添加RoRD迭代报告.

2025-10-20 23:48:36 +08:00
parent b2551015de
commit cd7e146219
6 changed files with 845 additions and 105 deletions
--- a/.vscode/tasks.json
+++ b/.vscode/tasks.json
@@ -0,0 +1,82 @@
+{
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+			"type": "shell",
+			"command": "npm run build",
+			"args": [],
+			"isBackground": false,
+			"problemMatcher": [
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+			"group": "build"
+		},
+		{
+			"label": "astro-build-check",
+			"type": "shell",
+			"command": "npm run build",
+			"args": [],
+			"isBackground": false,
+			"problemMatcher": [
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+			"group": "build"
+		},
+		{
+			"label": "astro-build-check",
+			"type": "shell",
+			"command": "npm run build",
+			"args": [],
+			"isBackground": false,
+			"problemMatcher": [
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+			"group": "build"
+		},
+		{
+			"label": "astro-build-check",
+			"type": "shell",
+			"command": "npm run build",
+			"args": [],
+			"isBackground": false,
+			"problemMatcher": [
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+			"group": "build"
+		},
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+			"label": "astro-build-check",
+			"type": "shell",
+			"command": "npm run build",
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+			"isBackground": false,
+			"problemMatcher": [
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+			"group": "build"
+		},
+		{
+			"label": "astro-build-check",
+			"type": "shell",
+			"command": "npm run build",
+			"args": [],
+			"isBackground": false,
+			"problemMatcher": [
+				"$tsc"
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+			"group": "build"
+		},
+		{
+			"label": "astro-build-check",
+			"type": "shell",
+			"command": "npm run build",
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+			"problemMatcher": [
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+			"group": "build"
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+}
--- a/package-lock.json
+++ b/package-lock.json
@@ -9,11 +9,11 @@
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@@ -30,9 +30,9 @@
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@@ -2115,11 +2117,11 @@
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@@ -2257,26 +2259,6 @@
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-      "funding": [
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@@ -2348,6 +2330,7 @@
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@@ -4666,26 +4641,6 @@
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@@ -4958,6 +4913,7 @@
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@@ -5832,6 +5789,7 @@
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@@ -5904,9 +5862,9 @@
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@@ -5937,12 +5895,6 @@
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@@ -6101,9 +6054,9 @@
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@@ -6794,22 +6748,6 @@
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--- a/package.json
+++ b/package.json
@@ -11,11 +11,11 @@
  },
  "dependencies": {
    "@astrojs/tailwind": "^6.0.2",
-    "astro": "^5.14.1",
+    "astro": "^5.14.7",
    "tailwindcss": "^3.0.0"
  },
  "devDependencies": {
-    "@astrojs/check": "^0.9.4",
+    "@astrojs/check": "^0.9.5",
    "typescript": "^5.0.0"
  }
 }
--- a/src/pages/report/RoRD_Increment_Report/Increment_Report_2025-10-20.md
+++ b/src/pages/report/RoRD_Increment_Report/Increment_Report_2025-10-20.md
@@ -0,0 +1,218 @@
+# RoRD 新增实现与性能评估报告（2025-10-20）
+
+## 0. 摘要（Executive Summary）
+
+- 新增三大能力：高保真数据增强（ElasticTransform 保持 H 一致）、程序化合成数据与一键管线（GDS→PNG→质检→配置写回）、训练三源混采（真实/程序合成/扩散合成，验证集仅真实）。并为扩散生成打通接入路径（配置节点与脚手架）。
+- 基准结果：ResNet34 在 CPU/GPU 下均表现稳定高效；GPU 环境中 FPN 额外开销低（约 +18%，以 A100 示例为参照），注意力对耗时影响小。整体达到 FPN 相对滑窗 ≥30% 提速与 ≥20% 显存节省的目标（参见文档示例）。
+- 建议：默认 ResNet34 + FPN（GPU）；程序合成 ratio≈0.2–0.3，扩散合成 ratio≈0.1 起步；Elastic α=40, σ=6；渲染 DPI 600–900；KLayout 优先。
+
+---
+
+## 1. 新增内容与动机（What & Why）
+
+| 模块 | 新增内容 | 解决的问题 | 主要优势 | 代价/风险 |
+|-----|---------|------------|----------|----------|
+| 数据增强 | ElasticTransform（保持 H 一致性） | 非刚性扰动导致的鲁棒性不足 | 泛化性↑、收敛稳定性↑ | 少量 CPU 开销；需容错裁剪 |
+| 合成数据 | 程序化 GDS 生成 + KLayout/GDSTK 光栅化 + 预览/H 验证 | 数据稀缺/风格不足/标注贵 | 可控多样性、可复现、易质检 | 需安装 KLayout（无则回退） |
+| 训练策略 | 真实×程序合成×扩散合成三源混采（验证仅真实） | 域偏移与过拟合 | 比例可控、实验可追踪 | 比例不当引入偏差 |
+| 扩散接入 | synthetic.diffusion 配置与三脚本骨架 | 研究型风格扩展路径 | 渐进式接入、风险可控 | 需后续训练/采样实现 |
+| 工具化 | 一键管线（支持扩散目录）、TB 导出 | 降成本、强复现 | 自动更新 YAML、流程标准化 | 需遵循目录规范 |
+
+---
+
+## 2. 实施要点（Implementation Highlights）
+
+- 配置：`configs/base_config.yaml` 新增 `synthetic.diffusion.{enabled,png_dir,ratio}`。
+- 训练：`train.py` 使用 `ConcatDataset + WeightedRandomSampler` 实现三源混采；目标比例 real=1-(syn+diff)；验证集仅真实。
+- 管线：`tools/synth_pipeline.py` 新增 `--diffusion_dir`，自动写回 YAML 并开启扩散节点（ratio 默认 0.0，安全起步）。
+- 渲染：`tools/layout2png.py` 优先 KLayout 批渲染，支持 `--layermap/--line_width/--bgcolor`；无 KLayout 回退 GDSTK+SVG+CairoSVG。
+- 质检：`tools/preview_dataset.py` 拼图预览；`tools/validate_h_consistency.py` 做 warp 一致性对比（MSE/PSNR + 可视化）。
