智能船舶航线优化方法综述

为提升船舶轨迹异常检测的精度和效率，解决传统异常检测方法存在的特征表征能力有限、补偿精度不足、容易出现梯度消失、过拟合等问题，提出一种基于Transformer_LSTM编解码器模型的无监督船舶轨迹异常检测方法。

该方法基于编码器解码器架构，由Transformer_LSTM模块替代传统神经网络实现轨迹特征提取和轨迹重构；将Transformer嵌入LSTM的递归机制，结合循环单元和注意力机制，利用自注意力和交叉注意力实现对循环单元状态向量的计算，实现对长序列模型的有效构建；通过最小化重构输出和原始输入之间的差异，使模型学习一般轨迹的特征和运动模式，将重构误差大于异常阈值的轨迹判定为异常轨迹。

采用2021年1月的船舶AIS数据进行实验，结果表明，模型在准确率、精确率以及召回率上相较于LOF，DBSCAN，VAE，LSTM等经典模型有着明显提升；F₁分数相较于VAE_LSTM模型提升约8.11%。

该方法的异常检测性能在各项指标上显著优于传统算法，可有效、可靠地运用于海上船舶轨迹异常检测。

Overview of route optimi-zation methods data for intelligent ships

In order to improve the accuracy and efficiency of ship trajectory anomaly detection, and solve the problems of traditional anomaly detection methods such as limited feature characterization ability, insufficient compensation accuracy, gradient disappearance and overfitting, an unsupervised ship trajectory anomaly detection method based on the Transformer_LSTM codec module is proposed.Based on the encoder decoder architecture, the Transformer_LSTM module replaces the traditional neural network to achieve track feature extraction and track reconstruction. By embedding the transformer into the recursive mechanism of LSTM, combined with the cyclic unit and attention mechanism, self-attention and cross-attention can be used to calculate the state vector of the cyclic unit and effectively construct the long sequence model. By minimizing the difference between the reconstructed output and original input, the model learns the characteristics and motion mode of the general trajectory, and trajectories with a reconstruction error greater than the abnormal threshold are judged as abnormal trajectories. AIS data collected in January 2021 is adopted. The results show that the accuracy, precesion and recall rate of the model are significantly improved compared with those of LOF, DBSCAN, VAE, LSTM, etc. The F₁ score is improved by 8.11% compared with that of the VAE_LSTM model.The anomaly detection performance of the proposed method is significantly superior to the traditional algorithm in various indexes, and the model can be effectively and reliably applied to the trajectory anomaly detection of ships at sea.