• 你的编织宇宙

    Knit Knit

    制作了织女必备的毛线整理和灵感收集工具:YarnInspire - 毛线管理与灵感收集

    涉及技术:OpenCode+DeepSeek作为本地agent,ChatGPT5.2作为线上客服(兼翻译),豆包辅助生图+Figma调整。

  • 养鱼游戏

    Aquarium

    用ai制作了网页版的放置类养鱼游戏:https://guoguoluo.github.io/aquarium/

    玩家将经营水族设施,饲养不同品质的鱼群,副产品会随时间自动产出并兑换货币,用于购买鱼苗、扩充收藏。成年鱼会按周期进入求偶并有概率繁殖后代,后代继承特征且可能突变(也许会产出一个会说话的黄色海绵或是带穿宇航服的松鼠!谁知道呢?)。玩家还可点击、拖拽与鱼互动,打造独一无二的深海生态。

    涉及技术:ChatGPT5.2,豆包辅助生图+Figma调整。

  • AI建筑立面理解+诺亚智能立面生成匹配

    研究方向:如何根据【立面参考图片】自动匹配生成与其特征相近而不一致的立面方案设计模型。

    技术包含:AI图像识别(迁移学习)+ AI专家系统(GH模拟)+ RPA制图(GH诺亚五代智能立面生形框架)

     

    Partners: Qiang Zhan, Mengchen Zhu, Xiaoxin Wang, Yuansheng Lin

  • 基于智慧社区的“微扰动”更新方法

    Approach to Accurately Micro-renovate Old Microdistricts Based on Video Surveillance

    A microdistrict (also called xiaoqu in Chinese) is a residential complex, a primary structural element of a residential area construction, and the most universal way to arrange housing for indigenous Chinese. Today, many old microdistricts in China need to be renovated. However, the traditional way of investigating the sites by architects themselves and then putting forward design proposals is neither labour-saving nor accurate, for architects can only observe parts of the problems in the given time for investigation. This project presents a new way to investigate old microdistricts based on surveillance videos. In this way, designers can easily know the real needs of users, can hear the indigenous voices, which lead to a more accurate renovation. This approach uses YOLOv3 to detect humans and cars in the image taken from the videos. According to these data, the exact time and area, when and where people and cars would appear, could be known. On the basis of it, accurate renovation design proposals are put forward. In this project, the approach is practised in a real microdistrict in Shanghai. The article will first introduce the background, methodology, and significance of this study. Then, the approach to accurately analysing and renovating old microdistricts based on surveillance videos would be described. Ultimately, the implementation will be presented.

    Partners: Di Zhu, Zhenzhao Xu