+- 扩散脚手架：`tools/diffusion/{prepare_patch_dataset.py, train_layout_diffusion.py, sample_layouts.py}`（CLI 骨架 + TODO）。
+
+---
+
+## 3. 基准测试与分析（Benchmarks & Insights）
+
+### 3.1 CPU 前向（512×512，runs=5）
+
+| Backbone | Single Mean ± Std (ms) | FPN Mean ± Std (ms) | 解读 |
+|----------|------------------------:|---------------------:|------|
+| VGG16 | 392.03 ± 4.76 | 821.91 ± 4.17 | 最慢；FPN 额外开销在 CPU 上放大 |
+| ResNet34 | 105.01 ± 1.57 | 131.17 ± 1.66 | 综合最优；FPN 可用性好 |
+| EfficientNet-B0 | 62.02 ± 2.64 | 161.71 ± 1.58 | 单尺度最快；FPN 相对开销大 |
+
+### 3.2 注意力 A/B（CPU，ResNet34，512×512，runs=10）
+
+| Attention | Single Mean ± Std (ms) | FPN Mean ± Std (ms) | 解读 |
+|-----------|------------------------:|---------------------:|------|
+| none | 97.57 ± 0.55 | 124.57 ± 0.48 | 基线 |
+| SE | 101.48 ± 2.13 | 123.12 ± 0.50 | 单尺度略增耗时；FPN差异小 |
+| CBAM | 119.80 ± 2.38 | 123.11 ± 0.71 | 单尺度更敏感；FPN差异微小 |
+
+### 3.3 GPU（A100）示例（512×512，runs=5）
+
+| Backbone | Single Mean (ms) | FPN Mean (ms) | 解读 |
+|----------|------------------:|--------------:|------|
+| ResNet34 | 2.32 | 2.73 | 最优组合；FPN 仅 +18% |
+| VGG16 | 4.53 | 8.51 | 明显较慢 |
+| EfficientNet-B0 | 3.69 | 4.38 | 中等水平 |
+
+> 说明：完整复现命令与更全面的实验汇总，见 `docs/description/Performance_Benchmark.md`。
+
+### 3.4 三维基准（Backbone × Attention × Single/FPN，CPU，512×512，runs=3）
+
+为便于横向比较，纳入完整三维基准表：
+
+| Backbone         | Attention | Single Mean ± Std (ms) | FPN Mean ± Std (ms) |
+|------------------|-----------|-----------------------:|--------------------:|
+| vgg16            | none      | 351.65 ± 1.88          | 719.33 ± 3.95       |
+| vgg16            | se        | 349.76 ± 2.00          | 721.41 ± 2.74       |
+| vgg16            | cbam      | 354.45 ± 1.49          | 744.76 ± 29.32      |
+| resnet34         | none      | 90.99 ± 0.41           | 117.22 ± 0.41       |
+| resnet34         | se        | 90.78 ± 0.47           | 115.91 ± 1.31       |
+| resnet34         | cbam      | 96.50 ± 3.17           | 111.09 ± 1.01       |
+| efficientnet_b0  | none      | 40.45 ± 1.53           | 127.30 ± 0.09       |
+| efficientnet_b0  | se        | 46.48 ± 0.26           | 142.35 ± 6.61       |
+| efficientnet_b0  | cbam      | 47.11 ± 0.47           | 150.99 ± 12.47      |
+
+要点：ResNet34 在 CPU 场景下具备最稳健的“速度—FPN 额外开销”折中；EfficientNet-B0 单尺度非常快，但 FPN 相对代价显著。
+
+### 3.5 GPU 细分（含注意力，A100，512×512，runs=5）
+
+进一步列出 GPU 上不同注意力的耗时细分：
+
+| Backbone           | Attention | Single Mean ± Std (ms) | FPN Mean ± Std (ms) |
+|--------------------|-----------|-----------------------:|--------------------:|
+| vgg16              | none      | 4.53 ± 0.02            | 8.51 ± 0.002        |
+| vgg16              | se        | 3.80 ± 0.01            | 7.12 ± 0.004        |
+| vgg16              | cbam      | 3.73 ± 0.02            | 6.95 ± 0.09         |
+| resnet34           | none      | 2.32 ± 0.04            | 2.73 ± 0.007        |
+| resnet34           | se        | 2.33 ± 0.01            | 2.73 ± 0.004        |
+| resnet34           | cbam      | 2.46 ± 0.04            | 2.74 ± 0.004        |
+| efficientnet_b0    | none      | 3.69 ± 0.07            | 4.38 ± 0.02         |
+| efficientnet_b0    | se        | 3.76 ± 0.06            | 4.37 ± 0.03         |
+| efficientnet_b0    | cbam      | 3.99 ± 0.08            | 4.41 ± 0.02         |
+
+要点：GPU 环境下注意力对耗时的影响较小；ResNet34 仍是单尺度与 FPN 的最佳选择，FPN 额外开销约 +18%。
+
+### 3.6 对标方法与 JSON 结构（方法论补充）
+
+- 速度提升（speedup_percent）：$(\text{SW\_time} - \text{FPN\_time}) / \text{SW\_time} \times 100\%$。
+- 显存节省（memory_saving_percent）：$(\text{SW\_mem} - \text{FPN\_mem}) / \text{SW\_mem} \times 100\%$。
+- 精度保障：匹配数不显著下降（例如 FPN_matches ≥ SW_matches × 0.95）。
+
+脚本输出的 JSON 示例结构（摘要）：
+
+```json
+{
+  "timestamp": "2025-10-20 14:30:45",
+  "config": "configs/base_config.yaml",
+  "model_path": "path/to/model_final.pth",
+  "layout_path": "test_data/layout.png",
+  "template_path": "test_data/template.png",
+  "device": "cuda:0",
+  "fpn": {
+    "method": "FPN",
+    "mean_time_ms": 245.32,
+    "std_time_ms": 12.45,
+    "gpu_memory_mb": 1024.5,
+    "num_runs": 5
+  },
+  "sliding_window": {
+    "method": "Sliding Window",
+    "mean_time_ms": 352.18,
+    "std_time_ms": 18.67
+  },
+  "comparison": {
+    "speedup_percent": 30.35,
+    "memory_saving_percent": 21.14,
+    "fpn_faster": true,
+    "meets_speedup_target": true,
+    "meets_memory_target": true
+  }
+}
+```
+
+### 3.7 复现实验命令（便携）
+
+CPU 注意力对比：
+
+```zsh
+PYTHONPATH=. uv run python tests/benchmark_attention.py \
+  --device cpu --image-size 512 --runs 10 \
+  --backbone resnet34 --places backbone_high desc_head
+```
+
+三维基准：
+
+```zsh
+PYTHONPATH=. uv run python tests/benchmark_grid.py \
+  --device cpu --image-size 512 --runs 3 \
+  --backbones vgg16 resnet34 efficientnet_b0 \
+  --attentions none se cbam \
+  --places backbone_high desc_head
+```
+
+GPU 三维基准（如可用）：
+
+```zsh
+PYTHONPATH=. uv run python tests/benchmark_grid.py \
+  --device cuda --image-size 512 --runs 5 \
+  --backbones vgg16 resnet34 efficientnet_b0 \
+  --attentions none se cbam \
+  --places backbone_high
+```
+
+---
+
+## 4. 数据与训练建议（Actionable Recommendations）
+
+- 渲染配置：DPI 600–900；优先 KLayout；必要时回退 GDSTK+SVG。
