json-canvas（JSON canvas 搜索）

学习笔记可视化Canvas技能

🎨 设计理念

为学习笔记优化的可视化系统，实现：

• 知识结构化: 将抽象概念转化为直观图形
• 关系可视化: 清晰展示知识间的关联
• 路径规划: 可视化学习进程和路径
• 思维整理: 支持头脑风暴和概念梳理

📊 核心应用

JSON Canvas = 节点系统 + 连接关系 + 布局算法

• 节点类型: 文本、文件、链接、分组
• 连接系统: 有向边、无向边、标签边
• 布局模式: 层次、网状、自由布局
• 交互功能: 缩放、拖拽、折叠展开

File Structure

A canvas file contains two top-level arrays:

{
  "nodes": [],
  "edges": []
}

• nodes (optional): Array of node objects
• edges (optional): Array of edge objects connecting nodes

Nodes

Nodes are objects placed on the canvas. There are four node types:

• text - Text content with Markdown
• file - Reference to files/attachments
• link - External URL
• group - Visual container for other nodes

Z-Index Ordering

Nodes are ordered by z-index in the array:

• First node = bottom layer (displayed below others)
• Last node = top layer (displayed above others)

Generic Node Attributes

All nodes share these attributes:

Text Nodes

Text nodes contain Markdown content.

{
  "id": "6f0ad84f44ce9c17",
  "type": "text",
  "x": 0,
  "y": 0,
  "width": 400,
  "height": 200,
  "text": "# Hello World\n\nThis is **Markdown** content."
}

File Nodes

File nodes reference files or attachments (images, videos, PDFs, notes, etc.).

{
  "id": "a1b2c3d4e5f67890",
  "type": "file",
  "x": 500,
  "y": 0,
  "width": 400,
  "height": 300,
  "file": "Attachments/diagram.png"
}

{
  "id": "b2c3d4e5f6789012",
  "type": "file",
  "x": 500,
  "y": 400,
  "width": 400,
  "height": 300,
  "file": "Notes/Project Overview.md",
  "subpath": "#Implementation"
}

Link Nodes

Link nodes display external URLs.

{
  "id": "c3d4e5f678901234",
  "type": "link",
  "x": 1000,
  "y": 0,
  "width": 400,
  "height": 200,
  "url": "https://obsidian.md"
}

Group Nodes

Group nodes are visual containers for organizing other nodes.

{
  "id": "d4e5f6789012345a",
  "type": "group",
  "x": -50,
  "y": -50,
  "width": 1000,
  "height": 600,
  "label": "Project Overview",
  "color": "4"
}

{
  "id": "e5f67890123456ab",
  "type": "group",
  "x": 0,
  "y": 700,
  "width": 800,
  "height": 500,
  "label": "Resources",
  "background": "Attachments/background.png",
  "backgroundStyle": "cover"
}

Background Styles

Edges

Edges are lines connecting nodes.

{
  "id": "f67890123456789a",
  "fromNode": "6f0ad84f44ce9c17",
  "toNode": "a1b2c3d4e5f67890"
}

{
  "id": "0123456789abcdef",
  "fromNode": "6f0ad84f44ce9c17",
  "fromSide": "right",
  "fromEnd": "none",
  "toNode": "b2c3d4e5f6789012",
  "toSide": "left",
  "toEnd": "arrow",
  "color": "1",
  "label": "leads to"
}

Side Values

End Shapes

Colors

The canvasColor type can be specified in two ways:

Hex Colors

{
  "color": "#FF0000"
}

Preset Colors

{
  "color": "1"
}

Note: Specific color values for presets are intentionally undefined, allowing applications to use their own brand colors.

