Fishing boat impact force prediction method based on AI

Abstract

The invention discloses a fishing boat impact force prediction method based on AI, and belongs to the technical field of fishing boat impact, and the method comprises the steps: constructing a multi-boat motion state data set; calculating a relative distance and a relative azimuth angle of each pair of fishing boats according to the multi-boat motion state data set, and extracting a distance change rate and an azimuth angle change rate; if the relative distance of each pair of fishing boats is lower than a preset safety distance threshold value, the collision risk probability of each pair of fishing boats is obtained in combination with the azimuth angle change rate; the collision force is predicted through the collision risk probability; according to the impact force value and the collision risk probability, generating a collision risk level of each boat, and dynamically grouping the fishing boats to obtain an optimized formation structure; and according to the optimized formation structure, the avoidance course adjustment angle and speed variation of each ship are calculated, an emergency avoidance instruction is generated, and the executed multi-ship cooperative motion state is obtained. According to the invention, intelligent collaborative collision avoidance of the fishing boat group is realized, and the safety of marine navigation is obviously improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of fishing boat impact prediction, and particularly relates to an AI-based fishing boat impact force prediction method. Background Art

[0002] As a globally important economic and food pillar industry, the safe and efficient operation of marine fishery is crucial for ensuring food security and the safety of fishermen. However, when fishing boats operate in complex and changeable marine environments, they frequently face the risk of collisions caused by multi-boat collaborative operations, which poses extremely high technical requirements for ship navigation and formation management.

[0003] Currently, fishing boat collision prevention mainly relies on traditional Automatic Identification Systems (AIS) and radar monitoring. However, these methods have significant limitations when dealing with dynamic multi-boat scenarios. They often cannot accurately capture the relative azimuth changes of multiple boats in real time, making it difficult to accurately predict potential collision risks. Especially in dense fishing grounds or bad weather, insufficient data fusion and limited prediction capabilities lead to frequent misjudgments or missed judgments.

[0004] In view of the problems in the prior art, there is an urgent need to propose an AI-based fishing boat impact force prediction method that integrates multi-source sensor data through artificial intelligence technology to real-time track the relative azimuth relationship of multiple boats and then predict potential impact forces. Summary of the Invention

[0005] To solve the above technical problems, the present invention proposes an AI-based fishing boat impact force prediction method to solve the problems existing in the above prior art.

[0006] To achieve the above object, the present invention provides an AI-based fishing boat impact force prediction method, and the method includes the following steps:

[0007] Obtain AIS, radar, and optical sensor data through multi-source sensors, and fuse them to generate a real-time multi-boat motion state data set;

[0008] According to the multi-boat motion state data set, calculate the relative distance and relative azimuth angle of each pair of fishing boats, and use time series modeling methods to extract the distance change rate and azimuth angle change rate;

[0009] If the relative distance of each pair of fishing boats is lower than a preset safety distance threshold, then combine the azimuth angle change rate and use a machine learning model to obtain the collision risk probability of each pair of fishing boats;

[0010] Predict the impact force through the collision risk probability using a dynamics model and quantify the impact force value;

[0011] Generate the collision risk level of each ship according to the impact force value and the collision risk probability, and then use a clustering algorithm to dynamically group the fishing boats to obtain an optimized formation structure;

[0012] If the collision risk level is higher than the preset risk threshold, then according to the optimized formation structure, calculate the avoidance course adjustment angle and speed change amount of each ship, and generate an emergency avoidance instruction;

[0013] Use the real-time communication protocol to send the emergency avoidance instruction to each fishing boat navigation system, update the navigation parameters of each ship, and obtain the multi-ship collaborative motion state after execution.

[0014] Optionally, the obtaining AIS, radar and optical sensor data through multi-source sensors and fusing them to generate a real-time multi-ship motion state data set includes:

[0015] Collect AIS data, radar data and optical data through multi-source sensors and store them as the original data set;

[0016] Use the Kalman filter algorithm to perform time synchronization processing on the original data set to obtain a time-aligned data set;

[0017] Complement the missing values in the time-aligned data set through linear interpolation method to obtain a complete data set;

[0018] According to the complete data set, use the weighted average method to fuse AIS, radar and optical data to generate a fused real-time multi-ship motion state data set, and the multi-ship motion state data set includes ship longitude and latitude, ship speed and ship course.

[0019] Optionally, the calculating the relative distance and relative azimuth angle of each pair of fishing boats according to the multi-ship motion state data set and using the time series modeling method to extract the distance change rate and azimuth angle change rate includes:

[0020] Obtain the longitude and latitude, speed and course of each pair of fishing boats from the multi-ship motion state data set, calculate the relative distance of each pair of fishing boats based on the Euclidean distance formula, and calculate the relative azimuth angle of each pair of fishing boats based on the arctangent function;

[0021] Use the time series analysis method to process the relative distance and relative azimuth angle, and calculate the distance change rate and azimuth angle change rate through a sliding window.

[0022] Optionally, the if the relative distance of each pair of fishing boats is lower than the preset safety distance threshold, then combining the azimuth angle change rate, use a machine learning model to obtain the collision risk probability of each pair of fishing boats, includes:

[0023] If the relative distance continues to decrease and is lower than the preset safety distance threshold, then generate a dynamic feature data set based on the relative distance and azimuth angle change rate of each pair of fishing boats;

[0024] Process the dynamic feature dataset through the sliding window method, calculate the continuous decreasing trend of the relative distance and the temporal features of the azimuth change rate, and obtain the temporal feature set;

[0025] Use the random forest algorithm to classify the temporal feature set, judge the collision risk of each pair of fishing boats, and generate the collision risk classification result;

[0026] Based on the collision risk classification result, combine the relative distance and the azimuth change rate, and predict the collision risk probability through the pre-trained logistic regression model to obtain the collision risk probability value of each pair of fishing boats.

[0027] Optionally, using the collision risk probability, predict the impact force by the dynamic model and quantify the impact force value, including:

[0028] Obtain the hull mass and relative speed of each pair of fishing boats from the multi-ship motion state dataset, and combine the collision risk probability to generate a feature dataset containing mass parameters, speed parameters, and risk probability;

[0029] Process the feature dataset through the sliding window method, calculate the temporal change trend of the relative speed and the distribution characteristics of the hull mass, and generate the temporal feature set;

[0030] Use the dynamic model to process the temporal feature set, calculate the potential impact force of each pair of fishing boats, and quantify the impact force value.

