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  3. 基于OpenHarmony的智能化应用生态挑战赛
OpenAtomGeneral MLAdvancedRegistration unverified

基于OpenHarmony的智能化应用生态挑战赛

A advanced general ml competition requiring roughly 25–70 hours for an end-to-end practice run.

Difficulty
Advanced
Estimated effort
25–70 hours
Registration deadline
To be confirmed
Compute
CPU only
OverviewPreparationSource

Overview

Who this competition fits

Advanced practitioners with experience in general machine learning. Requires strong technical foundations and comfort with complex ML pipelines.

Read the original official blurb
本赛题旨在激发和培养参赛者对开源系统的兴趣和对智能化场景的掌握能力,同时也为OpenHarmony系统贡献更多的创新应用。我们希望参赛者围绕OpenHarmony系统和适配其的智能硬件,开发出能够丰富OpenHarmony应用生态,改善用户体验的项目和解决方案。 Official category: 实战竞技赛. Organizers: 北京中软国际教育科技股份有限公司. Participants: 5.

Preparation

From registration to a first submission

  1. 01

    Python

  2. 02

    scikit-learn or similar ML library

  3. 03

    data analysis with pandas

Before you commit: Requires solid machine learning fundamentals and good experimental methodology. The competitive landscape is strong, with many experienced teams.

Source

How this page was assembled

Competition information is structured from the official page. Scores are platform estimates for decision support; official rules take precedence.

Official competition page
OpenAtom
Last checked
Feb 21, 2024

Entry assessment

est.
22

Prepare before joining

Overall fit estimate

Beginner fit22/100
Learning value71/100

Confirm eligibility and time commitment before moving into formal preparation.

Entry eligibility needs official confirmation

Open official pageSign in to saveOpen guide

Data and review state

Separate what is confirmed from what still needs review. Official rules and deadlines win.

View data stateClose
Source page verifiedMetric confirmedReviewed
Source published
Feb 21, 2024
Source created
Feb 21, 2024
Published on this site
Aug 1, 2026

Official metric is not published on the OpenAtom competition listing; verify scoring on the competition page.

reward_value_usd is a rough CNY→USD estimate (×0.14) for ranking only; use reward_summary for the official prize text.