🎓 学习笔记Canvas示例

🧠 概念思维导图

{
  "nodes": [
    {
      "id": "neural-network-main",
      "type": "text",
      "x": 400,
      "y": 200,
      "width": 320,
      "height": 180,
      "text": "# 🧠 神经网络\n\n**核心概念**\n- 受生物神经系统启发\n- 多层神经元连接\n- 通过训练学习模式",
      "color": "6"
    },
    {
      "id": "basic-concepts",
      "type": "group",
      "x": 50,
      "y": 50,
      "width": 280,
      "height": 400,
      "label": "📖 基础概念",
      "color": "4"
    },
    {
      "id": "neuron",
      "type": "text",
      "x": 80,
      "y": 100,
      "width": 220,
      "height": 120,
      "text": "## 神经元\n\n- 基本计算单元\n- 接收输入信号\n- 产生输出响应\n- **权重**调节连接强度",
      "color": "4"
    },
    {
      "id": "activation",
      "type": "text",
      "x": 80,
      "y": 250,
      "width": 220,
      "height": 120,
      "text": "## 激活函数\n\n- **Sigmoid**: (0,1)\n- **ReLU**: max(0,x)\n- **Tanh**: (-1,1)\n- 引入非线性变换",
      "color": "4"
    },
    {
      "id": "algorithms",
      "type": "group",
      "x": 780,
      "y": 50,
      "width": 280,
      "height": 400,
      "label": "⚙️ 核心算法",
      "color": "3"
    },
    {
      "id": "forward",
      "type": "text",
      "x": 810,
      "y": 100,
      "width": 220,
      "height": 120,
      "text": "## 前向传播\n\n$$y = f(Wx + b)$$\n\n- 输入→隐藏→输出\n- 逐层计算激活值\n- 得到预测结果",
      "color": "3"
    },
    {
      "id": "backward",
      "type": "text",
      "x": 810,
      "y": 250,
      "width": 220,
      "height": 120,
      "text": "## 反向传播\n\n$$\\frac{\\partial L}{\\partial W} = \\delta \\cdot a^T$$\n\n- 计算损失梯度\n- 链式法则求导\n- 更新网络参数",
      "color": "3"
    },
    {
      "id": "applications",
      "type": "group",
      "x": 400,
      "y": 450,
      "width": 320,
      "height": 200,
      "label": "🎯 应用领域",
      "color": "1"
    },
    {
      "id": "cv",
      "type": "text",
      "x": 430,
      "y": 500,
      "width": 130,
      "height": 80,
      "text": "## 计算机视觉\n- 图像识别\n- 目标检测",
      "color": "1"
    },
    {
      "id": "nlp",
      "type": "text",
      "x": 580,
      "y": 500,
      "width": 130,
      "height": 80,
      "text": "## 自然语言\n- 文本分类\n- 机器翻译",
      "color": "1"
    }
  ],
  "edges": [
    {
      "id": "main-to-concepts",
      "fromNode": "neural-network-main",
      "fromSide": "left",
      "toNode": "basic-concepts",
      "toSide": "right",
      "label": "包含",
      "color": "4"
    },
    {
      "id": "main-to-algorithms",
      "fromNode": "neural-network-main",
      "fromSide": "right",
      "toNode": "algorithms",
      "toSide": "left",
      "label": "使用",
      "color": "3"
    },
    {
      "id": "main-to-applications",
      "fromNode": "neural-network-main",
      "fromSide": "bottom",
      "toNode": "applications",
      "toSide": "top",
      "label": "应用于",
      "color": "1"
    },
    {
      "id": "forward-to-backward",
      "fromNode": "forward",
      "fromSide": "bottom",
      "toNode": "backward",
      "toSide": "top",
      "label": "梯度",
      "toEnd": "arrow",
      "color": "2"
    }
  ]
}