[0031] Optionally, generating the collision risk level of each ship according to the impact force value and the collision risk probability, and using the clustering algorithm to dynamically group the fishing boats to obtain the optimized formation structure, including:

[0032] Use the statistical method to jointly analyze the impact force value and the collision risk probability. If the impact force value exceeds the preset impact threshold and the collision risk probability is higher than the preset risk threshold, it is determined as a high-risk collision scenario;

[0033] Dynamically group the fishing boats in the high-risk scenario set through the K-means clustering algorithm, generate the collision risk level of the fishing boats based on the collision risk probability and the impact force, and obtain the collision risk level set;

[0034] According to the collision risk level set, use the sorting method to divide the fishing boats into priorities to obtain the optimized formation structure.

[0035] Optionally, if the collision risk level is higher than the preset risk threshold, then according to the optimized formation structure, calculate the avoidance course adjustment angle and speed change amount of each ship, and generate an emergency avoidance instruction, including:

[0036] According to the optimized formation structure, a path planning algorithm is used to calculate the avoidance course adjustment angles of each fishing boat, and a set of course adjustment angles is obtained;

[0037] If the adjustment angles in the set of course adjustment angles exceed the preset range, the speed change amounts of each fishing boat are calculated through a dynamic model, and a set of speed change amounts is obtained;

[0038] A fusion method is used to jointly process the set of course adjustment angles and the set of speed change amounts to generate an emergency avoidance instruction that meets the safety distance constraint.

[0039] Optionally, after the emergency avoidance instruction is sent to the navigation system of each fishing boat by using a real-time communication protocol to update the navigation parameters of each boat and obtain the multi-ship cooperative motion state after execution, it further includes:

[0040] According to the multi-ship cooperative motion state after execution, the relative distance and azimuth change rate of each pair of ships are repeatedly calculated to obtain an updated collision risk probability; if the updated collision risk probability is lower than the preset risk threshold, the current formation and navigation parameters are maintained, otherwise a new optimized formation structure is generated to obtain a new avoidance instruction.

[0041] The present invention also provides a computer device, including a memory, a processor, and a computer program stored on the memory, and the processor executes the computer program to implement the steps of the method.

[0042] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method are implemented.

[0043] Compared with the prior art, the present invention has the following advantages and technical effects:

[0044] The present invention discloses an AI-based fishing boat impact force prediction method. This method obtains the real-time multi-ship motion state through multi-source sensor fusion, calculates the relative motion characteristics between ships, and combines a machine learning model to predict the collision risk. Based on the collision risk and potential impact force, the present invention dynamically groups fishing boats to optimize the formation structure and generates emergency avoidance instructions in high-risk situations. The instructions are sent to the navigation system of each boat through real-time communication to achieve multi-ship cooperative collision avoidance. The present invention also verifies the avoidance effect through iterative calculation and re-optimizes the formation structure when necessary, so as to continuously ensure navigation safety. This method effectively integrates technologies such as multi-source data fusion, machine learning prediction, and dynamic formation optimization, realizes the intelligent cooperative collision avoidance of fishing boat groups, and significantly improves the safety of maritime navigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings, which form a part of this application, are used to provide a further understanding of this application. The schematic embodiments and descriptions thereof of this application are used to explain this application and do not constitute an improper limitation of this application. In the drawings:

[0046] Figure 1 It is a flowchart of the method according to the embodiment of the present invention. Detailed implementation manners

[0047] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments may be combined with each other. The following will refer to the accompanying drawings and combine the embodiments to detail this application.

[0048] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0049] Embodiment 1

[0050] As Figure 1 shown, in this embodiment, a method for predicting the impact force of fishing boats based on AI is provided. The method includes the following steps:

[0051] Obtain AIS, radar, and optical sensor data through multi-source sensors, and fuse them to generate a real-time multi-ship motion state data set;

[0052] According to the multi-ship motion state data set, calculate the relative distance and relative azimuth angle of each pair of fishing boats, and use the time series modeling method to extract the distance change rate and azimuth angle change rate;

[0053] If the relative distance of each pair of fishing boats is lower than the preset safe distance threshold, then combine the azimuth angle change rate and use a machine learning model to obtain the collision risk probability of each pair of fishing boats;

[0054] Through the collision risk probability, use a dynamic model to predict the impact force and quantify the impact force value;

[0055] According to the impact force value and the collision risk probability, generate the collision risk level of each ship, and then use a clustering algorithm to dynamically group the fishing boats to obtain an optimized formation structure;

[0056] If the collision risk level is higher than the preset risk threshold, then according to the optimized formation structure, calculate the avoidance course adjustment angle and speed change amount of each ship, and generate an emergency avoidance instruction;

[0057] Use a real-time communication protocol to send the emergency avoidance instruction to the navigation system of each fishing boat, update the navigation parameters of each ship, and obtain the multi-ship cooperative motion state after execution.

[0058] As a specific implementation manner, it specifically includes the following steps:

[0059] S101. Obtain AIS, radar, and optical sensor data through multi-source sensors, fuse them to generate a real-time multi-ship motion state data set, which includes the longitude, latitude, speed, and heading of each ship, and obtain a unified multi-ship motion state description.

[0060] Collect AIS data, radar data, and optical data through multi-source sensors and store them as the original data set. Use the Kalman filtering algorithm to perform time synchronization processing on the original data set to obtain a time-aligned data set. If there are missing data in the time-aligned data set, complete the missing values by the linear interpolation method to obtain a complete data set. According to the complete data set, fuse AIS, radar, and optical data by the weighted average method to generate a fused data set, which includes ship longitude, ship speed, and ship heading. Extract the motion states of each ship from the fused data set through cluster analysis to obtain a multi-ship motion state set. For the multi-ship motion state set, perform unified formatting processing to generate a unified multi-ship motion description. If there are abnormalities in the ship motion states in the unified description, screen them through a preset threshold to obtain the final multi-ship motion state description.

