数据与模型双核驱动的城市轨道交通自动售检票智能运维系统架构设计与实现
李景虎1蔡佳妮2范海斌3夏熠1高习明4
Design and Realization of Urban Rail Transit AFC Intelligent Operation and Maintenance System Architecture Driven by Dual-core of Data and Model
LI Jinghu1CAI Jiani2FAN Haibin3XIA Yi1GAO Ximing4
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作者信息:1.上海申通地铁集团有限公司运营设施设备管理部，201102，上海
2.上海申通地铁集团有限公司技术中心，201103，上海
3.上海轨道交通第三运营公司，200070，上海
4.上海华虹计通智能系统股份有限公司，201206，上海
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Affiliation:1.Operation Facilities and Equipment Management Department, Shanghai Shentong Metro Group Co., Ltd., 201102, Shanghai, China
2.Technical Center, Shanghai Shentong Metro Group Co., Ltd., 201103, Shanghai, China
3.Shanghai Metro Third Operating Company, 200070, Shanghai, China
4.Shanghai Huahong Jitong Intelligent Systems Co., Ltd., 201206, Shanghai, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2024.06.037
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中图分类号/CLCN:U231.94∶U293.2+2
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栏目/Col:研究报告
摘要:
［目的］城市轨道交通传统AFC(自动售检票)系统存在维修速度慢、维修方法过时、维修计划不合理和维修成本高等问题。需要采用数字化与智能化主动监测维护方法，基于数据与模型双核驱动搭建城市轨道交通AFC智能运维系统，将周期性的计划修转变为状态修和预知修，进而提高运维效率，降低运维成本。［方法］介绍了城市轨道交通AFC智能运维系统的架构及主要功能。先通过多传感器融合收集终端设备的数据，并通过上传到大数据平台进行处理，再利用深度学习算法与天牛须搜索优化算法对收集的数据进行数据分析，并优化模型，进而识别故障模式和预测潜在的设备故障。此外，该系统还采用了自动化工作流程管理工具来分析运维任务的执行效率，以优化日常维护和紧急响应策略。［结果及结论］该系统在上海轨道交通3号线试点运行中表现出色，能实现智能维护、设备动态履历管理、远程监控、移动终端、无纸化维护等功能，其运行平稳、操作方便、流程顺畅。通过效益分析可知，该系统切实提高了工作维修效率，降低了运营成本。
Abstracts:
［Objective］ The traditional urban rail transit AFC (automatic fare collection) system suffers from maintenance problems such as slow speed, outdated method, unreasonable plan and high cost.It is necessary to adopt digital and intelligent active monitoring and maintenance methods, and build an urban rail transit AFC intelligent operation and maintenance system architecture drive by dual-core of data and model, transforming periodic scheduled maintenance into condition-based and predictive maintenance, so as to improve the efficiency of operation and maintenance and reduce the cost. ［Method］ The architecture and main functions of AFC intelligent operation and maintenance system for urban rail transit is introduced.Firstly, the terminal equipment data are collected through multi-sensor fusion and uploaded to the big data platform for processing. Then, the collected data are analyzed by deep learning algorithm and BAS (beetle antennae search algorithm) , and the model is optimized to further identify the failure mode and predict the potential equipment failure. In addition, the system uses automated workflow management tools to analyze the execution efficiency of the operation and maintenance tasks, and optimize the routine maintenance and emergency response strategies. ［Result & Conclusion］ The system performs well in the trial operation on Shanghai Metro Line 3, and it can realize intelligent maintenance, dynamic equipment history management, remote monitoring, mobile terminal, paperless maintenance and other functions, featuring stable running, convenient operating, and smooth workflow. The benefit analysis shows that the system effectively improves the maintenance efficiency and reduces the operation cost.
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