📚 学习路径规划图

{
  "nodes": [
    {
      "id": "learning-path",
      "type": "group",
      "x": 0,
      "y": 0,
      "width": 1200,
      "height": 600,
      "label": "🎯 机器学习学习路径",
      "backgroundStyle": "cover"
    },
    {
      "id": "foundation",
      "type": "group",
      "x": 50,
      "y": 100,
      "width": 300,
      "height": 400,
      "label": "📖 基础阶段",
      "color": "4"
    },
    {
      "id": "math-basics",
      "type": "text",
      "x": 80,
      "y": 150,
      "width": 240,
      "height": 100,
      "text": "## 数学基础\n\n- 线性代数\n- 微积分\n- 概率统计\n- 优化理论",
      "color": "4"
    },
    {
      "id": "programming",
      "type": "text",
      "x": 80,
      "y": 280,
      "width": 240,
      "height": 100,
      "text": "## 编程基础\n\n- Python语法\n- NumPy/Pandas\n- 数据可视化\n- 算法基础",
      "color": "4"
    },
    {
      "id": "ml-concepts",
      "type": "text",
      "x": 80,
      "y": 410,
      "width": 240,
      "height": 80,
      "text": "## ML概念\n\n- 监督/无监督学习\n- 特征工程\n- 模型评估",
      "color": "4"
    },
    {
      "id": "core-algorithms",
      "type": "group",
      "x": 400,
      "y": 100,
      "width": 300,
      "height": 400,
      "label": "⚙️ 核心算法",
      "color": "3"
    },
    {
      "id": "classical-ml",
      "type": "text",
      "x": 430,
      "y": 150,
      "width": 240,
      "height": 100,
      "text": "## 经典算法\n\n- 线性回归\n- 决策树\n- SVM\n- 集成学习",
      "color": "3"
    },
    {
      "id": "neural-networks",
      "type": "text",
      "x": 430,
      "y": 280,
      "width": 240,
      "height": 100,
      "text": "## 神经网络\n\n- 感知机\n- 多层网络\n- 反向传播\n- 激活函数",
      "color": "3"
    },
    {
      "id": "optimization",
      "type": "text",
      "x": 430,
      "y": 410,
      "width": 240,
      "height": 80,
      "text": "## 优化方法\n\n- 梯度下降\n- Adam优化\n- 正则化技术",
      "color": "3"
    },
    {
      "id": "advanced-topics",
      "type": "group",
      "x": 750,
      "y": 100,
      "width": 300,
      "height": 400,
      "label": "🚀 进阶主题",
      "color": "1"
    },
    {
      "id": "deep-learning",
      "type": "text",
      "x": 780,
      "y": 150,
      "width": 240,
      "height": 100,
      "text": "## 深度学习\n\n- CNN卷积网络\n- RNN循环网络\n- Transformer\n- 注意力机制",
      "color": "1"
    },
    {
      "id": "specialized",
      "type": "text",
      "x": 780,
      "y": 280,
      "width": 240,
      "height": 100,
      "text": "## 专业领域\n\n- 计算机视觉\n- 自然语言处理\n- 强化学习\n- 推荐系统",
      "color": "1"
    },
    {
      "id": "practical",
      "type": "text",
      "x": 780,
      "y": 410,
      "width": 240,
      "height": 80,
      "text": "## 实践应用\n\n- 项目实战\n- 模型部署\n- 性能优化",
      "color": "1"
    }
  ],
  "edges": [
    {
      "id": "foundation-to-core",
      "fromNode": "foundation",
      "fromSide": "right",
      "toNode": "core-algorithms",
      "toSide": "left",
      "label": "掌握基础后",
      "toEnd": "arrow",
      "color": "6"
    },
    {
      "id": "core-to-advanced",
      "fromNode": "core-algorithms",
      "fromSide": "right",
      "toNode": "advanced-topics",
      "toSide": "left",
      "label": "深入理解",
      "toEnd": "arrow",
      "color": "2"
    },
    {
      "id": "math-to-programming",
      "fromNode": "math-basics",
      "fromSide": "bottom",
      "toNode": "programming",
      "toSide": "top",
      "label": "同步学习",
      "color": "5"
    },
    {
      "id": "programming-to-ml",
      "fromNode": "programming",
      "fromSide": "bottom",
      "toNode": "ml-concepts",
      "toSide": "top",
      "label": "实践结合",
      "color": "5"
    }
  ]
}