[0061] Implementably, collect ship-related data through multi-source sensors. AIS data provides information such as ship identity, position, and speed, and is usually updated in seconds; radar data obtains the distance and azimuth of the ship through electromagnetic wave reflection and is suitable for long-distance detection; optical data uses a camera to capture ship images and is suitable for high-precision close-range identification. Assume that an AIS receiver, shore-based radar, and optical camera are deployed at a certain port to collect data of a cargo ship: the AIS reports the longitude and latitude of 120.5°E, 30.2°N, and the speed of 10 knots; the radar detects a distance of 5 km and an azimuth angle of 30°; the optical data confirms the ship's contour. These data are stored as the original data set in the format of time stamps and sensor types.

[0062] In a possible implementation manner, the Kalman filter is used for time synchronization processing. Since the sampling frequencies of the sensors are different, such as AIS is updated every 10 seconds, radar is updated every 5 seconds, and optical is updated every 2 seconds, it is necessary to align them to a unified time axis. The Kalman filter smooths the data noise through prediction and update to generate a time-aligned data set. For example, unifiedly aligned to each second, the output position of the cargo ship at t = 10 seconds is 120.501°E, 30.201°N.

[0063] It should be noted that the Kalman filter can effectively reduce sensor noise and improve data consistency.

[0064] Furthermore, if there are missing values in the time-aligned dataset, such as no data from the radar at t = 12 seconds, linear interpolation can be used to fill in the blanks. Suppose the distances at t = 11 seconds and t = 13 seconds are 5.1 km and 5.3 km respectively, then the interpolated value at t = 12 seconds is 5.2 km. This method is simple and efficient, ensuring the continuity of the complete dataset and is suitable for real-time applications.

[0065] In a possible implementation, the weighted average method is used to fuse data, and the weights are based on sensor accuracy: the AIS position has high accuracy with a weight of 0.5; the radar distance is reliable with a weight of 0.3; the optical image is used for auxiliary verification with a weight of 0.2. After fusion, the longitude and latitude of the cargo ship at t = 10 seconds are 120.502°E, 30.202°N, the speed is 10.2 knots, and the course is 350°. The fused dataset improves the positioning accuracy and reduces the error of a single sensor.

[0066] In one embodiment, cluster analysis is used to extract the ship motion state. The K-means algorithm is used to classify ships into states such as stationary, sailing, and turning based on longitude, latitude, speed, and course. For example, a cargo ship with a speed of 10 knots and a stable course is classified as the sailing state; another ship with a speed of 0 knots is classified as the stationary state. The clustering results form a multi-ship motion state set, intuitively reflecting the dynamics of ships in the port. Then, unified formatting processing is performed to convert the motion state into a standard description in the format of "ship ID - time - state - longitude and latitude - speed - course". For example, "Cargo1 - 10:00 - sailing - 120.502°E, 30.202°N - 10.2 knots - 350°". This format is convenient for system analysis and sharing.

[0067] It can be understood that anomaly screening is achieved through preset thresholds, such as speeds exceeding 30 knots or sudden changes in course being anomalies. Suppose a ship's speed suddenly increases to 35 knots, exceeding the threshold, it is marked as an anomaly and excluded. The final description only retains reliable data, such as the normal sailing state of the cargo ship. This process ensures data quality and supports port scheduling and safety monitoring.

[0068] In summary, this embodiment uses time synchronization to improve data consistency, interpolation to ensure continuity, fusion to improve positioning accuracy, clustering and anomaly screening to enhance the reliability of state analysis, and the final description to support efficient ship traffic management, reduce the collision risk, and improve port operation efficiency.

[0069] S102. According to the multi-ship motion state dataset, calculate the relative distance and relative azimuth angle of each pair of fishing boats, and use the time series modeling method to extract the distance change rate and azimuth angle change rate to obtain the dynamic relative motion characteristics of each pair of ships.

[0070] Obtain the longitude, latitude, speed, and heading of each pair of fishing boats from the multi-vessel motion state dataset, and calculate the relative distance D and relative azimuth angle A of each pair of fishing boats. Among them, D is calculated through the Euclidean distance formula, and A is determined through the arctangent function based on the longitude and latitude differences. Use time series analysis methods to process the relative distance D and relative azimuth angle A, and calculate the distance change rate Rd and azimuth change rate Ra through a sliding window. Among them, Rd is the differential mean of D within the time window, and Ra is the differential mean of A within the time window, obtaining time series features. If there are missing values in the time series features, complete the missing Rd and Ra values through linear interpolation to generate a complete time series feature dataset. According to the complete time series feature dataset, use the K-means clustering algorithm to classify Rd and Ra, extract the dynamic relative motion patterns of each pair of boats, and obtain a set of classified motion features. Extract the dynamic features of each pair of fishing boats from the set of classified motion features, calculate the statistical indicators of Rd and Ra, including the mean and variance, to generate a dynamic feature description. If the statistical indicators of Rd or Ra in the dynamic feature description exceed the preset threshold, it is judged as an abnormal motion pattern, and the abnormal data is removed to obtain the final dynamic relative motion features. Through unified formatting processing, store the final dynamic relative motion features as a structured dataset, including boat pair combinations, relative distances, relative azimuth angles, distance change rates, and azimuth change rates, to generate a multi-vessel dynamic relative motion description.

[0071] Implementable, when calculating the relative distance D and relative azimuth angle A of each pair of fishing boats based on the multi-vessel motion state dataset, it can be processed through longitude and latitude data. The relative distance D uses the Euclidean distance formula to calculate the spatial distance based on the longitude and latitude differences between two boats. The relative azimuth angle A is determined by the arctangent function in combination with the longitude and latitude differences to determine the relative direction of the two boats. For example, fishing boat A is located at 120.3°E, 30.1°N, and fishing boat B is located at 120.4°E, 30.2°N. After calculation, D is approximately 15 kilometers and A is approximately 45°. This method intuitively reflects the spatial relationship between the two boats and is convenient for subsequent analysis.

[0072] In a possible implementation, time series analysis processes the relative distance D and relative azimuth angle A through a sliding window, and calculates the distance change rate Rd and azimuth change rate Ra. The sliding window can be set to 10 seconds to statistically calculate the differential means of D and A within the window. For example, for fishing boats A and B, D is 15 kilometers at t = 10 seconds and 14.5 kilometers at t = 20 seconds, and Rd is -0.05 km / s, indicating that the two boats are getting closer; A changes from 45° to 43°, and Ra is -0.2° / s, indicating a slight change in the azimuth angle. This analysis captures the dynamic trend and helps to judge the motion intention of the boats.