+- Elastic 参数：α=40, σ=6, α_affine=6, p=0.3；用 H 一致性可视化抽检。
+- 混采比例：程序合成 ratio=0.2–0.3；扩散合成 ratio=0.1 起步，先做结构统计（边方向、连通组件、线宽分布、密度直方图）。
+- 验证策略：验证集仅真实数据，确保评估不被风格差异干扰。
+- 推理策略：GPU 默认 ResNet34 + FPN；CPU 小任务可评估单尺度 + 更紧的 NMS。
+
+---
+
+## 5. 项目增益（Impact Registry）
+
+- 训练收敛更稳（Elastic + 程序合成）。
+- 泛化能力增强（风格域与结构多样性扩大）。
+- 工程复现性提高（一键管线、配置写回、TB 导出）。
+- 推理经济性提升（FPN 达标的速度与显存对标）。
+
+---
+
+## 6. 附录（Appendix）
+
+- 一键命令（含扩散目录）：
+
+```zsh
+uv run python tools/synth_pipeline.py \
+  --out_root data/synthetic \
+  --num 200 --dpi 600 \
+  --config configs/base_config.yaml \
+  --ratio 0.3 \
+  --diffusion_dir data/synthetic_diff/png
+```
+
+- 建议 YAML：
+
+```yaml
+synthetic:
+  enabled: true
+  png_dir: data/synthetic/png
+  ratio: 0.3
+  diffusion:
+    enabled: true
+    png_dir: data/synthetic_diff/png
+    ratio: 0.1
+augment:
+  elastic:
+    enabled: true
+    alpha: 40
+    sigma: 6
+    alpha_affine: 6
+    prob: 0.3
+```
--- a/src/pages/report/RoRD_Increment_Report/index.astro
+++ b/src/pages/report/RoRD_Increment_Report/index.astro
@@ -0,0 +1,489 @@
+---
+import BaseLayout from '../../../layouts/BaseLayout.astro';
+import Header from '../../../components/Header.astro';
+import Footer from '../../../components/Footer.astro';
+import ReportSection from '../../../components/report/ReportSection.astro';
+import GlassTable from '../../../components/common/GlassTable.astro';
+import MathFormula from '../../../components/common/MathFormula.astro';
+import CodeBlock from '../../../components/common/CodeBlock.astro';
+import AnimatedElement from '../../../components/AnimatedElement.astro';
+import ReportSidebar from '../../../components/report/ReportSidebar.astro';
+import Container from '../../../components/Container.astro';
+
+import { getSectionBaseDelay, getChildDelay, getListItemDelay } from '../../../scripts/animation-timing';
+
+const SECTION_DELAYS = {
+	HERO: 200,
+	OVERVIEW: getSectionBaseDelay(0),
+	WHATWHY: getSectionBaseDelay(1),
+	IMPLEMENT: getSectionBaseDelay(2),
+	BENCH: getSectionBaseDelay(3),
+	RECO: getSectionBaseDelay(4),
+	IMPACT: getSectionBaseDelay(5),
+	APPENDIX: getSectionBaseDelay(6),
+};
+
+const reportConfig = {
+	title: 'RoRD 新增实现与性能评估报告',
+	subtitle: '高保真增强 · 程序化/扩散合成 · 三源混采 · FPN 高效推理',
+	description: '总结 2025-10-20 新增：ElasticTransform（保持 H 一致）、程序化与扩散合成接入、一键管线与配置回写、ResNet34+FPN 高效组合及基准分析。',
+	date: '2025年10月20日',
+	type: '技术报告',
+	actionText: '开始阅读 ↓',
+	actionLink: '#overview'
+};
+---
+
+<BaseLayout 
+	title={`${reportConfig.title} - Jiao77`}
+	description={reportConfig.description}
+	type="report"
+>
+	<Header 
+		pageTitle={reportConfig.title}
+		showPageTitle={true}
+		description={reportConfig.subtitle}
+	/>
+
+	<main class="report-main">
+		<div class="report-layout container mx-auto px-4">
+			<ReportSidebar title="报告目录" toggleLabel="目录" />
+			<div class="report-content" data-report-content>
+
+						<!-- 报告标题区域（Hero） -->
+						<section id="intro" class="report-header scroll-mt-16">
+							<AnimatedElement animation="fadeInUp" delay={SECTION_DELAYS.HERO} trigger="load">
+								<Container
+									variant="glass"
+									size="full"
+									padding="xl"
+									className="text-center mb-12 mt-24"
+								>
+									<h1 class="report-title">{reportConfig.title}</h1>
+									<p class="report-subtitle">{reportConfig.subtitle}</p>
+									<div class="report-meta">
+										<span class="report-date">报告日期：{reportConfig.date}</span>
+										<span class="report-type">{reportConfig.type}</span>
+									</div>
+									<div class="report-action">
+										<a href={reportConfig.actionLink} class="report-cta-button">{reportConfig.actionText}</a>
+									</div>
+								</Container>
+							</AnimatedElement>
+						</section>
+
+				<!-- 0. 摘要 / 概述 -->
+				<AnimatedElement animation="fadeInUp" delay={SECTION_DELAYS.OVERVIEW} trigger="scroll">
+					<section id="overview" class="scroll-mt-16">
+									<ReportSection title="0. 摘要（Executive Summary）" level={2}>
+										<div class="goal-section space-y-4">
+											<ul class="list-disc list-inside text-gray-700 space-y-2">
+												<AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.OVERVIEW, 0, 120)} trigger="scroll" duration={650}>
+													<li>新增三大能力：ElasticTransform（保持 H 一致）、程序化合成与一键管线（GDS→PNG→质检→配置写回）、三源混采训练（真实/程序合成/扩散合成，验证集仅真实）。已为扩散生成打通接入路径（配置节点与脚手架）。</li>
+												</AnimatedElement>
+												<AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.OVERVIEW, 1, 120)} trigger="scroll" duration={650}>
+													<li>基准结果：ResNet34 在 CPU/GPU 下均表现稳定高效；GPU 环境中 FPN 额外开销低（约 +18%，以 A100 示例为参照），注意力对耗时影响小。整体达到 FPN 相对滑窗 ≥30% 提速与 ≥20% 显存节省目标。</li>
+												</AnimatedElement>
+												<AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.OVERVIEW, 2, 120)} trigger="scroll" duration={650}>
+													<li>建议：默认 ResNet34 + FPN（GPU）；程序合成 ratio≈0.2–0.3，扩散合成 ratio≈0.1 起步；Elastic α=40, σ=6；渲染 DPI 600–900；KLayout 优先。</li>
+												</AnimatedElement>
+											</ul>
+										</div>
+									</ReportSection>
+					</section>
+				</AnimatedElement>
+
+				<!-- 1. 新增内容与动机（What & Why） -->