🔍 知识关联图谱

{
  "nodes": [
    {
      "id": "central-concept",
      "type": "text",
      "x": 500,
      "y": 300,
      "width": 350,
      "height": 200,
      "text": "# 🎯 深度学习\n\n**核心研究领域**\n- 基于神经网络的多层学习\n- 自动特征提取\n- 端到端训练\n\n**关键突破**: ImageNet 2012",
      "color": "6"
    },
    {
      "id": "related-notes",
      "type": "group",
      "x": 100,
      "y": 50,
      "width": 250,
      "height": 180,
      "label": "📝 相关笔记",
      "color": "5"
    },
    {
      "id": "nn-basics",
      "type": "file",
      "x": 120,
      "y": 80,
      "width": 210,
      "height": 60,
      "file": "神经网络基础.md",
      "subpath": "#核心概念"
    },
    {
      "id": "backprop",
      "type": "file",
      "x": 120,
      "y": 160,
      "width": 210,
      "height": 60,
      "file": "反向传播算法.md"
    },
    {
      "id": "resources",
      "type": "group",
      "x": 950,
      "y": 50,
      "width": 250,
      "height": 180,
      "label": "📚 学习资源",
      "color": "3"
    },
    {
      "id": "course-link",
      "type": "link",
      "x": 970,
      "y": 80,
      "width": 210,
      "height": 60,
      "url": "https://cs231n.github.io/",
      "text": "CS231n课程"
    },
    {
      "id": "book-link",
      "type": "link",
      "x": 970,
      "y": 160,
      "width": 210,
      "height": 60,
      "url": "https://www.deeplearningbook.org/",
      "text": "深度学习教材"
    },
    {
      "id": "applications",
      "type": "group",
      "x": 100,
      "y": 450,
      "width": 800,
      "height": 200,
      "label": "🎯 应用领域",
      "color": "1"
    },
    {
      "id": "cnn",
      "type": "text",
      "x": 150,
      "y": 490,
      "width": 180,
      "height": 100,
      "text": "## 🖼️ CNN\n\n- 图像分类\n- 目标检测\n- 语义分割",
      "color": "1"
    },
    {
      "id": "rnn",
      "type": "text",
      "x": 360,
      "y": 490",
      "width": 180,
      "height": 100,
      "text": "## 📝 RNN\n\n- 序列建模\n- 语言处理\n- 时间序列",
      "color": "1"
    },
    {
      "id": "gan",
      "type": "text",
      "x": 570,
      "y": 490,
      "width": 180,
      "height": 100,
      "text": "## 🎨 GAN\n\n- 图像生成\n- 风格迁移\n- 数据增强",
      "color": "1"
    },
    {
      "id": "transformer",
      "type": "text",
      "x": 780,
      "y": 490,
      "width": 180,
      "height": 100,
      "text": "## ⚡ Transformer\n\n- 注意力机制\n- BERT/GPT\n- 大语言模型",
      "color": "1"
    },
    {
      "id": "visual-diagram",
      "type": "file",
      "x": 950,
      "y": 300,
      "width": 250,
      "height": 200,
      "file": "assets/deep-learning-architecture.png",
      "subpath": ""
    }
  ],
  "edges": [
    {
      "id": "central-to-notes",
      "fromNode": "central-concept",
      "fromSide": "left",
      "toNode": "related-notes",
      "toSide": "right",
      "label": "理论基础",
      "color": "5"
    },
    {
      "id": "central-to-resources",
      "fromNode": "central-concept",
      "fromSide": "right",
      "toNode": "resources",
      "toSide": "left",
      "label": "学习材料",
      "color": "3"
    },
    {
      "id": "central-to-applications",
      "fromNode": "central-concept",
      "fromSide": "bottom",
      "toNode": "applications",
      "toSide": "top",
      "label": "实际应用",
      "color": "1"
    },
    {
      "id": "nn-to-central",
      "fromNode": "nn-basics",
      "fromSide": "right",
      "toNode": "central-concept",
      "toSide": "left",
      "label": "前置知识",
      "toEnd": "arrow",
      "color": "4"
    },
    {
      "id": "central-to-visual",
      "fromNode": "central-concept",
      "fromSide": "right",
      "toNode": "visual-diagram",
      "toSide": "left",
      "label": "架构图",
      "color": "2"
    },
    {
      "id": "app-connections",
      "fromNode": "cnn",
      "fromSide": "right",
      "toNode": "rnn",
      "toSide": "left",
      "label": "并行发展",
      "color": "6"
    },
    {
      "id": "rnn-to-transformer",
      "fromNode": "rnn",
      "fromSide": "right",
      "toNode": "transformer",
      "toSide": "left",
      "label": "演进关系",
      "toEnd": "arrow",
      "color": "2"
    }
  ]
}