[0073] It should be noted that the missing values in the time - series features are filled in by linear interpolation. For example, if Rd is missing at t = 30 seconds, Rd is - 0.05 km / s at t = 20 seconds, and is - 0.03 km / s at t = 40 seconds, then the interpolation value for t = 30 seconds is - 0.04 km / s. Interpolation ensures data continuity, supports subsequent clustering analysis, and avoids pattern recognition biases caused by data missing.

[0074] Specifically, the K - means clustering algorithm classifies dynamic relative motion patterns based on Rd and Ra. Suppose the clustering is divided into three categories: approaching, moving away, and parallel. For example, a pair of ships with a negative Rd and Ra close to 0° is classified as the approaching pattern; a positive Rd indicates moving away. For fishing boats A and B with Rd = - 0.05 km / s and Ra = - 0.2° / s, they are classified as the approaching pattern after clustering. This classification intuitively reflects the relative motion law of the pair of ships and is convenient for dynamic monitoring.

[0075] In one embodiment, the dynamic feature description is generated by calculating the mean and variance of Rd and Ra. For example, the mean of Rd for fishing boats A and B within 60 seconds is - 0.04 km / s, the variance is 0.01, the mean of Ra is - 0.15° / s, and the variance is 0.05. These statistical metrics quantify the motion stability. The mean reflects the trend, and the variance indicates the degree of fluctuation, which helps to evaluate the motion consistency.

[0076] Among them, the abnormal motion pattern is judged by comparing the statistical metrics of Rd or Ra with a preset threshold. For example, the threshold is set such that when the absolute value of Rd is greater than 0.2 km / s, it is abnormal. If the Rd of a pair of ships is 0.3 km / s, exceeding the threshold, it is marked as abnormal and excluded. After excluding the abnormal data, reliable features are retained to ensure the accuracy of the analysis results.

[0077] The unified formatting process stores the dynamic relative motion features as a structured data set in the format of "ship pair ID - time - D - A - Rd - Ra". For example, "fishing boat A - B - 10:00 - 15 km - 45° - - 0.05 km / s - - 0.2° / s". This format is convenient for system storage and sharing and supports dynamic monitoring and early warning in fishery management.

[0078] It can be understood that the above - mentioned method forms a complete description of dynamic relative motion through multi - level processing, from distance and azimuth angle calculation to clustering and abnormal screening. This description provides reliable data support for fishing boat motion analysis and helps with maritime traffic management and safety assurance.

[0079] S103. If the relative distance continues to decrease and is lower than the preset threshold D (D represents the safety distance threshold, unit: meter), then combined with the azimuth angle change rate, a machine - learning model is used to predict the collision possibility, and the collision risk probability for each pair of fishing boats is obtained.

[0080] If the relative distance continues to decrease and is lower than the preset safety distance threshold D (in meters, representing the minimum safety interval between two ships), then obtain the relative distance and the rate of change of azimuth angle for each pair of fishing boats from the multi-ship motion state dataset to generate a dynamic feature dataset. Process the dynamic feature dataset through a sliding window method, calculate the continuous decrease trend of the relative distance and the temporal features of the rate of change of azimuth angle to obtain a set of temporal features. Use the random forest algorithm to classify the set of temporal features, determine whether each pair of fishing boats has a high collision risk, and generate a collision risk classification result. If the collision risk classification result is high risk, then combine the relative distance and the rate of change of azimuth angle, and predict the collision risk probability through a pre-trained logistic regression model to obtain the collision risk probability value for each pair of fishing boats.

[0081] Implementable, based on the multi-ship motion state dataset, the continuous decrease of the relative distance is a key indicator for judging collision risk. The relative distance is calculated from the longitude and latitude data of two ships and reflects the spatial proximity between the ships. If the relative distance is lower than the preset safety distance threshold, for example, 500 meters, it indicates that the two ships may enter a high-risk area.

[0082] In a possible implementation, fishing boat A is located at 120.5°E, 30.2°N, and fishing boat B is located at 120.6°E, 30.3°N. The calculated relative distance is 400 meters, which is lower than the safety threshold, triggering subsequent dynamic feature extraction. This method intuitively judges potential risks through spatial positions, facilitating rapid response.

[0083] It should be noted that the generation of the dynamic feature dataset depends on the temporal analysis of the relative distance and the rate of change of azimuth angle. The rate of change of azimuth angle is calculated by the difference in azimuth angles at adjacent time points and reflects the dynamic adjustment of the relative direction of the ships. For example, the azimuth angles of fishing boats A and B are 50° at t = 10 seconds and become 48° at t = 20 seconds, and the rate of change of azimuth angle is -0.2° / second, indicating a slight deflection in direction.

[0084] In one embodiment, the dynamic feature dataset includes the relative distance, the rate of change of distance, and the rate of change of azimuth angle, in the format of "ship pair ID - time - distance - rate of change of azimuth angle", such as "fishing boat A - B - 10:00 - 400 meters - -0.2° / second". This structured data facilitates system processing.

[0085] Specifically, the sliding window method processes the dynamic feature dataset to extract time-series features. The sliding window can be set to 20 seconds, and the mean and trend of the relative distance within the window are calculated. For example, the relative distance between fishing vessels A and B decreases from 400 meters to 350 meters from t = 10 seconds to t = 30 seconds, with a continuous decreasing trend and a distance change rate of -2.5 m / s. The time-series features of the azimuth change rate are calculated through the mean and variance within the window, such as a mean of -0.15° / s and a variance of 0.02. This analysis captures the dynamic patterns of distance and direction, supporting risk assessment.

[0086] In one embodiment, the random forest algorithm classifies the time-series feature set to determine the collision risk level. The random forest is an ensemble of multiple decision trees. Based on features such as the distance change rate and azimuth change rate, the pairs of vessels are classified into high, medium, and low risks. For example, the distance change rate between fishing vessels A and B is -2.5 m / s, and the variance of the azimuth change rate is 0.02, and the classification result is high risk. This method uses feature combinations to improve the classification accuracy.