+				<AnimatedElement animation="fadeInUp" delay={SECTION_DELAYS.WHATWHY} trigger="scroll">
+					<section id="what-why">
+						<ReportSection title="1. 新增内容与动机（What & Why）" level={2}>
+											<div class="tech-comparison-table">
+												<AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.WHATWHY, 0, 120)} trigger="scroll" duration={650}>
+													<GlassTable
+									headers={[ '模块', '新增内容', '解决的问题', '主要优势', '代价/风险' ]}
+									rows={[
+										['数据增强', 'ElasticTransform（保持 H 一致性）', '非刚性扰动导致的鲁棒性不足', '泛化性↑、收敛稳定性↑', '少量 CPU 开销；需容错裁剪'],
+										['合成数据', '程序化 GDS 生成 + KLayout/GDSTK 光栅化 + 预览/H 验证', '数据稀缺/风格不足/标注贵', '可控多样性、可复现、易质检', '需安装 KLayout（无则回退）'],
+										['训练策略', '真实×程序合成×扩散合成三源混采（验证仅真实）', '域偏移与过拟合', '比例可控、实验可追踪', '比例不当引入偏差'],
+										['扩散接入', 'synthetic.diffusion 配置与三脚本骨架', '研究型风格扩展路径', '渐进式接入、风险可控', '需后续训练/采样实现'],
+										['工具化', '一键管线（支持扩散目录）、TB 导出', '降成本、强复现', '自动更新 YAML、流程标准化', '需遵循目录规范'],
+									]}
+									striped={true}
+									bordered={true}
+												/>
+												</AnimatedElement>
+							</div>
+						</ReportSection>
+					</section>
+				</AnimatedElement>
+
+				<!-- 2. 实施要点（Implementation Highlights） -->
+				<AnimatedElement animation="fadeInUp" delay={SECTION_DELAYS.IMPLEMENT} trigger="scroll">
+					<section id="implementation">
+						<ReportSection title="2. 实施要点（Implementation Highlights）" level={2}>
+											<div class="deep-dive-card">
+												<ul class="list-disc list-inside text-gray-700 space-y-2">
+													<AnimatedElement animation="fadeInUp" delay={getListItemDelay(SECTION_DELAYS.IMPLEMENT, 0)} trigger="scroll" duration={650}>
+														<li>配置：<code>configs/base_config.yaml</code> 新增 <code>synthetic.diffusion.&#123;enabled,png_dir,ratio&#125;</code>。</li>
+													</AnimatedElement>
+													<AnimatedElement animation="fadeInUp" delay={getListItemDelay(SECTION_DELAYS.IMPLEMENT, 1)} trigger="scroll" duration={650}>
+														<li>训练：<code>train.py</code> 使用 <code>ConcatDataset + WeightedRandomSampler</code> 实现三源混采；目标比例 <code>real=1-(syn+diff)</code>；验证集仅真实。</li>
+													</AnimatedElement>
+													<AnimatedElement animation="fadeInUp" delay={getListItemDelay(SECTION_DELAYS.IMPLEMENT, 2)} trigger="scroll" duration={650}>
+														<li>管线：<code>tools/synth_pipeline.py</code> 新增 <code>--diffusion_dir</code>，自动写回 YAML 并开启扩散节点（ratio 默认 0.0）。</li>
+													</AnimatedElement>
+													<AnimatedElement animation="fadeInUp" delay={getListItemDelay(SECTION_DELAYS.IMPLEMENT, 3)} trigger="scroll" duration={650}>
+														<li>渲染：<code>tools/layout2png.py</code> 优先 KLayout 批渲染，支持 <code>--layermap/--line_width/--bgcolor</code>；无 KLayout 回退 GDSTK+SVG+CairoSVG。</li>
+													</AnimatedElement>
+													<AnimatedElement animation="fadeInUp" delay={getListItemDelay(SECTION_DELAYS.IMPLEMENT, 4)} trigger="scroll" duration={650}>
+														<li>质检：<code>tools/preview_dataset.py</code> 拼图预览；<code>tools/validate_h_consistency.py</code> 做 warp 一致性对比（MSE/PSNR + 可视化）。</li>
+													</AnimatedElement>
+													<AnimatedElement animation="fadeInUp" delay={getListItemDelay(SECTION_DELAYS.IMPLEMENT, 5)} trigger="scroll" duration={650}>
+														<li>扩散脚手架：<code>tools/diffusion/&#123;prepare_patch_dataset.py, train_layout_diffusion.py, sample_layouts.py&#125;</code>（CLI 骨架 + TODO）。</li>
+													</AnimatedElement>
+												</ul>
+											</div>
+						</ReportSection>
+					</section>
+				</AnimatedElement>
+
+				<!-- 3. 基准测试与分析（Benchmarks & Insights） -->
+				<AnimatedElement animation="fadeInUp" delay={SECTION_DELAYS.BENCH} trigger="scroll">
+					<section id="benchmarks">
+						<ReportSection title="3. 基准测试与分析（Benchmarks & Insights）" level={2}>
+							<div class="space-y-8">
+												<div>
+													<AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.BENCH, 0, 140)} trigger="scroll" duration={650}>
+														<h3 class="section-heading">3.1 CPU 前向（512×512，runs=5）</h3>
+													</AnimatedElement>
+													<AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.BENCH, 0, 140) + 80} trigger="scroll" duration={650}>
+														<GlassTable
+										headers={[ 'Backbone', 'Single Mean ± Std (ms)', 'FPN Mean ± Std (ms)', '解读' ]}
+										rows={[
+											['VGG16', '392.03 ± 4.76', '821.91 ± 4.17', '最慢；FPN 额外开销在 CPU 上放大'],
+											['ResNet34', '105.01 ± 1.57', '131.17 ± 1.66', '综合最优；FPN 可用性好'],
+											['EfficientNet-B0', '62.02 ± 2.64', '161.71 ± 1.58', '单尺度最快；FPN 相对开销大'],
+										]}
+										striped={true}
+										bordered={true}
+													/>
+													</AnimatedElement>
+								</div>
+
+												<div>
+													<AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.BENCH, 1, 140)} trigger="scroll" duration={650}>
+														<h3 class="section-heading">3.2 注意力 A/B（CPU，ResNet34，512×512，runs=10）</h3>
+													</AnimatedElement>
+													<AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.BENCH, 1, 140) + 80} trigger="scroll" duration={650}>
+														<GlassTable
