异常检测：如何利用物联网提高核心产品的准确性和效率
公司规模
Mid-size Company
地区
- Europe
国家
- France
产品
- Coyote IoT devices
- Coyote mobile applications
- Dataiku Data Science Studio
技术栈
- Vertica
- Python
- PostgreSQL
实施规模
- Enterprise-wide Deployment
影响指标
- Productivity Improvements
- Customer Satisfaction
技术
- 分析与建模 - 预测分析
- 分析与建模 - 实时分析
适用行业
- 运输
适用功能
- 物流运输
用例
- 实时定位系统 (RTLS)
- 预测性维护
服务
- 数据科学服务
关于客户
Coyote 是欧洲领先的实时道路信息提供商。该公司成立于 2005 年，拥有 250 名员工。该公司在欧洲拥有 480 万用户，2014 年的营业额超过 1 亿欧元。Coyote 的产品包括基于物联网的设备和移动应用程序，使用户能够警告其他驾驶员他们在驾驶时发现的交通危险和状况。这些产品在很大程度上依赖于传入数据的准确性，尤其是其嵌入式地图中的行车速度限制。
挑战
Coyote 是欧洲领先的实时道路信息提供商，在保持其嵌入式地图中限速数据的准确性方面面临巨大挑战。这些数据对于其物联网设备和移动应用程序的运行至关重要，这些设备和应用程序可以警告驾驶员交通危险和状况。该公司需要一种基于算法的自动化解决方案，可以纠正限速数据并利用来自其物联网设备的大量传入数据来生成可操作的见解和预测。这还需要在公司内部灌输一种数据驱动的方法，其中决策基于真实数据而不是标准分析报告。
解决方案
Coyote 使用 Dataiku Data Science Studio 开发了一种利用大量 IoT 数据的算法。该算法将道路划分为多个部分并分析每个部分的模式，使 Coyote 能够构建一个预测模型来估算路段的速度限制。这一机器学习过程有助于检测速度限制异常，并使 Coyote 能够估算所显示速度限制的整体质量和可靠性。Dataiku DSS 的协作功能在此过程中至关重要，它使具有不同技能的员工能够协同工作，并促进公司内部对数据挖掘、可视化和智能数据问题的广泛了解。
运营影响
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