[0087] In one embodiment, the high-risk pairs of vessels are further used to predict the collision risk probability through a logistic regression model. The logistic regression model takes the relative distance and azimuth change rate as inputs and outputs a probability value. For example, the distance between fishing vessels A and B is 350 meters, and the distance change rate is -2.5 m / s, and the model predicts a collision probability of 85%. This quantified result provides a basis for early warning.

[0088] It can be understood that the above method forms a complete risk assessment process through multi-level analysis, from distance monitoring to probability prediction. The implementation of each technical topic supports each other, ensuring the rigor and practicality of the analysis, and providing reliable support for fishery safety management.

[0089] S104: Calculate the potential impact force F (unit: Newton) using a dynamic model based on the collision risk probability and relative motion characteristics. Based on the hull mass m (unit: kilogram) and relative velocity v (unit: m / s), obtain the quantified impact force value.

[0090] Obtain the hull mass and relative velocity of each pair of fishing vessels from the fishing vessel motion state dataset. Combine the pre-established collision risk probability to generate a feature dataset containing mass parameters, velocity parameters, and risk probability, obtaining the feature dataset. Process the feature dataset through the sliding window method, calculate the time-series change trend of the relative velocity and the distribution characteristics of the hull mass, generate a time-series feature set, obtaining the time-series feature set. Use a dynamic model to process the time-series feature set, calculate the potential impact force of each pair of fishing vessels, and obtain the quantified impact force value.

[0091] Feasible. When obtaining the hull mass and relative speed of each pair of fishing vessels from the fishing vessel motion state dataset and generating the feature dataset in combination with the collision risk probability, it is necessary to clarify the data source and processing logic. The hull mass is usually extracted from the fishing vessel registration information, reflecting the physical attributes of the vessel. The relative speed is calculated through the speed and course difference between two vessels, reflecting the dynamic approaching characteristics.

[0092] In a possible implementation, the mass of fishing vessel C is 5000 kg, the mass of fishing vessel D is 7000 kg, the relative speed is 3 m / s, and the collision risk probability is 80%. These data are integrated into a feature dataset in the format of "vessel pair ID - time - mass - relative speed - risk probability", such as "Fishing vessel C - D - 10:00 - 5000 kg - 7000 kg - 3 m / s - 80%". This structured data facilitates subsequent analysis and clearly reflects the dynamic and risk states of the vessel pair.

[0093] It should be noted that when using the sliding window method to process the feature dataset, it aims to capture the temporal variation of the relative speed and the mass distribution characteristics. The sliding window can be set to 30 seconds to analyze the mean value and change trend of the relative speed within the analysis window.

[0094] For example, the relative speed of fishing vessels C and D increases from 3 m / s to 4 m / s from t = 10 s to t = 40 s, showing a trend of accelerating approach, and the speed change rate is 0.033 m / s². The mass distribution characteristics are analyzed through the mean value and difference of the hull mass within the window. For example, the mean mass is 6000 kg and the difference is 2000 kg. This set of temporal characteristics provides a dynamic basis for kinetic analysis and highlights the synergistic effect of speed and mass.

[0095] Specifically, when using a kinetic model to process the set of temporal characteristics, a quantization value is calculated based on the potential impact force formula. The potential impact force reflects the energy intensity of the collision and is closely related to mass and speed.

[0096] In an embodiment, the relative speed of fishing vessels C and D is 4 m / s, and the smaller mass value of 5000 kg is taken, resulting in a higher impact force value, indicating a high-energy risk. In another example, the relative speed of fishing vessels E and F is 2 m / s, and the mass is 4000 kg, with a lower impact force and less risk. This quantitative analysis intuitively reflects the potential threats of different vessel pairs through the combination of mass and speed, facilitating priority ranking.

[0097] In an embodiment, the implementation of the kinetic model can adjust the analysis weight in combination with the risk probability. For example, for vessel pairs with a risk probability higher than 85%, their impact force values will be given priority, triggering a more stringent early warning mechanism.

[0098] In one embodiment, the characteristic datasets of fishing vessels G and H show a mass of 6,000 kilograms, a relative speed of 3.5 meters per second, and a risk probability of 90%. The calculated impact force is relatively high, and the system marks it as high-priority. This method improves the pertinence of risk assessment through multi-parameter fusion.

[0099] It can be understood that the above method forms a complete process from data to quantified risk through characteristic dataset generation, time-series feature extraction, and kinetic analysis. The implementation of each topic supports each other to ensure the comprehensiveness and practicality of the analysis.

[0100] For example, the structured design of the characteristic dataset facilitates sliding window processing, while the time-series features provide dynamic inputs for the kinetic model. The final quantification of the impact force provides an intuitive basis for risk management. This multi-level analysis is logically clear and adapts to the complex dynamic requirements of the fishing scenario.

[0101] S105. Generate the collision risk level of each ship according to the quantified impact force value and the collision risk probability, and use the clustering algorithm to dynamically group the fishing vessels to obtain an optimized formation structure.

[0102] Obtain the relative speed characteristics and hull mass distribution of each pair of fishing vessels from the fishing vessel motion state dataset, and use a preprocessing method to generate a characteristic dataset containing the time-series change trend to obtain the characteristic dataset. Process the characteristic dataset through a kinetic model, and calculate the quantified impact force of each pair of fishing vessels according to the formula F = mv² / 2, where F is the quantified impact force (unit: Newton), m is the hull mass (unit: kilogram), and v is the relative speed (unit: meter per second), to obtain a set of quantified impact forces. Use a statistical method to jointly analyze the set of quantified impact forces and the collision risk probability. If the quantified impact force exceeds the preset threshold and the risk probability is higher than the set value, it is determined as a high-risk collision scenario to obtain a set of high-risk scenarios. Dynamically group the fishing vessels in the set of high-risk scenarios through the K-means clustering algorithm, generate the collision risk level of the fishing vessels according to the collision risk probability and the quantified impact force to obtain a set of collision risk levels. According to the set of collision risk levels, use a sorting method to prioritize the fishing vessels. If the collision risk level of a fishing vessel is high, its motion trajectory is preferentially adjusted to obtain an adjusted trajectory set. Process the adjusted trajectory set through an optimization algorithm to generate a fishing vessel formation structure that meets the collision risk level constraints to obtain an optimized formation structure. Use a verification method to evaluate the optimized formation structure. If the distance between the fishing vessels in the optimized formation structure meets the preset safety threshold, it is determined as the final formation plan to obtain the final formation plan.

