Baidu AI Cloud Education China Translated from Chinese

North China Electric Power University (华北电力大学)

Enterprise Knowledge Search

North China Electric Power University wanted a general campus assistant that could answer everyday questions, guide users into the right business systems, and do so securely on university-controlled infrastructure without exposing sensitive institutional data.

Reported impact

Business outcome

The university gained a single conversational entry point for many campus services, improved the usability of existing systems, and protected sensitive data through private deployment instead of relying on a fully public AI service model.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement

Solution

North China Electric Power University built the 'i Huadian' assistant with Baidu AI Cloud as a campus-wide service for students and staff. The implementation combines Qianfan AppBuilder with a 'ERNIE 4.0 + Super Assistant' pattern: a very large model handles deep understanding and response generation, while a smaller intent-recognition model helps route users to the right business application or answer path. The assistant supports multi-turn dialogue for everyday needs such as checking notices, phone numbers, workflows, courses, meal-card top-ups, library borrowing, and document lookup. Just as importantly, the service was designed for controlled deployment: the Super Assistant's core application services run in a private on-premises machine room so institutional data stays inside a protected environment while operating cost remains manageable. The result is a conversational front door that links fragmented campus systems into one secure and easier-to-use experience.

Data flow

Campus notices, guides, workflows, and application-entry data are connected to the assistant, where Qianfan and ERNIE provide broad language understanding while a smaller model routes intent. Users ask questions in natural language, the assistant returns answers or pushes the right business-system entry point, and the full interaction remains inside the university's private deployment environment.

Solution architecture

5 components · 4 layers
  1. Compute
    • Qianfan Large Model Platform Provides the underlying large-model capability for broad campus question answering and service generation.
  2. Serving
    • ERNIE 4.0 + Super Assistant Solution Handles complex understanding and response generation for the campus-wide AI assistant experience.
    • Intent Recognition Small Model Detects user intent precisely so the assistant can push the right campus application entry points.
  3. Orchestration
    • Qianfan AppBuilder Supports the assembly of the university's campus-assistant application and service logic.
  4. Governance
    • Private On-premises Deployment Keeps the assistant's core services and data inside the university's own protected machine-room environment.

Architecture clues

  • Private machine-room deployment is central to the solution's security and cost design.
  • The assistant is intended to push users directly into campus applications rather than only answer questions in chat.
  • The assistant uses a large-plus-small-model pattern, with a large model for depth and a smaller model for intent routing.

Evidence from the source

Baidu proposed an 'ERNIE 4.0 + Super Assistant' solution for the campus assistant (original: 创新性地提出了"文心4.0+超级助理解决方案").
Core application services are deployed in a local private machine room to protect all data and reduce large-model privatization cost (original: 超级助理的核心应用服务被部署在本地私有化的机房中,所有数据都受到严格保护).
The assistant combines a thousand-billion-parameter large model with a hundred-billion small model for intent recognition (original: 它采用了千亿级别的文心大模型进行深度服务,同时结合百亿级别的小模型进行精准的意图识别).
The university built the 'i Huadian' assistant on Qianfan AppBuilder to support notices, workflows, courses, meal-card top-ups, and library queries (original: 基于千帆AppBuilder,打造AI助手"i华电"…涵盖查课程、充饭卡、借书籍、查文献等校园生活高频需求).
Transparency Evidence & methodology 75/100 reference value · 87% 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 value75/100
  • 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 impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • 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
    4 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 specificity8/10
    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

    North China Electric Power University (华北电力大学) customer story

    First-party
    https://cloud.baidu.com/customer/case/huabeidianlidaxue.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.