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Automated Railcar Inspections Increase Security and Revenue
技术
- 处理器与边缘智能 - 嵌入式和边缘计算机
适用行业
- 铁路与地铁
用例
- 边缘计算与边缘智能
服务
- 云规划/设计/实施服务
挑战
Duos Technologies Group, Inc.（“Duos”或“公司”——纳斯达克代码：DUOT）为行业和政府客户提供智能检测、自动化、安全和安保解决方案，不断突破 IT 的界限。为了跟上为其边缘轨道车检查扩展支持 AI 的数据捕获分析的步伐，该公司选择了最新的 Dell EMC PowerEdge 服务器。
Duos Technologies 面临的挑战是找到一种利用技术作为力量倍增器的方法，以满足客户对全速运行列车更好、更快的检查过程的要求。 Duos 在边缘使用 AI 开发了创新的数据分析解决方案，以进行更可靠的轨道车检查，在所有气候和条件下 24/7/365 可用。
客户
二重奏技术
关于客户
Duos Technologies , Inc. 提供了范围广泛的复杂智能技术解决方案，重点是关键任务应用。 duo tech 总部位于佛罗里达州杰克逊维尔，为北美各地的客户提供在所有类型环境中集成安全、安保、态势感知和自动化平台的服务。
解决方案
Duos Technologies Group 在边缘利用复杂的人工智能 (AI) 与 Dell EMC PowerEdge 服务器相结合，在不停止列车的情况下执行自动化安全检查
除了创新的货运铁路解决方案外，Duos 还构建了多层智能系统，以应对广泛行业中的复杂挑战——从保护关键石油、天然气和石化基础设施到惩教设施的运动控制和态势感知系统，所有这些都得到了支持戴尔科技公司。
运营影响
数量效益
相关案例.
Case Study
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Case Study
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The Delhi NCR Metro, a major public transportation system in India, was facing a challenge in providing accurate and comprehensive information to its daily commuters and tourists. The lack of a centralized platform for information about metro station details, train schedules, fare details, parking, elevators, and tourist locations was causing inconvenience to the users. The challenge was to develop a mobile app that could provide all this information accurately and conveniently. The app needed to be equipped with GPS services to help users find the nearest metro and renowned locations. An interactive map was also required to assist travelers who were familiar with the metro lines. The goal was to provide maximum information with minimum input.
Case Study
Cooperation with VR FleetCare for predictive analytics
Bogies are the most significant components of the rail fleet in terms of lifecycle costs and traffic safety. In addition to creating significant cost savings for the rail fleet owners, data-driven maintenance will enhance safety and the usability of the rolling stock. The predictive maintenance capability will improve reliability of the trains, cost-efficiency and passenger comfort. Train traffic will operate more reliably when it is possible to predict rolling stock malfunctions before they cause disruptions in traffic.
