吉林大学学报(信息科学版) ›› 2021, Vol. 39 ›› Issue (6): 662-668.

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施工现场人员车辆无感化管理技术研究与应用

李洪丰1 , 吴 越1 , 张树东1 , 刘红宇2 , 李 兴2 , 狄 波3   

  1. 1. 国网吉林省电力有限公司, 建设部, 长春 130021; 2. 北京国电通网络技术有限公司 建设及安监业务事业部, 北京 100192; 3. 国网吉林省电力有限公司 吉林省送变电工程有限公司项目管理部, 长春 130021
  • 收稿日期:2021-05-31 出版日期:2021-12-01 发布日期:2021-12-02
  • 作者简介:李洪丰(1969— ), 男, 长春人, 国网吉林省电力有限公司高级工程师, 主要从事电网建设管理研究, ( Tel) 86- 18143089373(E-mail)594122581@ qq. com。
  • 基金资助:
    2020-吉林施工现场智慧物联系统二期基金资助项目(B36806200591)

Research and Application of Non-Perceptible Management Technology for Personnel and Vehicles on Substation Construction Site

LI Hongfeng 1 , WU Yue 1 , ZHANG Shudong 1 , LIU Hongyu 2 , LI Xing 2 , DI Bo 3   

  1. 1. Construction Department, State Grid Jilin Electric Power Company Limited, Changchun 130021, China; 2. Construction and Safety Supervision Department, Beijing Guodiantong Network Technology Company Limited, Beijing 100192, China; 3. Project Management Department, State Grid Jilin Electric Power Company Limited Jilin Power Transmission and Transformation Engineering Company Limited, Changchun 130021, China
  • Received:2021-05-31 Online:2021-12-01 Published:2021-12-02

摘要: 为提高变电站施工现场人员车辆进出场管控的效率和精确度, 建设一套基于多种视频分析技术的无感化 交互人车管理系统, 以此取代传统管控手段, 构建智慧工地管控体系。 首先在现场管控软件系统中建立并维护 现场人员车辆基础信息库, 以提供视频分析比对基准; 在变电站正门及周界分别布设双向人员/ 车辆识别筒机、 全景摄像头和周界球机。 对视频流使用人脸识别、 入侵检测等视频分析技术进行实时识别分析, 达到人车考勤 统计和异常行为告警的管理目的。 实验及应用表明, 该系统对多目标平均识别准确率可达 95% , 大幅提高了 变电站施工现场人员车辆进出场管控的效率和精确度。

关键词: 智慧工地 , 无感化交互 , 动态多人脸识别技术 , 车牌识别技术 , 行为监测技术 , 人员车辆管控

Abstract: In order to improve the efficiency and accuracy of the monitoring personal and vehicles on the substation construction site, a method of non-perceptible interactive based on multiple video analysis technologies is established to replace traditional monitor methods and to build a smart construction site. First, a basic information database of on-site personnel and vehicles in the monitor software system is built to provide a benchmark for video analysis and comparison. Then two-way personnel / vehicle identification drums, panoramic cameras and perimeter dome cameras are deployed at the main entrance and perimeter of the substation. Face recognition, intrusion detection and other video analysis technologies for real-time recognition and analysis of video streams are used to check the attendance of personnel and vehicles and to trigger abnormal behavior alarms. The average multi-target recognition accuracy rate of this research application can reach 95% , which greatly improves the efficiency and accuracy of the entry and exit control of personnel and vehicles on the substation construction site.

Key words: smart construction site, non-perceptibleinteraction, dynamic multi-face recognition technology, license plate recognition technology, behavior monitoring technology, personnel and vehicle monitor

中图分类号: 

  • TP391. 41