基于大数据和智能感知的车辆数据引擎系统设计

Design of vehicle data engine system based on big data platform and intelligent perception

为实现城市轨道车辆的精准化运维指导,需要掌握最新的车辆状态数据并对其发展趋势进行预判,对车辆的实时运行数据和历史运维数据进行同时空的数据挖掘分析研究十分必要。最终,用户无须掌握代码编写技能即可自行创建、修改数据分析模型,模型能自动运行输出结果,为车辆状态的判断提供依据。为此,文章基于大数据平台和智能感知技术设计了一套车辆数据引擎系统,将牵引、制动、车门、网络和LCU等系统的运行数据进行清洗整合,实现数据的时空对齐,利用画布开发组件,采用“拖拉拽”的方式构建数据分析策略模型,可替代重复繁杂的人工作业进行多维数据分析决策、作业流程监控、故障隐患甄别并预警、寿命预测等工作,实现数据分析自定义编辑、自动分析、自动预警,指导故障处理、备件采购等功能,从而达到多专业数据融合、数据综合监视、故障联动分析、数据统计分析、自动化处理分析、结果输出和推演预测、一体化决策及可视化展示,为检修运用模式和策略制定提供数据支撑,进一步保证地铁列车行车安全,提升列车可靠性、降低运营成本。

To guide the operation and maintenance(O&M)of the urban rail transit vehicles precisely,it is necessary to collect the latest vehicle status data and predict the development trend.Therefore,it is essential to conduct data mining and analysis for the real-time operation data and the historical O&M data of the vehicles in the same time and space,and enable the users to create and modify the data analysis model without the ability to write code.The model can run automatically and output the results to provide a basis for the vehicle status judgment.To this end,a set of vehicle data engine system was designed based on the big data platform and intelligent perception to clean and integrate the operation data of the traction,braking,door,network and LCU systems for the spatial and temporal alignment of data.In addition,data analysis strategy model was developed by means of drag and drop based on the canvas components to carry out multidimensional data analysis,make decisions,monitor the working process,identify hidden troubles and give early warning,and make life predictions,replacing the repetitive and complex manual operation.It can also provide functions such as data analysis custom editing,automatic analysis,automatic warning,troubleshooting guidance,and spare parts procurement,thus achieving multi-discipline data fusion,integrated data supervisory control,fault linkage analysis,data statistical analysis,automated processing analysis,result output,deduction and prediction,integrated decision-making and visual display,providing data support for the development of maintenance modes and strategies,further ensuring the safety of metro trains,improving the train reliability,and reducing the operating costs.