Flowchart

{
  "nodes": [
    {
      "id": "a0b1c2d3e4f5a6b7",
      "type": "text",
      "x": 200,
      "y": 0,
      "width": 150,
      "height": 60,
      "text": "**Start**",
      "color": "4"
    },
    {
      "id": "b1c2d3e4f5a6b7c8",
      "type": "text",
      "x": 200,
      "y": 100,
      "width": 150,
      "height": 60,
      "text": "Step 1:\nGather data"
    },
    {
      "id": "c2d3e4f5a6b7c8d9",
      "type": "text",
      "x": 200,
      "y": 200,
      "width": 150,
      "height": 80,
      "text": "**Decision**\n\nIs data valid?",
      "color": "3"
    },
    {
      "id": "d3e4f5a6b7c8d9e0",
      "type": "text",
      "x": 400,
      "y": 200,
      "width": 150,
      "height": 60,
      "text": "Process data"
    },
    {
      "id": "e4f5a6b7c8d9e0f1",
      "type": "text",
      "x": 0,
      "y": 200,
      "width": 150,
      "height": 60,
      "text": "Request new data",
      "color": "1"
    },
    {
      "id": "f5a6b7c8d9e0f1a2",
      "type": "text",
      "x": 400,
      "y": 320,
      "width": 150,
      "height": 60,
      "text": "**End**",
      "color": "4"
    }
  ],
  "edges": [
    {
      "id": "a6b7c8d9e0f1a2b3",
      "fromNode": "a0b1c2d3e4f5a6b7",
      "fromSide": "bottom",
      "toNode": "b1c2d3e4f5a6b7c8",
      "toSide": "top"
    },
    {
      "id": "b7c8d9e0f1a2b3c4",
      "fromNode": "b1c2d3e4f5a6b7c8",
      "fromSide": "bottom",
      "toNode": "c2d3e4f5a6b7c8d9",
      "toSide": "top"
    },
    {
      "id": "c8d9e0f1a2b3c4d5",
      "fromNode": "c2d3e4f5a6b7c8d9",
      "fromSide": "right",
      "toNode": "d3e4f5a6b7c8d9e0",
      "toSide": "left",
      "label": "Yes",
      "color": "4"
    },
    {
      "id": "d9e0f1a2b3c4d5e6",
      "fromNode": "c2d3e4f5a6b7c8d9",
      "fromSide": "left",
      "toNode": "e4f5a6b7c8d9e0f1",
      "toSide": "right",
      "label": "No",
      "color": "1"
    },
    {
      "id": "e0f1a2b3c4d5e6f7",
      "fromNode": "e4f5a6b7c8d9e0f1",
      "fromSide": "top",
      "fromEnd": "none",
      "toNode": "b1c2d3e4f5a6b7c8",
      "toSide": "left",
      "toEnd": "arrow"
    },
    {
      "id": "f1a2b3c4d5e6f7a8",
      "fromNode": "d3e4f5a6b7c8d9e0",
      "fromSide": "bottom",
      "toNode": "f5a6b7c8d9e0f1a2",
      "toSide": "top"
    }
  ]
}

ID Generation

Node and edge IDs must be unique strings. Obsidian generates 16-character hexadecimal IDs:

"id": "6f0ad84f44ce9c17"
"id": "a3b2c1d0e9f8g7h6"
"id": "1234567890abcdef"

This format is a 16-character lowercase hex string (64-bit random value).

