistance to fluid flow under still water conditions; the dynamic tension data segment represents a data set of tension changes experienced by the fishing vessel's mooring system under different environmental conditions, and is used to measure the load fluctuations borne by the fishing vessel's mooring system during the balancing process; the hydrostatic recovery stiffness represents a quantitative indicator of the mooring system's ability to maintain the balance of the fishing vessel in a still water environment, and reflects the system's ability to respond to load changes under still water conditions.

[0092] In some embodiments, determining the elastic recovery amount of the anchoring tension of the target fishing vessel by the hydrostatic recovery stiffness can be achieved by the following steps:

[0093] Extracting an elastic failure index of the target fishing vessel anchoring tension from the hydrostatic recovery stiffness;

[0094] Determine the hydrostatic load concentration of the target fishing vessel's anchoring pull;

[0095] The elastic recovery amount of the anchoring tension of the target fishing vessel is determined according to the elastic failure index and the static water load concentration.

[0096] In specific implementation, first, the elastic performance of the mooring system is analyzed from the hydrostatic recovery stiffness, and the elastic failure index can be determined by calculating the stress-strain relationship of the system. For example, the limit point of elastic deformation is identified through the stress-strain curve generated by the hydrostatic recovery stiffness, and then, the elastic value and elastic modulus corresponding to the limit point of elastic deformation are used to calculate the ratio, and the ratio is used as the elastic failure index. In other embodiments, other methods can also be used to determine the elastic failure index, which is not limited here; then, the load on the anchor is analyzed through the hydrostatic recovery stiffness and environmental load information. The distribution of loads in the mooring system, the load concentration usually involves a statistical analysis of the stress conditions of each part of the mooring system, and the hydrostatic load concentration can be determined by the following formula, that is, hydrostatic load concentration = standard deviation of load / average value of load. In other embodiments, other methods can also be used to determine the hydrostatic load concentration, which is not limited here; finally, the elastic recovery amount of the mooring tension of the target fishing boat can be determined according to the elastic failure index and the hydrostatic load concentration. The following method can be used, that is, elastic recovery amount = elastic failure index * hydrostatic load concentration, which is not limited here.

[0097] It should be noted that the elastic failure index in the present application represents the ultimate quantitative index of the elastic response of the mooring system under hydrostatic load, and the elastic failure index is used to describe the limit of the elastic response of the mooring system under hydrostatic conditions; the hydrostatic load concentration represents the degree of distribution concentration of the load on the mooring system under hydrostatic conditions; the elastic recovery amount represents the configuration amount required to maintain the elastic stability of the mooring system under hydrostatic conditions, including the adjustment of material properties and design parameters, to ensure the stability of the system under load.

[0098] In step 105, the tensile stiffness coefficient of the target fishing boat anchor is determined according to the equilibrium fluctuation ratio and elastic recovery amount of the target fishing boat anchoring tension, and the load transfer operation is performed on the target fishing boat anchoring state according to the tensile stiffness coefficient to obtain the predicted tension data of the target fishing boat anchoring.

[0099] In some embodiments, determining the tensile stiffness coefficient of the target fishing boat anchoring according to the equilibrium fluctuation ratio and elastic recovery amount of the target fishing boat anchoring tension can be achieved by the following steps:

[0100] determining the mooring tensile load of the target fishing vessel anchored according to the elastic recovery amount;

[0101] The tensile stiffness coefficient of the target fishing vessel anchoring is determined by the mooring tensile load and the balanced wave ratio.

[0102] In specific implementation, first, the elastic recovery amount is taken as input, and the tensile load of the mooring system is calculated in combination with the design parameters and the actual environmental conditions. The tensile load can usually be calculated using an elastic mechanics model, that is, anchor tensile load = elastic recovery amount * effective area or length of the mooring system. In other embodiments, other methods can also be used to determine the mooring tensile load, which is not limited here; then, the tensile stiffness coefficient of the target fishing boat anchored is determined by the mooring tensile load and the balanced fluctuation ratio. It can be determined in the following manner, that is, tensile stiffness coefficient = mooring tensile load / balanced fluctuation ratio, which will not be repeated here.

[0103] It should be noted that the mooring tensile load in this application refers to the tensile force generated by the elastic configuration of the mooring system under still water conditions, which is used to measure the carrying capacity of the mooring system of a fishing vessel; the tensile stiffness coefficient represents the tensile capacity and stability index of the mooring system under actual environmental conditions.