[0103] Implementable. When extracting relative speed features and hull mass distribution from the fishing vessel motion state dataset, a feature dataset containing time series change trends can be generated through preprocessing methods. The relative speed feature is calculated based on the fishing vessel's speed and course difference, and the mass distribution is obtained from the registration information. In one possible implementation, the speeds of fishing vessels A and B within 10 minutes are 5 m / s and 6 m / s respectively, and the course difference is 30 degrees. The calculated relative speed is 2.5 m / s. The mass distribution shows that A is 4000 kg and B is 6000 kg. The preprocessing uses a sliding window with a window size of 60 seconds to capture the trend of the relative speed rising from 2.5 m / s to 3 m / s, generating a feature dataset in the format of "vessel pair ID - time - relative speed - mass mean - trend", such as "A - B - 10:00 - 2.5 m / s - 5000 kg - rising".

[0104] Specifically, when the dynamic model processes the feature dataset, the quantized impact force is calculated based on the formula.

[0105] It should be noted that the model takes a smaller mass and relative speed as inputs to ensure a conservative estimate. For example, the relative speed of fishing vessels C and D is 3 m / s, and the mass is taken as 4000 kg to generate the impact force value, which is incorporated into the set of quantized impact forces. The impact force values of different vessel pairs reflect the potential collision intensity, facilitating subsequent analysis.

[0106] In one embodiment, when jointly analyzing the set of quantized impact forces and the collision risk probability, the impact force threshold is set to 10000 N and the risk probability threshold is set to 75%. The impact force of fishing vessels E and F is 12000 N, and the risk probability is 80%, which is determined as a high-risk scenario. In another example, the impact force of fishing vessels G and H is 8000 N, and the risk probability is 70%, which does not reach the threshold and is excluded from high risk. This screening logic is clear and highlights high-risk vessel pairs.

[0107] In one embodiment, the K-means clustering algorithm groups the set of high-risk scenarios and generates risk levels based on the impact force and risk probability. For example, the impact force of fishing vessels I and J is 15000 N, and the risk probability is 85%. After clustering, they are classified into the high-risk level; the impact force of fishing vessels K and L is 11000 N, and the risk probability is 78%, which is classified into the medium-risk level. The clustering results form a set of collision risk levels, intuitively reflecting the threat degree of vessel pairs.

[0108] It can be understood that the priority division is based on the set of risk levels, and the trajectory is adjusted using a sorting method. High-risk fishing vessels such as I and J adjust their courses first, expanding the spacing to 100 m to generate an adjusted set of trajectories. Low-risk vessel pairs maintain their original trajectories. This method ensures that high-risk vessel pairs are processed in a timely manner.

[0109] For example, when the optimization algorithm processes the trajectory set, it generates a formation structure based on the risk level constraint. After adjustment, the distance between fishing vessels M and N is 120 meters, meeting the safety requirements and being incorporated into the optimized formation structure. In another example, the distance between fishing vessels O and P is 80 meters, which does not meet the standard and needs further optimization. This iterative optimization ensures the rationality of the formation.

[0110] In a possible implementation, the verification method evaluates the optimized formation structure and checks whether the distance between fishing vessels meets the safety threshold of 100 meters. For example, the distance between fishing vessels Q and R is 130 meters, meeting the requirements and being determined as the final formation plan; the distance between fishing vessels S and T is 90 meters, and it needs to be readjusted. This verification ensures the feasibility of the plan and adapts to the dynamic requirements of the fishing scenario.

[0111] S106. If the collision risk level is higher than the preset threshold R (R represents the risk level threshold, without unit), then according to the optimized formation structure, calculate the avoidance course adjustment angle and speed change amount of each ship to obtain an emergency avoidance instruction.

[0112] If the collision risk level is higher than the preset threshold R (R represents the risk level threshold, without unit), then according to the optimized formation structure, use the path planning algorithm to calculate the avoidance course adjustment angle of each fishing vessel to obtain a set of course adjustment angles. If the adjustment angles in the set of course adjustment angles exceed the preset range, then calculate the speed change amount of each fishing vessel through the dynamic model to obtain a set of speed change amounts. Use a fusion method to jointly process the set of course adjustment angles and the set of speed change amounts to generate a set of emergency avoidance instructions that meet the safety distance constraint, obtaining a set of emergency avoidance instructions. Optimize the set of emergency avoidance instructions through the dynamic trajectory planning method to generate the adjusted motion trajectories of each fishing vessel, obtaining a set of adjusted trajectories. According to the real-time data update method, verify the set of adjusted trajectories. If the relative position distribution between fishing vessels meets the safety distance constraint, then determine it as the final avoidance plan, obtaining the final avoidance plan.

[0113] Implementably, use the path planning algorithm to calculate the avoidance course adjustment angle: use the A* algorithm, considering the turning radius of the fishing vessel and the sea area obstacles, to generate the adjustment angle. For example, fishing vessel E needs to be adjusted 15 degrees to the right, and fishing vessel F needs to be adjusted 10 degrees to the left to form a set of course adjustment angles. If the adjustment angle exceeds the preset range, such as 20 degrees, then calculate the speed change amount through the dynamic model. The dynamic model takes the current speed and the ship mass as inputs to generate a speed adjustment plan. For example, fishing vessel G decelerates by 0.5 m / s to generate a set of speed change amounts.

[0114] It should be noted that the fusion method jointly processes the heading adjustment angle set and the speed change amount set to generate an emergency avoidance instruction set. The fusion method can adopt weighted decision-making, giving priority to ensuring heading adjustment and supplemented by speed changes. For example, fishing vessel H adjusts its heading by 12 degrees and decelerates by 0.3 m / s to form an instruction "Vessel H - 10:05 - Heading 12 degrees - Deceleration 0.3 m / s", which is incorporated into the emergency avoidance instruction set.

[0115] In one embodiment, the dynamic trajectory planning method optimizes the emergency avoidance instruction set to generate an adjusted trajectory set.