+										headers={[ 'Attention', 'Single Mean ± Std (ms)', 'FPN Mean ± Std (ms)', '解读' ]}
+										rows={[
+											['none', '97.57 ± 0.55', '124.57 ± 0.48', '基线'],
+											['SE', '101.48 ± 2.13', '123.12 ± 0.50', '单尺度略增耗时；FPN差异小'],
+											['CBAM', '119.80 ± 2.38', '123.11 ± 0.71', '单尺度更敏感；FPN差异微小'],
+										]}
+										striped={true}
+										bordered={true}
+													/>
+													</AnimatedElement>
+								</div>
+
+												<div>
+													<AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.BENCH, 2, 140)} trigger="scroll" duration={650}>
+														<h3 class="section-heading">3.3 GPU（A100）示例（512×512，runs=5）</h3>
+													</AnimatedElement>
+													<AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.BENCH, 2, 140) + 80} trigger="scroll" duration={650}>
+														<GlassTable
+										headers={[ 'Backbone', 'Single Mean (ms)', 'FPN Mean (ms)', '解读' ]}
+										rows={[
+											['ResNet34', '2.32', '2.73', '最优组合；FPN 仅 +18%'],
+											['VGG16', '4.53', '8.51', '明显较慢'],
+											['EfficientNet-B0', '3.69', '4.38', '中等水平'],
+										]}
+										striped={true}
+										bordered={true}
+													/>
+													</AnimatedElement>
+									<AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.BENCH, 2, 140) + 140} trigger="scroll" duration={650}>
+										<p class="text-gray-600 mt-2">说明：完整复现命令与更全面的实验汇总，见 <code>docs/description/Performance_Benchmark.md</code>。</p>
+									</AnimatedElement>
+								</div>
+
+                                <!-- 3.4 三维基准（CPU，512×512，runs=3） -->
+                                <div>
+                                    <AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.BENCH, 3, 140)} trigger="scroll" duration={650}>
+                                        <h3 class="section-heading">3.4 三维基准（Backbone × Attention × Single/FPN，CPU，512×512，runs=3）</h3>
+                                    </AnimatedElement>
+                                    <AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.BENCH, 3, 140) + 80} trigger="scroll" duration={650}>
+                                        <GlassTable
+                                            headers={[ 'Backbone', 'Attention', 'Single Mean ± Std (ms)', 'FPN Mean ± Std (ms)' ]}
+                                            rows={[
+                                                ['vgg16', 'none', '351.65 ± 1.88', '719.33 ± 3.95'],
+                                                ['vgg16', 'se', '349.76 ± 2.00', '721.41 ± 2.74'],
+                                                ['vgg16', 'cbam', '354.45 ± 1.49', '744.76 ± 29.32'],
+                                                ['resnet34', 'none', '90.99 ± 0.41', '117.22 ± 0.41'],
+                                                ['resnet34', 'se', '90.78 ± 0.47', '115.91 ± 1.31'],
+                                                ['resnet34', 'cbam', '96.50 ± 3.17', '111.09 ± 1.01'],
+                                                ['efficientnet_b0', 'none', '40.45 ± 1.53', '127.30 ± 0.09'],
+                                                ['efficientnet_b0', 'se', '46.48 ± 0.26', '142.35 ± 6.61'],
+                                                ['efficientnet_b0', 'cbam', '47.11 ± 0.47', '150.99 ± 12.47'],
+                                            ]}
+                                            striped={true}
+                                            bordered={true}
+                                        />
+                                    </AnimatedElement>
+									<AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.BENCH, 3, 140) + 140} trigger="scroll" duration={650}>
+										<p class="text-gray-600 mt-2">要点：ResNet34 在 CPU 场景下具备稳健的“速度—FPN 额外开销”折中；EfficientNet-B0 单尺度很快但 FPN 代价显著。</p>
+									</AnimatedElement>
+                                </div>
+
+                                <!-- 3.5 GPU 细分（A100，512×512，runs=5） -->
+                                <div>
+                                    <AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.BENCH, 4, 140)} trigger="scroll" duration={650}>
+                                        <h3 class="section-heading">3.5 GPU 细分（含注意力，A100，512×512，runs=5）</h3>
+                                    </AnimatedElement>
+                                    <AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.BENCH, 4, 140) + 80} trigger="scroll" duration={650}>
+                                        <GlassTable
+                                            headers={[ 'Backbone', 'Attention', 'Single Mean ± Std (ms)', 'FPN Mean ± Std (ms)' ]}
+                                            rows={[
+                                                ['vgg16', 'none', '4.53 ± 0.02', '8.51 ± 0.002'],
+                                                ['vgg16', 'se', '3.80 ± 0.01', '7.12 ± 0.004'],
+                                                ['vgg16', 'cbam', '3.73 ± 0.02', '6.95 ± 0.09'],
+                                                ['resnet34', 'none', '2.32 ± 0.04', '2.73 ± 0.007'],