[0104] In specific implementation, the load transfer operation is performed on the anchoring state of the target fishing vessel according to the tensile stiffness coefficient, and then the predicted tension data of the anchoring of the target fishing vessel is obtained. The following steps can be adopted, namely: first, the real-time tension data of the target fishing vessel under different environmental conditions are collected, including historical data and current environmental condition data. These data should include the time series of tension and related environmental load information, such as wind speed, wave height, etc.; then, the tensile stiffness coefficient is used as the key parameter of the tension prediction model during the load transfer operation, and a dynamic prediction model is established. The model can be based on statistical methods (such as time series analysis), machine learning algorithms (such as regression analysis, neural networks) Or physical modeling (such as system dynamics model), the input of the model includes tensile stiffness coefficient, historical tension data and environmental condition data; then, the tensile stiffness coefficient is used as an input parameter in the model, and the relevant parameters in the model are adjusted. The tensile stiffness coefficient reflects the stability of the mooring system under different load conditions. By adjusting the model parameters, the changing trend of the tension data can be more accurately predicted; finally, the prediction model is run to generate the tension data of the target fishing vessel anchored in the future time period according to the current environmental conditions and the tensile stiffness coefficient. The model will output the trend, fluctuation range and possible extreme values ​​of the future tension. For example, using technologies such as Rolling Forecast, the model is dynamically updated to adapt to real-time data and environmental changes, and the prediction results are analyzed to evaluate the accuracy and reliability of the prediction, check the degree of consistency between the predicted data and the actual tension data, and adjust the model to improve the prediction accuracy, thereby completing the dynamic prediction of the tension data of the target fishing vessel anchored. I will not go into details here.

[0105] In addition, in another aspect of the present application, in some embodiments, the present application provides an intelligent prediction system for anchor tension data of fishing vessels, referring to Figure 4, which is a schematic diagram of exemplary hardware and / or software of a fishing vessel anchoring tension data intelligent prediction system according to some embodiments of the present application, the fishing vessel anchoring tension data intelligent prediction system 400 includes: a monitoring module 401, a processing module 402 and an execution module 403, which are respectively described as follows:

[0106] Monitoring module 401, in this application, the monitoring module 401 is mainly used to monitor the tension of the anchor chain of the moored fishing boat, and obtain the static tension of the anchor chain of the target fishing boat when it is moored;

[0107] Processing module 402, in the present application, the processing module 402 is mainly used to decompose the static tension, obtain fatigue data of the anchor chain tension of the target fishing boat when it is anchored, and determine the tension deviation index of the anchored target fishing boat according to the fatigue data;

[0108] The processing module 402 is further used to determine the horizontal tension load of the target fishing boat under standard wind pressure when anchored, determine the heel constraint of the target fishing boat when subjected to crosswind according to the horizontal tension load and the tension deviation index, and further determine the equilibrium fluctuation ratio of the anchor tension of the target fishing boat according to the heel constraint;

[0109] The processing module 402 is further used to obtain environmental load information of the working environment of the target fishing boat, and determine the hydrostatic recovery stiffness of the target fishing boat when it is anchored to maintain the balance of the fishing boat through the environmental load information; and then determine the elastic recovery amount of the anchoring tension of the target fishing boat through the hydrostatic recovery stiffness;

[0110] Execution module 403, in the present application, execution module 403 is mainly used to determine the tensile stiffness coefficient of the anchoring of the target fishing boat according to the equilibrium fluctuation ratio and elastic recovery amount of the anchoring tension of the target fishing boat, perform load transfer calculation on the anchoring state of the target fishing boat according to the tensile stiffness coefficient, and then obtain the predicted tension data of the anchoring of the target fishing boat.

[0111] In addition, the present application also provides a computer device, which includes a memory and a processor, the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned intelligent prediction method for fishing vessel anchor tension data.

[0112] In some embodiments, reference Figure 5 , which is a schematic diagram of the structure of a computer device for implementing a method for intelligently predicting fishing boat anchor tension data according to some embodiments of the present application. The method for intelligently predicting fishing boat anchor tension data in the above embodiment can be Figure 5 The computer device 500 shown in the figure is implemented, and the computer device 500 includes at least one processor 501, a communication bus 502, a memory 503 and at least one communication interface 504.

[0113] The processor 501 may be a general-purpose central processing unit (CPU), or an application-specific integrated circuit (ASIC) or one or more processors for controlling the execution of the intelligent prediction method for anchor tension data of fishing vessels in the present application.

[0114] The communication bus 502 may be used to transmit information between the above-mentioned components.

[0115] The memory 503 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 503 may exist independently and be connected to the processor 501 via the communication bus 502. The memory 503 may also be integrated with the processor 501.

[0116] The memory 503 is used to store the program code for executing the solution of the present application, and the execution is controlled by the processor 501. The processor 501 is used to execute the program code stored in the memory 503. The program code may include one or more software modules. The intelligent prediction method for anchor tension data of fishing vessels in the above embodiment can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0117] The communication interface 504 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0118] In a specific implementation, as an embodiment, a computer device may include multiple processors, each of which may be a single-CPU processor or a multi-CPU processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0119] The above-mentioned computer device may be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device may be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device or an embedded device. The embodiment of the present application does not limit the type of computer device.

[0120] In addition, the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned intelligent prediction method for fishing vessel anchor tension data is implemented.

