| In recent years,with the deterioration of offshore environment and the depletion of fishery resources,the operating area of marine fishing has developed from offshore to ocean.Ocean fishing can make efficient use of marine resources and promote the development of marine economy.However,pelagic fishing operations have also caused a series of problems,for example,because the operating area is far away from the coast,it is not conducive to the sustainable development of marine resources,which is not conducive to the sustainable development of marine resources;in addition,the rapid increase in the number of fishing vessels engaged in offshore fishing has led to frequent collision accidents and an increase in cross-border fishing behavior,which may lead to environmental pollution and overfishing of fish,which is not conducive to the protection of the marine ecological environment.In view of the above problems,through the analysis and judgment of the behavior of ocean-going fishing vessels,we can timely obtain the distribution of fishery resources in the fishing area and understand the law of the development of fishery resources;by predicting the trajectory of fishing vessels,the occurrence of maritime traffic accidents and cross-border fishing can be reduced.In this paper,the algorithms of fishing vessel behavior analysis and trajectory prediction are studied based on deep learning technology.The main contents of this paper are as follows:（1）The original data of fishing boat is preprocessed.The fishing vessel data used in this paper is the AIS data generated by the Automatic Identification System.Because of the large order of magnitude,redundancy and abnormal data of AIS data,the AIS data is preprocessed before entering the training model.Firstly,according to the specification of AIS data,the abnormal data are eliminated,and secondly,the trails of fishing vessels are segmented by using AIS data.Finally,in order to ensure the accuracy and continuity of data,this paper uses the method of adding time interval column to AIS data to replace the general interpolation method,which eliminates the malpractice of using interpolation method to insert false data to some extent,and improves the rigid fixed time interval pattern in interpolation method.（2）Research on behavior classification model of fishing vessels.The research on the behavior of fishing vessels is helpful to help the regulatory authorities to judge the behavior of fishing vessels,to understand the distribution of resources in marine fishing areas and the development law of fishery resources through the behavior analysis of fishing vessels,and to divide the normal berthing range of fishing vessels through the behavior analysis of fishing vessels,so as to judge whether there is abnormal berthing behavior of fishing vessels.This paper studies and proposes a fishing vessel behavior classification model based on Long Short-Term Memory（LSTM）,which is Dilated Convolution-Convolution Neural Networks-Long Short-Term Memory（DC-CNNLSTM）.According to the navigation characteristic information of time,latitude,longitude,speed to the ground,course to the ground and true angle of the fishing vessel in AIS data,the four behaviors of fishing vessels are judged-mobile navigation,wind navigation,berthing and fishing.The performance experiments show that the accuracy of the DC-CNN-LSTM model proposed in this paper is higher than that of CNN-LSTM,DCNN-LSTM,LSTM and BP network experiments,which is 3.5%,4.6%,6.7% and 12%respectively.As the behavior data of fishing vessels is an important related factor for the trajectory prediction of fishing vessels,the behavior judgment of fishing vessels is of great significance to the follow-up trajectory prediction.（3）Research on prediction model of fishing vessel trajectory.The research on the trajectory prediction of fishing vessels can predict the positions of different fishing vessels at the same time in order to determine whether collision accidents will occur;it can also predict whether fishing vessels will cross the border or break into dangerous areas.In this paper,the factors affecting the position of fishing vessels are redistributed and optimized,and the length,width,draught depth and behavior of fishing vessels are added to the influencing factors on the basis of traditional research.at the same time,according to the time series characteristics of AIS data,a neural network model AMLSTM combining attention mechanism and LSTM is proposed.The experimental results show that the prediction accuracy of the model with the addition of fishing vessel length and width data,draft depth and behavior is significantly higher than that of the model without,indicating that the performance of the AM-LSTM proposed in this paper is significantly better than that of the CNN-LSTM,RNN-LSTM,LSTM and BP model. |
