Baidu AI Cloud Public Sector China Translated from Chinese

Haidian District (北京市海淀区)

IT Operations Automation · Real-time Analytics

Haidian's rapid urban growth made manual, department-siloed governance too slow and expensive, especially for problems like muck-truck regulation where data fragmentation and cross-department coordination prevented efficient response.

160,000 data items processed daily
20+ smart scenarios
200,000 algorithm calls
21 TB of algorithm data per day

Reported impact

Business outcome

Haidian improved urban-governance precision and enforcement efficiency, sharply reduced muck-truck violations, and created a reusable smart-city operating model for other safety and service scenarios.

  • Customer Experience
  • Productivity Improvement
  • Risk Reduction

Solution

Baidu AI Cloud and Haidian District built a city-brain operating model that combines AI recognition, spatiotemporal data, and cross-department data fusion to automate urban-governance scenarios. In the featured muck-truck use case, the city brain unifies construction-site records, satellite map layers, nearby video feeds, disposal-site data, and departmental datasets from traffic, environmental, and urban-management systems into a single one-vehicle-one-file view. The project follows a 'three integrations and five cross-domain links' design that merges technology, business, and data while reaching across levels, towns, systems, departments, and business functions. Baidu's data service center acts as the core data hub, using deep learning, knowledge graphs, and multimodal data understanding to capture violations, predict routes, support law-enforcement evidence, and automate case handling. The same model then extends to other city operations such as snow and ice response, where camera-based AI can warn municipal teams tens of minutes earlier than traditional monitoring alone.

Data flow

Construction-site data, satellite imagery, road video, disposal-site information, and cross-department datasets are fused inside the city brain, AI models identify vehicles and violations, prediction models estimate routes and risk, and the outputs flow into enforcement, dispatch, and other city-governance actions.

Solution architecture

2 components · 2 layers
  1. Storage
    • Data Service Center Acts as the core urban data hub, processing heterogeneous image, voice, spatial, and operational data for governance analytics and action.
  2. Orchestration
    • Haidian City Brain Coordinates cross-domain urban-governance data, AI recognition, prediction, and enforcement workflows across Haidian's smart-city scenarios.

Architecture clues

  • The data platform processes multimodal urban data at tens of millions of records per day with many sub-algorithms.
  • The design follows a 'three integrations and five cross-domain links' governance pattern.
  • The same city-brain foundation supports both vehicle-governance and adverse-weather response workflows.

Evidence from the source

Haidian uses Baidu AI Cloud's artificial-intelligence technology in the city brain to fuse needs, business, data, and technology and keep evolving smart-city governance. (original: 利用百度智能云人工智能技术,在海淀城市大脑实现全面的创新应用,形成需求、业务、数据、技术的融合发展和动态演进。)
The city brain is built on a core data service center that turns image, voice, and spatial data into an urban data brain for intelligent city operation. (original: “数据服务中心”作为百度城市大脑的核心组成部分...通过人工智能、大数据、知识图谱等技术手段,对城市影像、语音、空间等多源异构数据进行智能化识别、理解、处理和分析,形成“城市数据大脑”。)
The platform processes more than 60 million data records a day, about 160,000 items daily, 21T of algorithm data per day, nearly 200 sub-algorithms, and 20+ smart scenarios. (original: 摄像头上万路,传感器2万点位作用,每天获取的数据大概有6000多万条,日均需要处理数据16万条左右;感知算法数据达到每天21T,涉及近200个子算法、20万次调用、20多个智慧化场景。)
The solution integrates construction-site data, satellite map layers, nearby video, disposal-site data, and city-level department data into a full-chain one-vehicle-one-file system. (original: 系统整合了工地信息、卫星图斑、周边视频、消纳地点等监测数据,以及交通、环保、城管等市级和区级跨部门数据,将渣土车全链条的数据进行打通、融合,构建一车一档信息。)
The system increased muck-truck captures by nearly 30%, pushed violation-feature recognition accuracy above 95%, route prediction above 60%, and reduced the violation rate from 26% to 4%. (original: 可以实现渣土车抓拍数量提升近30%,系统对渣土车号牌遮挡等违法特征识别的准确率达到95%以上,渣土车行驶轨迹预测分析准确率超过60%,渣土车违规率从26%下降到了4%。)
Transparency Evidence & methodology 95/100 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 value95/100
  • 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 impact25/25
    6 metrics 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

    Haidian District (北京市海淀区) customer story

    First-party
    https://cloud.baidu.com/customer/case/haidian.html
    Source type
    Vendor-published customer story
    Published
    May 8, 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 8, 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.

Related

Similar use cases

See all like this
Baidu AI Cloud Jun 19, 2025 Haikou Traffic Police Detachment Public Sector China After Typhoon Yagi damaged most field sensing equipment, Haikou traffic police urgently needed to restore signal-control effectiveness without waiting for every radar, camera, and detector to be rebuilt, or the city… 567 intersections were affected by the typhoon. IT Operations AutomationReal-time Analytics Edge Computing Node · Qianfan AppBuilder · Qianfan ModelBuilder Baidu AI Cloud May 14, 2025 Jingxiong Expressway Hebei Section Public Sector China Jingxiong Expressway needed a more precise and collaborative operating model because fragmented systems, weak video perception, rigid workflows, and slow emergency coordination limited how effectively highway managers… Business-system efficiency is expected to improve 80%+. IT Operations AutomationReal-time Analytics BaiGe Heterogeneous Compute Platform · Highway Industry Large Model · Qianfan Large Model Platform Baidu AI Cloud May 13, 2025 Hebei Expressway Group Jingxiong Expressway Branch (河北高速公路集团有限公司京雄分公司) Public Sector China Jingxiong Expressway operators struggled with rigid workflows, fragmented data and systems, weak video perception, and slow emergency handling, which limited the precision and responsiveness of highway operations… Business-system efficiency is expected to improve 80%+. IT Operations AutomationReal-time Analytics Highway Industry Large Model · Qianfan AppBuilder · Qianfan ModelBuilder