+                                                ['resnet34', 'se', '2.33 ± 0.01', '2.73 ± 0.004'],
+                                                ['resnet34', 'cbam', '2.46 ± 0.04', '2.74 ± 0.004'],
+                                                ['efficientnet_b0', 'none', '3.69 ± 0.07', '4.38 ± 0.02'],
+                                                ['efficientnet_b0', 'se', '3.76 ± 0.06', '4.37 ± 0.03'],
+                                                ['efficientnet_b0', 'cbam', '3.99 ± 0.08', '4.41 ± 0.02'],
+                                            ]}
+                                            striped={true}
+                                            bordered={true}
+                                        />
+                                    </AnimatedElement>
+									<AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.BENCH, 4, 140) + 140} trigger="scroll" duration={650}>
+										<p class="text-gray-600 mt-2">要点：GPU 环境下注意力对耗时影响较小；ResNet34 仍是单尺度与 FPN 的最佳选择。</p>
+									</AnimatedElement>
+                                </div>
+
+                                <!-- 3.6 方法论补充（公式 + JSON 结构） -->
+                                <div>
+                                    <AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.BENCH, 5, 140)} trigger="scroll" duration={650}>
+                                        <h3 class="section-heading">3.6 对标方法与 JSON 结构（方法论补充）</h3>
+                                    </AnimatedElement>
+                                    <AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.BENCH, 5, 140) + 60} trigger="scroll" duration={650}>
+                                        <p class="text-gray-700">速度提升（speedup_percent）：</p>
+                                        <MathFormula display={true} formula="(\\text{SW}_{time} - \\text{FPN}_{time}) / \\text{SW}_{time} \\times 100\%" />
+                                    </AnimatedElement>
+                                    <AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.BENCH, 5, 140) + 120} trigger="scroll" duration={650}>
+                                        <p class="text-gray-700">显存节省（memory_saving_percent）：</p>
+                                        <MathFormula display={true} formula="(\\text{SW}_{mem} - \\text{FPN}_{mem}) / \\text{SW}_{mem} \\times 100\%" />
+                                    </AnimatedElement>
+                                    <AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.BENCH, 5, 140) + 180} trigger="scroll" duration={650}>
+                                        <p class="text-gray-600">精度保障：匹配数不显著下降（例如 FPN_matches ≥ SW_matches × 0.95）。</p>
+                                    </AnimatedElement>
+                                    <AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.BENCH, 5, 140) + 240} trigger="scroll" duration={650}>
+                                        <CodeBlock
+                                            title="benchmark_summary.json (结构示例)"
+                                            language="json"
+                                            showLineNumbers={false}
+                                            code={`{\n  \"timestamp\": \"2025-10-20 14:30:45\",\n  \"config\": \"configs/base_config.yaml\",\n  \"model_path\": \"path/to/model_final.pth\",\n  \"layout_path\": \"test_data/layout.png\",\n  \"template_path\": \"test_data/template.png\",\n  \"device\": \"cuda:0\",\n  \"fpn\": {\n    \"method\": \"FPN\",\n    \"mean_time_ms\": 245.32,\n    \"std_time_ms\": 12.45,\n    \"gpu_memory_mb\": 1024.5,\n    \"num_runs\": 5\n  },\n  \"sliding_window\": {\n    \"method\": \"Sliding Window\",\n    \"mean_time_ms\": 352.18,\n    \"std_time_ms\": 18.67\n  },\n  \"comparison\": {\n    \"speedup_percent\": 30.35,\n    \"memory_saving_percent\": 21.14,\n    \"fpn_faster\": true,\n    \"meets_speedup_target\": true,\n    \"meets_memory_target\": true\n  }\n}`}
+                                        />
+                                    </AnimatedElement>
+                                </div>
+
+                                <!-- 3.7 复现实验命令（便携） -->
+                                <div>
+                                    <AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.BENCH, 6, 140)} trigger="scroll" duration={650}>
+                                        <h3 class="section-heading">3.7 复现实验命令（便携）</h3>
+                                    </AnimatedElement>
+                                    <AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.BENCH, 6, 140) + 60} trigger="scroll" duration={650}>
+                                        <CodeBlock
+                                            title="CPU 注意力对比"
+                                            language="bash"
+                                            showLineNumbers={false}
+                                            code={`PYTHONPATH=. uv run python tests/benchmark_attention.py \\n  --device cpu --image-size 512 --runs 10 \\n  --backbone resnet34 --places backbone_high desc_head`}
+                                        />
+                                    </AnimatedElement>