[0121] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0122] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for intelligent prediction of fishing vessel anchor tension data, characterized in that: The steps include: Monitor the tension of the anchor chain of the moored fishing vessel to obtain the static tension of the anchor chain of the target fishing vessel when it is moored; Decomposing the static tension to obtain fatigue data of the anchor chain tension of the target fishing vessel when it is anchored, and determining the tension deviation index of the anchoring of the target fishing vessel according to the fatigue data; Determine the horizontal tension load of the target fishing boat when it is anchored under standard wind pressure, determine the heel constraint of the target fishing boat when it is subjected to crosswind according to the horizontal tension load and the tension deviation index, and then determine the equilibrium fluctuation ratio of the anchor tension of the target fishing boat according to the heel constraint; Acquire environmental load information of the working environment of the target fishing boat, determine the hydrostatic recovery stiffness of the target fishing boat when it is anchored to maintain the balance of the fishing boat through the environmental load information; and then determine the elastic recovery amount of the anchoring tension of the target fishing boat through the hydrostatic recovery stiffness; The tensile stiffness coefficient of the anchoring of the target fishing boat is determined according to the equilibrium fluctuation ratio and elastic recovery of the anchoring tension of the target fishing boat, and the load transfer operation is performed on the anchoring state of the target fishing boat according to the tensile stiffness coefficient to obtain the predicted tension data of the anchoring of the target fishing boat.

2. The method according to claim 1, characterized in that Decomposing the static tension to obtain fatigue data of the anchor chain tension of the target fishing boat when it is anchored specifically includes: Determining the tension matching amount of the target fishing vessel when anchored according to the static tension; Determine the tension loss of the anchor chain of the target fishing vessel when it is anchored; The tension matching amount is mapped to the stretch loss amount to obtain fatigue data of the anchor chain tension when the target fishing vessel is anchored.

3. The method according to claim 1, characterized in that Determining the tension deviation index of the target fishing vessel anchoring according to the fatigue data specifically includes: Determine the tension relaxation data of the anchoring of the target fishing vessel according to the fatigue data; Determine the tension deviation coefficient of the target fishing vessel anchor; The tension deviation index of the target fishing vessel anchoring is determined according to the tension relaxation data and the tension deviation coefficient.

4. The method according to claim 1, characterized in that The balanced fluctuation ratio of the anchoring tension of the target fishing vessel determined by the heel constraint specifically includes: Determining the tensile strength index of the anchoring tension of the target fishing vessel according to the heel constraint; Determine the load failure probability of the anchoring pull of the target fishing vessel; The equilibrium fluctuation ratio of the anchoring tension of the target fishing vessel is determined by the tensile strength index and the load failure probability.

5. The method according to claim 1, characterized in that Determining the hydrostatic recovery stiffness of the target fishing boat when anchoring to maintain the balance of the fishing boat through the environmental load information specifically includes: Determine the hydrostatic resistance coefficient when the anchoring tension of the target fishing vessel is subjected to inertial drag according to the environmental load information; Determine the dynamic tension data segment of the target fishing vessel when it is anchored to maintain the balance of the fishing vessel; The hydrostatic restoring stiffness of the target fishing boat when anchored to maintain the balance of the fishing boat is determined according to the hydrostatic resistance coefficient and the dynamic tension data segment.

6. The method according to claim 1, characterized in that The elastic recovery amount of the target fishing boat anchoring tension determined by the hydrostatic recovery stiffness specifically includes: Extracting an elastic failure index of the target fishing vessel anchoring tension from the hydrostatic recovery stiffness; Determine the hydrostatic load concentration of the target fishing vessel's anchoring pull; The elastic recovery amount of the anchoring tension of the target fishing vessel is determined according to the elastic failure index and the static water load concentration.

7. The method according to claim 1, characterized in that The static tension of the anchor chain of the target fishing vessel when it is anchored is obtained by reading the anchor tension monitoring data storage unit of the target fishing vessel.

8. An intelligent prediction system for fishing vessel anchor tension data, characterized in that: include: A monitoring module is used to monitor the tension of the anchor chain of the moored fishing vessel and obtain the static tension of the anchor chain of the target fishing vessel when it is moored; A processing module, for decomposing the static tension to obtain fatigue data of the anchor chain tension of the target fishing vessel when it is anchored, and determining a tension deviation index of the anchored target fishing vessel according to the fatigue data; The processing module is further used to determine the horizontal tension load of the target fishing boat when it is anchored under standard wind pressure, determine the heel constraint of the target fishing boat when it is subjected to crosswind according to the horizontal tension load and the tension deviation index, and further determine the equilibrium fluctuation ratio of the anchor tension of the target fishing boat according to the heel constraint; The processing module is further used to obtain environmental load information of the working environment of the target fishing boat, and determine the hydrostatic recovery stiffness of the target fishing boat when it is anchored to maintain the balance of the fishing boat through the environmental load information; and then determine the elastic recovery amount of the anchoring tension of the target fishing boat through the hydrostatic recovery stiffness; The execution module is used to determine the tensile stiffness coefficient of the anchor of the target fishing boat according to the equilibrium fluctuation ratio and elastic recovery amount of the anchor tension of the target fishing boat, perform load transfer calculation on the anchor state of the target fishing boat according to the tensile stiffness coefficient, and then obtain the predicted tension data of the anchor of the target fishing boat.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores codes, and the processor is configured to obtain the codes and execute the method for intelligent prediction of fishing vessel anchor tension data according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for intelligently predicting fishing vessel anchor tension data according to any one of claims 1 to 7 is implemented.

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