Baidu AI Cloud Public Sector China Translated from Chinese

Guangzhou Baiyun District Water Affairs Department (广州市白云区水务部门)

Predictive Maintenance · Real-time Analytics

Baiyun District needed to raise flood-response, reservoir management, and drainage supervision efficiency despite limited staff, more than 50 flood-prone tunnels, over 90,000 drainage users, fragmented external systems, and no unified digital base for water data and decision-making.

50+ flood-prone culverts and tunnels are covered by intelligent waterlogging warning.
90,000+ drainage users can be monitored in real time.
Baiyun District previously had only 48 water-quality monitoring points.
Reservoir patrol efficiency improved 80%.

Reported impact

Business outcome

Baiyun District reduced manual patrol effort, improved real-time supervision, extended monitoring to drainage sources and flood points, and gave water managers a faster way to detect issues and coordinate response.

  • Productivity Improvement
  • Risk Reduction
  • Scalability

Solution

Guangzhou Baiyun District introduced Baidu AI Cloud's Smart Water platform, district brain, and Kaiwu Industrial Internet Platform to create a unified intelligent water-management backbone. The implementation combines a digital base with an intelligence engine in a '1+1+N' architecture, where the water brain supports multiple operational applications across river and reservoir management, water-engineering operations, flood emergency response, and drainage supervision. Field sensors, cameras, and monitoring devices send water-level, rainfall, seepage-pressure, displacement, and discharge data into the platform, where AI models detect abnormal conditions and trigger alerts to mobile apps, control-room screens, and onsite broadcast systems. At Helong Reservoir, the system adds intrusion detection and electronic fences for remote patrol operations.

Data flow

Cameras, flood sensors, reservoir monitors, and drainage sensors send operational and environmental data into the unified Kaiwu-based platform. The district water brain analyzes those feeds for flood risk, intrusion, compliance, and water-condition anomalies, then pushes alerts to mobile apps, control-room displays, broadcast devices, and enforcement workflows.

Solution architecture

3 components · 2 layers
  1. Serving
    • Baidu AI Cloud Smart Water Provides the intelligent water-operations applications used for reservoir supervision, drainage monitoring, and flood response.
    • District Brain Acts as the district-level decision layer that consolidates water data and supports scenario analysis and command dispatch.
  2. Orchestration
    • Kaiwu Industrial Internet Platform Supplies the unified digital foundation used to collect, manage, and process water-related data across the district.

Architecture clues

  • Drainage supervision monitors COD, pH, and suspended solids at discharge outlets
  • Flood-warning logic can automatically trigger alarms and lower gates when water depth exceeds thresholds
  • Reservoir operations include intrusion detection, electronic fences, and mobile alert delivery
  • The project uses a '1+1+N' architecture of digital base, intelligence engine, and multiple scenario applications

Evidence from the source

At drainage outlets, Baidu AI Cloud deployed smart sensors to collect indicators such as COD, pH, and suspended solids and let the water brain judge compliance (original: 在排水户的排污口布设智能传感器,将对预处理后的污水进行监测,收集化学需氧量、酸碱度、悬浮物等关键指标信息,交由后端的水务大脑进行甄别).
At the reservoir, the system delivers real-time warning, intrusion detection, electronic fences, and key water-condition monitoring to mobile phones, computers, and control-room screens (original: 入侵检测、电子围栏等智能应用落地后…系统就会自动报警…还将信息发送到工作人员手机APP上、监控室大屏上…可统计降雨量、水位、渗流渗压、位移沉降等).
Baidu AI Cloud won the Baiyun District smart-water project in March 2022 and used the Kaiwu Industrial Internet Platform to build a new intelligent water-services foundation (original: 2022年3月,百度智能云中标白云区智慧水务建设项目,将依托开物工业互联网平台,打造新一代水务智能化底座).
The case reports 50+ intelligent flood warnings, 90,000+ real-time monitored drainage users, and an 80% improvement in reservoir patrol efficiency (original: 50+ 涵洞积水智能预警;90000+ 排水户排污实时监测;80% 水库巡查效率提升).
The project created a '1+1+N' architecture in which a water brain supports multiple application models for analysis, prediction, and operations (original: 双方在白云智慧水务项目中打造“1+1+N”的架构…以一个智能化大脑支撑N种应用模型的分析、预测、运行等).
Transparency Evidence & methodology 85/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value85/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact15/25
    1 metric describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Baidu AI Cloud

    Guangzhou Baiyun District Water Affairs Department (广州市白云区水务部门) customer story

    First-party
    https://cloud.baidu.com/customer/case/guangzhoushuiwuju.html
    Source type
    Vendor-published customer story
    Published
    May 9, 2025
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page May 9, 2025 · Baidu AI Cloud · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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