🎨 学习笔记Canvas最佳实践

📐 布局设计原则

层次化布局

// 推荐的知识层次布局
{
  "核心概念": { "x": 400, "y": 200, "层级": 0 },
  "基础理论": { "x": 100, "y": 100, "层级": 1 },
  "应用实践": { "x": 700, "y": 100, "层级": 1 },
  "相关资源": { "x": 100, "y": 400, "层级": 2 }
}

节点尺寸规范

间距和对齐

// 标准间距配置
{
  "节点间距": "60-100px",
  "分组内边距": "30-50px",
  "层次间距": "150-200px",
  "网格对齐": "20px倍数"
}

🎯 学习笔记类型模板

🧠 概念关系图

{
  "布局": "中心辐射式",
  "核心": "主要概念",
  "分支": "相关概念、属性、应用",
  "连接": "包含关系、影响关系、对比关系"
}

🛤️ 学习路径图

{
  "布局": "线性流程式",
  "阶段": "基础→进阶→高级→实践",
  "连接": "前置关系、依赖关系",
  "标记": "完成状态、掌握程度"
}

🌳 知识体系图

{
  "布局": "树状层次式",
  "根节点": "学科领域",
  "分支": "子领域、具体方向",
  "叶子": "具体概念、技术点"
}

🔄 思维导图

{
  "布局": "自由发散式",
  "中心": "主题或问题",
  "分支": "想法、关键词、疑问",
  "连接": "关联思路、启发关系"
}

🎨 视觉设计指南

颜色编码系统

{
  "颜色方案": {
    "核心概念": "6 (紫色)",
    "理论基础": "4 (绿色)",
    "实践应用": "1 (红色)",
    "资源链接": "3 (黄色)",
    "疑问问题": "2 (橙色)",
    "已完成": "4 (绿色)",
    "进行中": "3 (黄色)",
    "待开始": "1 (红色)"
  }
}

连接线样式

{
  "连接类型": {
    "包含关系": { "样式": "实线", "箭头": "无", "颜色": "4" },
    "依赖关系": { "样式": "实线", "箭头": "有", "颜色": "6" },
    "影响关系": { "样式": "虚线", "箭头": "有", "颜色": "2" },
    "对比关系": { "样式": "点线", "箭头": "无", "颜色": "3" },
    "时序关系": { "样式": "实线", "箭头": "双向", "颜色": "1" }
  }
}

📝 内容组织技巧

文本内容结构

# 节点标题
## 关键要点
- 要点1：简短描述
- 要点2：核心概念

## 重要公式
$$数学表达式$$

## 代码示例
`关键函数`

## 状态标记
✅ 已掌握  🔄 学习中  ❓ 有疑问

分组命名规范

{
  "分组类型": {
    "阶段分组": "📖 基础阶段 / ⚙️ 核心算法 / 🚀 进阶主题",
    "主题分组": "🧠 概念理论 / 💻 实践应用 / 📚 学习资源",
    "状态分组": "✅ 已完成 / 🔄 进行中 / 📋 待开始",
    "类型分组": "🔧 工具方法 / 📊 数据分析 / 🎯 应用场景"
  }
}

🔄 维护和更新

定期维护任务

{
  "每周检查": [
    "更新学习进度状态",
    "添加新发现的概念关联",
    "修正过时的信息链接"
  ],
  "每月整理": [
    "重新组织混乱的布局",
    "补充缺失的知识节点",
    "优化视觉设计效果"
  ]
}

版本管理建议

{
  "版本控制": {
    "重要节点": "创建备份前修改",
    "结构调整": "记录变更原因",
    "内容更新": "标注修改时间和内容"
  }
}

Validation Rules

1. All id values must be unique across nodes and edges
2. fromNode and toNode must reference existing node IDs
3. Required fields must be present for each node type
4. type must be one of: text, file, link, group
5. backgroundStyle must be one of: cover, ratio, repeat
6. fromSide, toSide must be one of: top, right, bottom, left
7. fromEnd, toEnd must be one of: none, arrow
8. Color presets must be "1" through "6" or valid hex color

References

• JSON Canvas Spec 1.0
• JSON Canvas GitHub