Pre-warning system of maritime traffic safety risk in restricted visibility weather
Article Text (Baidu Translation)
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摘要: 为了提高海上交通安全风险预警的实用性与精度, 建立了能见度不良天气下海上交通风险预警系统, 由风险矩阵知识库、交通流密度预测子系统与能见度预警子系统组成; 通过采集大样本, 运用不完备信息条件下模糊信息分配理论修正了专家调查法, 确定了海上交通风险矩阵; 采用人工神经网络中极限学习机理论的短时船舶交通流密度预测算法计算了交通流密度; 采用区域大气模式系统对气象和海洋预报部门提供的能见度预报数据进行空间和时间精细网格化划分, 计算了能见距离; 采用系统预测了空间网格为2nmile×2nmile和时间步长为10min的关注海域的能见距离和交通流密度, 以验证系统的有效性。仿真结果表明: 2个不同时间段12个时间点的能见距离预测准确率分别达到75%、75%、80%、75%、80%、75%和75%、75%、80%、80%、80%、75%, 相应的交通流密度预测准确率全部达到80%, 预测结果可靠, 并且, 实现了能见度不良天气下海域航行风险的可视化与智能化监控。Abstract: To enhance the pre-warning applicability and accuracy of maritime traffic safety risk, a pre-warning system in restricted visibility weather of the risk was set up, and it was composed of the risk matrix knowledge base, traffic flow density prediction subsystem and visibility warning subsystem.By collecting large samples, the expert survey method was modified by using the fuzzy information distribution theory under the condition of incomplete information, and the maritime traffic risk matrix was determined.The traffic density was calculated by using the short-time prediction algorithm of traffic density based on the limit learning machine theory in the artificial neural network.The regional atmospheric model system was used to divide the visibility forecast data provided by the meteorological and marine forecasting departments into spatialtemporal fine meshes, and the visible distance was calculated.The system was used to predict the visibility distance and traffic flow density of the focused sea area with spatial grids of 2 nmile by2 nmile and time step of 10 min, so as to verify the effectiveness of the system.Simulation resultshows that at 12 time points in two different time periods, the prediction accuracy rates of visible distance are 75%, 75%, 80%, 75%, 80%, 75%, 75%, 75%, 80%, 80%, 80%and 75%.The prediction accuracy rates of corresponding traffic flow densities are up to 80%.Therefore, the forecast result is reliable, and the system can realize the visualization and intelligent monitoring of navigation risk in sea area in restricted visibility weather.
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图 2 成山角分道通航进出口位置
Figure 2. Entrance and exit location of Chengshanjiao traffic separation
图 3 威海海事局VTS管理平台界面
Figure 3. Interface of Weihai Maritime Safety Administration VTS management platform
图 4 自编的船舶信息自动读取程序软件显示面板
Figure 4. Display panel of self-compiled ship information automatic reading program software
图 6 成山角海域内外警戒区网格划分编号
Figure 6. Grid partition numbers of Chengshanjiao internal and external sea alert areas
表 6 调整通过时间后的实例数据和计算结果
Table 6. Example data and calculation results after adjusting passing times
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