[0116] Implementable, spline curve fitting is used to smooth the track changes. For example, the adjusted trajectory of fishing vessel I shows that its distance from fishing vessel J has increased to 120 m, meeting the safety requirements. The real-time data update method verifies the adjusted trajectory set to check whether the distance between fishing vessels meets the standard. For example, the distance between fishing vessels K and L is 110 m, meeting the 100 m threshold, and it is determined as the final avoidance plan; the distance between fishing vessels M and N is 95 m, and re-planning is required.

[0117] It can be understood that the above method ensures the safe navigation of fishing vessels in complex dynamic scenarios through multi-level analysis and optimization. The implementation methods of each technical theme are closely connected, forming a complete logical chain from data extraction to final plan verification to meet the real-time needs of the fishing scenario.

[0118] S107. Update the navigation parameters of each ship through the emergency avoidance instruction, and use the real-time communication protocol to send the instruction to the navigation system of each fishing vessel to obtain the multi-ship cooperative motion state after execution. According to the multi-ship cooperative motion state after execution, recalculate the relative distance and azimuth angle change rate, verify the effect of the avoidance instruction, and obtain the updated collision risk probability. If the updated collision risk probability is lower than the preset threshold P (P represents the safety probability threshold, dimensionless), then maintain the current formation and navigation parameters; otherwise, return to the dynamic grouping step, regenerate the optimized formation structure, and obtain a new avoidance instruction.

[0119] Update the navigation parameters of each fishing vessel through the emergency avoidance instruction, and send the instruction to the navigation system of each fishing vessel using a real-time communication protocol to obtain the cooperative motion state data. According to the cooperative motion state data, calculate the relative distance and the azimuth change rate between each fishing vessel to obtain the relative motion feature set. Use a preprocessing method to filter the relative motion feature set to generate a smooth motion feature data set, and obtain the smooth feature data set. Through the collision risk assessment model, based on the smooth feature data set, calculate the collision risk probability of each fishing vessel to obtain the collision risk probability set. If any probability in the collision risk probability set is higher than the preset threshold P (P represents the safety probability threshold, dimensionless), then trigger the dynamic grouping algorithm to generate an optimized formation structure and obtain the optimized formation structure data. According to the optimized formation structure data, calculate the avoidance course adjustment angle and the speed change amount of each fishing vessel to generate a new avoidance instruction set and obtain the avoidance instruction set. Send the avoidance instruction set to the navigation system of each fishing vessel through the real-time communication protocol to update the cooperative motion state data and obtain the updated cooperative motion state data.

[0120] Implementable, the update of the emergency avoidance instruction involves applying the calculated course and speed adjustment parameters to the fishing vessel navigation system. The instruction contains specific course angles and speed change amounts. For example, fishing vessel A needs to adjust its course to 270 degrees and reduce its speed to 3 m / s. The real-time communication protocol uses a lightweight protocol based on MQTT to ensure low-latency instruction transmission. After receiving the instruction, the fishing vessel navigation system automatically adjusts the rudder angle and engine power to update the navigation parameters. For example, fishing vessel B receives the instruction "Adjust the course to 265 degrees and decelerate by 0.4 m / s", and the navigation system completes the parameter adjustment within 2 seconds, generates the cooperative motion state data, and records the real-time position, speed, and course of each vessel.

[0121] In a possible implementation, based on the cooperative motion state data, calculate the relative distance and the azimuth change rate. The relative distance is calculated through the longitude and latitude of each fishing vessel. For example, the distance between fishing vessels C and D is 120 meters. The azimuth change rate reflects the relative motion trend between fishing vessels. For example, the azimuth of fishing vessel C relative to D changes by 1.5 degrees per second. The relative motion feature set contains "vessel pair ID - time - distance - azimuth change rate", such as "C - D - 10:10 - 120 meters - 1.5 degrees / s". These data provide a basis for subsequent risk assessment.

[0122] It should be noted that the preprocessing method filters the relative motion feature set to eliminate sensor noise.

[0123] In this embodiment, a Kalman filter is used to generate the smooth feature data set. For example, the distance data between fishing vessels E and F is smoothed from the original fluctuating value to a stable 110 meters, and the azimuth change rate is smoothed to 1.2 degrees / s. The smooth feature data set ensures the accuracy of the collision risk assessment.

[0124] Specifically, the collision risk assessment model calculates the collision risk probability based on the smooth feature dataset. The model combines the distance, speed difference, and azimuth change rate to generate a probability value. For example, the distance between fishing vessels G and H is 90 meters, and the speed difference is 1.5 m / s. The model outputs a collision probability of 0.75, which is higher than the preset threshold of 0.6. The collision risk probability set records the probability values of all pairs of vessels, providing a basis for dynamic grouping.

[0125] In one embodiment, the dynamic grouping algorithm optimizes the formation structure according to the collision risk probability set. The algorithm considers the motion state of the fishing vessels and the sea area environment to generate a grouping scheme.

[0126] For example, fishing vessels I, J, and K are grouped together, maintaining an interval distance of 150 meters. The optimized formation structure data includes the vessel IDs and recommended spacing of each group to ensure the overall navigation safety.

[0127] For example, based on the optimized formation structure data, the avoidance course adjustment angle and speed change amount are calculated. Fishing vessel L needs to adjust 10 degrees to the left and decelerate by 0.3 m / s; fishing vessel M adjusts 8 degrees to the right and keeps the speed unchanged. The avoidance instruction set is stored in the format of "vessel ID - time - course adjustment - speed change", such as "L - 10:15 - 10 degrees to the left - decelerate 0.3 m / s".

[0128] In one embodiment, the real - time communication protocol sends the avoidance instruction set to the navigation system to update the cooperative motion state data. After receiving the instruction, fishing vessel N adjusts its course to 280 degrees and reduces its speed to 4 m / s. The updated cooperative motion state data shows that the distance between fishing vessels N and O increases to 130 meters, meeting the safety requirements. This multi - level instruction generation and sending mechanism ensures the cooperative and safe navigation of fishing vessels in a dynamic scenario.

[0129] Embodiment Two

[0130] This embodiment also provides a computer device, including a memory, a processor, and a computer program stored on the memory. The processor executes the computer program to implement the steps of the method.

[0131] Embodiment Three

[0132] This embodiment also provides a computer - readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the method.

