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基于前端开发框架的舰船运动姿态数据监测系统

Ship motion attitude data monitoring system based on front-end development framework
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摘要 利用运动姿态监测结果为舰船可靠航行提供决策依据。为此,设计了基于前端开发框架的舰船运动姿态数据监测系统。系统设施层中,利用MEMS陀螺仪芯片与3轴MEMS加速度计采集舰船运动姿态信息,并将所采集信息传送至业务逻辑层;业务逻辑层利用控制子层和管理子层为用户提供姿态数据监测服务;控制子层中的姿态解算模块,依据传感器信息采集结果,利用卡尔曼滤波算法解算舰船运动姿态信息。基于此,利用Bootstrap的前端开发框架,通过viewport元数据标签完成前端UI设计,为用户提供可视化的人机交互界面,将姿态解算结果利用用户界面层为用户展示。测试结果表明,该系统对舰船航行运动姿态的监测结果与实际结果极为接近,适用于实际工作。 Utilizing the results of motion attitude monitoring to provide decision-making basis for reliable navigation of ships.For this purpose,a ship motion attitude data monitoring system based on a front-end development framework was designed.In the system infrastructure layer,MEMS gyroscope chips and 3-axis MEMS accelerometers are used to collect ship motion attitude information,and the collected information is transmitted to the business logic layer;The business logic layer utilizes the control and management sub layers to provide posture data monitoring services for users;The attitude calculation module in the control sub layer uses the Kalman filtering algorithm to calculate the ship's motion attitude information based on the sensor information collection results.Based on this,the front-end development framework of Bootstrap is utilized to complete the front-end UI design through the viewport metadata tag,providing users with a visual human-machine interaction interface.The posture calculation results are presented to users through the user interface layer.The test results show that the monitoring results of the system for the ship's navigation motion attitude are very close to the actual results,and are suitableforpractical work.
作者 吴亚林 吕太之 WU Ya-lin;LV Tai-zhi(School of Information Engineering,Jiangsu Maritime Institute,Nanjing 211170,China)
出处 《舰船科学技术》 北大核心 2024年第4期158-161,共4页 Ship Science and Technology
基金 江苏省高等学校基础科学(自然科学)研究重大项目(23KJA580002) 2022江苏省“青蓝工程”优秀教学团队(苏教师函(2022)29号)。
关键词 前端开发框架 舰船运动姿态 数据监测系统 业务逻辑层 卡尔曼滤波 front-end development framework ship motion attitude data monitoring system business logic layer Kalman filtering
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