[1]刘帅,苑连更,胥雷刚,等.铁路工机具上下线智能化清点核查系统研究[J].高速铁路技术,2025,16(06):90-96.[doi:10.12098/j.issn.1674-8247.2025.06.013]
　LIU Shuai,YUAN Liangeng,XU Leigang,et al.Software Development for Automated Inventory and Verification of Railway Maintenance Tools[J].HIGH SPEED RAILWAY TECHNOLOGY,2025,16(06):90-96.[doi:10.12098/j.issn.1674-8247.2025.06.013]
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铁路工机具上下线智能化清点核查系统研究()

《高速铁路技术》[ISSN:1674-8247/CN:51-1730/U]

卷:
16卷
期数:
2025年06期
页码:
90-96
栏目:
建造运维
出版日期:
2025-12-30

文章信息/Info

Title:
Software Development for Automated Inventory and Verification of Railway Maintenance Tools
文章编号:
1674-8247(2025)06-0090-07
作者:
刘帅1; 苑连更2; 胥雷刚2; 李岩2; 白双1
(1.北京交通大学, 北京 100044; 2.中国铁路北京局集团有限公司, 北京 100070)
Author(s):
LIU Shuai1;  YUAN Liangeng2;  XU Leigang2;  LI Yan2;  BAI Shuang1
(1.Beijing Jiaotong University, Beijing 100044, China; 2. China Railway Beijing Group Co., Ltd., Beijing 100070, China)
关键词:
高速铁路运维;  工机具管理;  目标检测;  智能清点核查系统
Keywords:
high-speed railway maintenance;  tool management;  object detection;  Intelligent Inventory Verification System
分类号:
U216
DOI:
10.12098/j.issn.1674-8247.2025.06.013
文献标志码:
A
摘要:
高速铁路夜间检修是保障列车持续、安全、稳定运行的重要环节。针对当前高速铁路工务运维中人工清点效率低、核查易出错等问题,本文设计并实现了一套铁路工机具上下线智能化自动清点核查系统。系统采用B/S架构,前端基于Vue框架,后端业务服务采用SpringBoot构建,结合MongoDB进行数据管理。在实际应用中,作业人员通过微信小程序采集并上传现场图像; 系统后端调用部署于Flask框架的旋转目标检测模型对工机具进行精准识别,并与计划工单自动比对,实现异常结果提示与核查结果输出。测试结果表明,系统平均响应时间为1.8 s,识别准确率达96%。该系统运行稳定,安全性和兼容性良好,满足实际铁路运维场景下的部署与使用需求。
Abstract:
Nighttime maintenance of high-speed railways is crucial to ensuring the continuous, safe, and efficient operation of trains. To mitigate the inefficiencies and high error rates associated with manual inventory checks in current railway maintenance workflows, this paper designed and implemented an intelligent automatic inventory and verification system for railway tools and equipment. The system employs a browser-server(B/S)architecture, with the front-end developed using the Vue framework and the back-end services built on SpringBoot, integrated with MongoDB for efficient data management. In operational settings, field personnel use a WeChat Mini Program to capture and upload images of tools. The server-side component utilizes a rotated object detection model, deployed on a Flask framework, to accurately identify tools and automatically compare them with the corresponding task sheets, generating anomaly alerts and verification results. The test results show that the system has an average response time of 1.8 seconds and an accuracy rate of 96%. The system operates stably with strong security and compatibility, meeting the deployment and operational requirements in real-world railway maintenance scenarios.

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备注/Memo

备注/Memo:
收稿日期:2025-07-07
作者简介:刘帅(2001-),男,硕士研究生。
基金项目:中国铁路北京局集团有限公司科技研究开发计划重点课题(2024BG01)
更新日期/Last Update: 2025-12-30