SOA构架下的海上船舶状态实时监测算法设计

Design of real time monitoring algorithm of marine ship state under SOA architecture

针对当前海上船舶状态实时监测算法存在的检测准确度低,无法对船舶状态进行准确分析问题,提出一种基于多层神经网络的SOA构架下的海上船舶状态实时监测算法,根据算法的需求建立SOA构架下的海上船舶状态实时监测数学模型,利用中值滤波器通过Haar小波变换通过采样、分解、信号处理以及重构过程对采集的信号进行去噪,保证海上船舶状态监测的准确性。在此基础上,为保证海上船舶状态监控的实时性,利用多层神经网络算法建立权值矩阵,确定监测数据的采样规则,从而完成SOA构架下的海上船舶状态实时监测算法。实验结果表明,所提算法能够及时对海上船舶状态数据进行采集,且检测的准确度较高。

In view of the low detection accuracy and the inability to accurately analyze the status of a ship, a real-time monitoring algorithm based on multi-layer neural network for SOA is proposed. According to the requirements of the algorithm, an SOA Under the framework of the real-time monitoring mathematical model of the state of the ship at sea, the median filter is used to denoise the collected signals through the sampling, decomposition, signal processing and reconstruction processes by the Haar wavelet transform to ensure the accuracy of the marine vessel status monitoring. Based on the above,in order to ensure the real-time status of marine vessel status monitoring, a multi-layer neural network algorithm is used to establish the weight matrix and determine the sampling rules of the monitoring data so as to complete the real-time monitoring of marine vessel status under the SOA framework. The experimental results show that the method proposed in this paper can collect the state data of ships at sea in time and the detection accuracy is high.