[0133] The above is only the preferred specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An AI-based method for predicting the impact force of fishing boats, characterized in that, The method includes the following steps: Obtain AIS, radar, and optical sensor data through multi-source sensors, and fuse them to generate a real-time multi-vessel motion state dataset; According to the multi-vessel motion state dataset, calculate the relative distance and relative azimuth angle of each pair of fishing vessels, and use time series modeling methods to extract the distance change rate and azimuth change rate; If the relative distance of each pair of fishing vessels is lower than the preset safety distance threshold, then combine the azimuth change rate and use a machine learning model to obtain the collision risk probability of each pair of fishing vessels; Based on the collision risk probability, use a dynamic model to predict the impact force and quantify the impact force value; According to the impact force value and collision risk probability, generate the collision risk level of each vessel, and then use a clustering algorithm to dynamically group the fishing vessels to obtain an optimized formation structure; If the collision risk level is higher than the preset risk threshold, then according to the optimized formation structure, calculate the avoidance course adjustment angle and speed change amount of each vessel, and generate an emergency avoidance instruction; Use a real-time communication protocol to send the emergency avoidance instruction to each fishing vessel navigation system, update the navigation parameters of each vessel, and obtain the multi-vessel cooperative motion state after execution.

2. The method according to claim 1, characterized in that, The step of obtaining AIS, radar, and optical sensor data through multi-source sensors and fusing them to generate a real-time multi-vessel motion state dataset includes: Collect AIS data, radar data, and optical data through multi-source sensors and store them as the original dataset; Use the Kalman filter algorithm to perform time synchronization processing on the original dataset to obtain a time-aligned dataset; Complete the missing values in the time-aligned dataset by linear interpolation method to obtain a complete dataset; According to the complete dataset, use the weighted average method to fuse AIS, radar, and optical data to generate a fused real-time multi-vessel motion state dataset, and the multi-vessel motion state dataset includes vessel longitude and latitude, vessel speed, and vessel course.

3. The method according to claim 1, characterized in that, The step of calculating the relative distance and relative azimuth angle of each pair of fishing vessels according to the multi-vessel motion state dataset and using time series modeling methods to extract the distance change rate and azimuth change rate includes: Obtain the longitude and latitude, speed, and course of each pair of fishing vessels from the multi-vessel motion state dataset, calculate the relative distance of each pair of fishing vessels based on the Euclidean distance formula, and calculate the relative azimuth angle of each pair of fishing vessels based on the arctangent function; Use time series analysis methods to process the relative distance and relative azimuth angle, and calculate the distance change rate and azimuth change rate through a sliding window.

4. The method according to claim 1, characterized in that, The step of if the relative distance of each pair of fishing vessels is lower than the preset safety distance threshold, then combine the azimuth change rate and use a machine learning model to obtain the collision risk probability of each pair of fishing vessels includes: If the relative distance continues to decrease and is lower than the preset safety distance threshold, then generate a dynamic feature dataset based on the relative distance and azimuth change rate of each pair of fishing vessels; Process the dynamic feature dataset by the sliding window method, calculate the continuous decrease trend of the relative distance and the time series features of the azimuth change rate, and obtain a time series feature set; Use the random forest algorithm to classify the time series feature set, judge the collision risk of each pair of fishing vessels, and generate a collision risk classification result; Based on the collision risk classification results, combined with the relative distance and the rate of change of azimuth angle, predict the collision risk probability through a pre-trained logistic regression model to obtain the collision risk probability values for each pair of fishing vessels.

5. The method according to claim 1, characterized in that, Using the collision risk probability, predict the impact force by means of a dynamic model and quantify the impact force value, including: Obtain the hull mass and relative velocity of each pair of fishing vessels from the multi-vessel motion state dataset, and combine with the collision risk probability to generate a feature dataset containing mass parameters, velocity parameters, and risk probability; Process the feature dataset by means of a sliding window method, calculate the temporal change trend of the relative velocity and the distribution characteristics of the hull mass, and generate a temporal feature set; Use a dynamic model to process the temporal feature set, calculate the potential impact force of each pair of fishing vessels, and quantify the impact force value.

6. The method according to claim 1, characterized in that, Generating the collision risk level of each vessel according to the impact force value and the collision risk probability, and using a clustering algorithm to dynamically group the fishing vessels to obtain an optimized formation structure, including: Conduct a joint analysis of the impact force value and the collision risk probability by means of a statistical method. If the impact force value exceeds the preset impact threshold and the collision risk probability is higher than the preset risk threshold, it is determined as a high-risk collision scenario; Dynamically group the fishing vessels in the high-risk scenario set through the K-means clustering algorithm, generate the collision risk level of the fishing vessels based on the collision risk probability and the impact force, and obtain a collision risk level set; According to the collision risk level set, use a sorting method to divide the fishing vessels into priorities to obtain an optimized formation structure.

7. The method according to claim 1, characterized in that, If the collision risk level is higher than the preset risk threshold, then according to the optimized formation structure, calculate the avoidance course adjustment angle and speed change amount of each vessel, and generate an emergency avoidance instruction, including: According to the optimized formation structure, use a path planning algorithm to calculate the avoidance course adjustment angle of each fishing vessel to obtain a course adjustment angle set; If the adjustment angles in the course adjustment angle set exceed the preset range, then calculate the speed change amount of each fishing vessel through a dynamic model to obtain a speed change amount set; Use a fusion method to jointly process the course adjustment angle set and the speed change amount set to generate an emergency avoidance instruction that meets the safety distance constraint.

8. The method according to claim 1, wherein After using the real-time communication protocol to send the emergency avoidance instruction to the navigation system of each fishing vessel and update the navigation parameters of each vessel to obtain the multi-vessel cooperative motion state after execution, it further includes: According to the multi-vessel cooperative motion state after execution, repeatedly calculate the relative distance and the rate of change of azimuth angle of each pair of vessels to obtain the updated collision risk probability; if the updated collision risk probability is lower than the preset risk threshold, maintain the current formation and navigation parameters, otherwise regenerate the optimized formation structure to obtain a new avoidance instruction.

9. A computer device, comprising a memory, a processor, and a computer program stored on the memory, wherein The processor executes the computer program to implement the steps of the method according to any one of claims 1-8.

10. A computer-readable storage medium, on which a computer program is stored, wherein When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1-8.

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