Predictive Maintenance · Real-time Analytics
Quanzhou Water Group needed a unified digital operations model to manage water scarcity, urban flooding, pipe leakage, wastewater reliability, and high energy use across plants, pump stations, and dispersed treatment sites.
Reported impact
Quanzhou Water Group raised plant automation, cut energy use, improved wastewater reliability, reduced onsite inspection effort, and established a reusable smart-water operating model.
Quanzhou Water Group built its 'Water Brain' with Baidu AI Cloud and partner Eruidi to connect source water, treatment plants, distribution, and wastewater operations on a shared digital platform. BML Full-Featured AI Development Platform supplied the AI environment for computer-vision and forecasting workloads. Telemetry from plants, pump stations, and distributed wastewater sites was consolidated into a Big Data Platform that provided distributed compute, a data lake, visualization, and device connectivity. On top of that layer, an AI Water Demand Forecasting Model used historical consumption, weather, holiday, and seasonal signals to predict demand and dynamically tune pressure, flow, and frequency. Intelligent Vision Technology monitored worker safety and equipment anomalies, while a Digital Twin Platform modeled water processes so operators could monitor assets, optimize control loops, issue remote commands, and manage inspections electronically.
Plant telemetry, pump-station measurements, wastewater-site signals, historical water usage, and contextual inputs such as weather and holidays flow into the Big Data Platform. BML-developed vision and forecasting models analyze the feeds, while the Digital Twin Platform visualizes assets, optimizes control settings, and sends alerts or remote commands back to operators and field equipment.
Baidu AI Cloud's intelligent vision technology and Eruidi's applications can efficiently identify abnormal equipment operation and worker-production risks and warn on abnormal temperature and vibration (original: 百度智能云提供的智能视觉技术和埃睿迪公司实现的智能化应用，这些技术手段可以高效识别设备运行异常，及时发现员工生产作业风险，对异常的高温、震动及时预警).
The AI water-demand forecasting model combines historical water-use data with weather, holidays, and seasonal changes to predict demand more accurately (original: AI 用水量预测模型可结合历史用水数据，以及天气、节假日、季节变化等因素，通过机器学习算法更加精准地预测出用水量).
The Quanzhou Water Brain is the first to achieve integrated governance across rivers, lakes, sea areas, and reservoirs (original: 泉州水务大脑率先实现江、河、湖、海和水库水务综合治理).
The Water Brain improved personnel efficiency by more than 5%, reduced unit energy use for water production and supply by 8%, and increased the reliability of distributed wastewater-treatment facilities by 5% (original: 帮助泉州水务集团整体人员效率提升5%以上，制水供水单位能耗下降8%，分散式污水处理设施运行可靠性提升5%).
The big-data platform provides distributed computing, a data lake, and data visualization, while the digital-twin platform supports unified modeling and real-time monitoring and optimization (original: 大数据平台提供分布式计算、数据湖和数据可视化的技术…；数字孪生平台提供了相关水务模型的统一建模及模型管理…高效率完成各种工艺的实时监控和优化控制).
The group faced water shortages, urban flooding and waterlogging, pipe-network leakage, difficult secondary-water-supply management, high energy consumption, and user-experience issues (original: 面临水资源短缺，城市内涝和积水、管网漏损、二次供水设施管理困难、供水生产供应高耗能，以及用户体验亟需提高等问题).
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