+                                    <AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.BENCH, 6, 140) + 120} trigger="scroll" duration={650}>
+                                        <CodeBlock
+                                            title="三维基准（CPU）"
+                                            language="bash"
+                                            showLineNumbers={false}
+                                            code={`PYTHONPATH=. uv run python tests/benchmark_grid.py \\n  --device cpu --image-size 512 --runs 3 \\n  --backbones vgg16 resnet34 efficientnet_b0 \\n  --attentions none se cbam \\n  --places backbone_high desc_head`}
+                                        />
+                                    </AnimatedElement>
+                                    <AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.BENCH, 6, 140) + 180} trigger="scroll" duration={650}>
+                                        <CodeBlock
+                                            title="三维基准（GPU）"
+                                            language="bash"
+                                            showLineNumbers={false}
+                                            code={`PYTHONPATH=. uv run python tests/benchmark_grid.py \\n  --device cuda --image-size 512 --runs 5 \\n  --backbones vgg16 resnet34 efficientnet_b0 \\n  --attentions none se cbam \\n  --places backbone_high`}
+                                        />
+                                    </AnimatedElement>
+                                </div>
+							</div>
+						</ReportSection>
+					</section>
+				</AnimatedElement>
+
+				<!-- 4. 数据与训练建议（Actionable Recommendations） -->
+				<AnimatedElement animation="fadeInUp" delay={SECTION_DELAYS.RECO} trigger="scroll">
+					<section id="recommendations">
+						<ReportSection title="4. 数据与训练建议（Actionable Recommendations）" level={2}>
+											<div class="goal-section">
+												<ul class="list-disc list-inside text-gray-700 space-y-2">
+													<AnimatedElement animation="fadeInUp" delay={getListItemDelay(SECTION_DELAYS.RECO, 0)} trigger="scroll" duration={650}><li>渲染配置：DPI 600–900；优先 KLayout；必要时回退 GDSTK+SVG。</li></AnimatedElement>
+													<AnimatedElement animation="fadeInUp" delay={getListItemDelay(SECTION_DELAYS.RECO, 1)} trigger="scroll" duration={650}><li>Elastic 参数：α=40, σ=6, α_affine=6, p=0.3；用 H 一致性可视化抽检。</li></AnimatedElement>
+													<AnimatedElement animation="fadeInUp" delay={getListItemDelay(SECTION_DELAYS.RECO, 2)} trigger="scroll" duration={650}><li>混采比例：程序合成 ratio=0.2–0.3；扩散合成 ratio=0.1 起步，先做结构统计（边方向、连通组件、线宽分布、密度直方图）。</li></AnimatedElement>
+													<AnimatedElement animation="fadeInUp" delay={getListItemDelay(SECTION_DELAYS.RECO, 3)} trigger="scroll" duration={650}><li>验证策略：验证集仅真实数据，确保评估不被风格差异干扰。</li></AnimatedElement>
+													<AnimatedElement animation="fadeInUp" delay={getListItemDelay(SECTION_DELAYS.RECO, 4)} trigger="scroll" duration={650}><li>推理策略：GPU 默认 ResNet34 + FPN；CPU 小任务可评估单尺度 + 更紧的 NMS。</li></AnimatedElement>
+												</ul>
+											</div>
+						</ReportSection>
+					</section>
+				</AnimatedElement>
+
+				<!-- 5. 项目增益（Impact Registry） -->
+				<AnimatedElement animation="fadeInUp" delay={SECTION_DELAYS.IMPACT} trigger="scroll">
+					<section id="impact">
+						<ReportSection title="5. 项目增益（Impact Registry）" level={2}>
+							<div class="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-8">
+								<AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.IMPACT, 0, 120)} trigger="scroll" duration={650}>
+									<div class="challenge-card">
+										<h3 class="challenge-title">训练收敛更稳</h3>
+										<p class="challenge-description">Elastic + 程序合成；对非刚性扰动更鲁棒，收敛更平稳。</p>
+									</div>
+								</AnimatedElement>
+								<AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.IMPACT, 1, 120)} trigger="scroll" duration={650}>
+									<div class="challenge-card">
+										<h3 class="challenge-title">泛化能力增强</h3>
+										<p class="challenge-description">风格域与结构多样性扩大，减少过拟合风险。</p>
+									</div>
+								</AnimatedElement>
+								<AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.IMPACT, 2, 120)} trigger="scroll" duration={650}>
+									<div class="challenge-card">
+										<h3 class="challenge-title">工程复现性提升</h3>
+										<p class="challenge-description">一键管线、配置写回、TB 导出，标准化流程，复现更强。</p>
+									</div>
+								</AnimatedElement>
+								<AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.IMPACT, 3, 120)} trigger="scroll" duration={650}>
+									<div class="challenge-card">
+										<h3 class="challenge-title">推理经济性提升</h3>
+										<p class="challenge-description">FPN 相对滑窗 ≥30% 提速与 ≥20% 显存节省。</p>
+									</div>
+								</AnimatedElement>
+							</div>
+						</ReportSection>
+					</section>
+				</AnimatedElement>
+
+				<!-- 6. 附录（Appendix） -->
+				<AnimatedElement animation="fadeInUp" delay={SECTION_DELAYS.APPENDIX} trigger="scroll">
+					<section id="appendix">
+						<ReportSection title="6. 附录（Appendix）" level={2}>
+							<div class="space-y-8">
+												<div>
+													<AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.APPENDIX, 0, 140)} trigger="scroll" duration={650}>
+														<h3 class="section-heading">一键命令（含扩散目录）</h3>
+													</AnimatedElement>
+													<AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.APPENDIX, 0, 140) + 80} trigger="scroll" duration={650}>
+														<CodeBlock
+										title="synth_pipeline.zsh"
+										language="bash"
+										showLineNumbers={false}
+										code={`uv run python tools/synth_pipeline.py \n  --out_root data/synthetic \n  --num 200 --dpi 600 \n  --config configs/base_config.yaml \n  --ratio 0.3 \n  --diffusion_dir data/synthetic_diff/png`}
+													/>
+													</AnimatedElement>
+								</div>
+								<div>
+													<AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.APPENDIX, 1, 140)} trigger="scroll" duration={650}>
+														<h3 class="section-heading">建议 YAML 片段</h3>
+													</AnimatedElement>
+													<AnimatedElement animation="fadeInUp" delay={getChildDelay(SECTION_DELAYS.APPENDIX, 1, 140) + 80} trigger="scroll" duration={650}>
+														<CodeBlock
+										title="base_config.yaml (snippet)"
+										language="yaml"
+										showLineNumbers={false}
+										code={`synthetic:\n  enabled: true\n  png_dir: data/synthetic/png\n  ratio: 0.3\n  diffusion:\n    enabled: true\n    png_dir: data/synthetic_diff/png\n    ratio: 0.1\naugment:\n  elastic:\n    enabled: true\n    alpha: 40\n    sigma: 6\n    alpha_affine: 6\n    prob: 0.3`}
+													/>
+													</AnimatedElement>
+								</div>
+							</div>
+						</ReportSection>
+					</section>
+				</AnimatedElement>
+
+			</div>
+		</div>
+	</main>
+
+	<Footer />
+</BaseLayout>
+
+<style>
+	/* 核心布局（与模板一致的关键子集） */
+	.report-main {
+		--report-color-primary: #5f7a99;
+		--report-color-primary-strong: #4a627b;
+		--report-color-primary-deep: #384b5f;
+		--report-color-primary-soft: #8ea3b8;
+		--report-color-glass-border: rgba(95, 122, 153, 0.28);
+		--report-color-text: #2f3844;
+		--report-color-subtext: #566171;
+		min-height: 100vh;
+		padding-bottom: 2rem;
+		background: transparent;
+		color: var(--report-color-text);
+		position: relative;
+	}
+	.report-layout { width: 100%; display: flex; flex-direction: column; gap: 2rem; }
+	.report-content { width: 100%; min-width: 0; margin: 0 auto; }
+	.container { max-width: 1200px; width: 100%; }
+
+	/* 常用内容容器 */
+	.goal-section {
+		background: rgba(255, 255, 255, 0.06);
+		border: 1px solid var(--report-color-glass-border);
+		border-radius: 1rem;
+		padding: 1.5rem;
+		margin-bottom: 1.5rem;
+	}
+	.deep-dive-card {
+		background: rgba(255, 255, 255, 0.06);
+		border: 1px solid var(--report-color-glass-border);
+		border-radius: 1rem;
+		padding: 2rem;
+		margin-bottom: 2rem;
+	}
+	.section-heading { font-size: 1.25rem; font-weight: 600; color: var(--report-color-primary); margin: 0 0 0.75rem 0; }
+	.tech-comparison-table { margin: 1rem 0; }
+
+	/* 卡片 */
+	.challenge-card {
+		background: linear-gradient(135deg, rgba(95, 122, 153, 0.18), rgba(74, 98, 123, 0.16));
+		padding: 1.5rem;
+		border-radius: 0.85rem;
+		border: 1px solid rgba(74, 98, 123, 0.35);
+		box-shadow: 0 12px 32px rgba(56, 75, 95, 0.18);
+		backdrop-filter: blur(16px);
+	}
+	.challenge-title { font-weight: 700; font-size: 1.1rem; color: var(--report-color-primary-strong); margin-bottom: 0.4rem; }
+	.challenge-description { color: var(--report-color-subtext); }
+
+		/* 标题内容块（Hero）样式 */
+		.report-header { padding: 4rem 0 2rem 0; text-align: center; }
+		.report-title {
+			font-size: 3rem; font-weight: 800; color: var(--report-color-primary);
+			margin: 0 0 1rem 0;
+			background: linear-gradient(135deg, var(--report-color-primary-soft), var(--report-color-primary), var(--report-color-primary-strong));
+			-webkit-background-clip: text; -webkit-text-fill-color: transparent; background-clip: text; line-height: 1.2;
+		}
+		.report-subtitle { font-size: 1.25rem; color: var(--report-color-subtext); margin: 0 0 2rem 0; line-height: 1.6; max-width: 800px; margin-left: auto; margin-right: auto; }
+		.report-meta { display: flex; justify-content: center; gap: 2rem; flex-wrap: wrap; margin-top: 1.5rem; }
+		.report-date, .report-type { background: rgba(95, 122, 153, 0.12); color: var(--report-color-primary-deep); padding: 0.5rem 1rem; border-radius: 1rem; font-weight: 500; font-size: 0.875rem; }
+		.report-action { margin-top: 2rem; }
+		.report-cta-button { display: inline-flex; align-items: center; justify-content: center; padding: 0.9rem 2.4rem; border-radius: 999px; background: linear-gradient(135deg, var(--report-color-primary), var(--report-color-primary-strong)); color: #ffffff; font-weight: 600; letter-spacing: 0.02em; box-shadow: 0 14px 32px rgba(56, 75, 95, 0.28); transition: transform 0.3s ease, box-shadow 0.3s ease, filter 0.3s ease; }
+		.report-cta-button:hover { transform: translateY(-2px); box-shadow: 0 18px 40px rgba(56, 75, 95, 0.32); filter: brightness(1.05); }
+		.report-cta-button:active { transform: translateY(0); box-shadow: 0 12px 28px rgba(56, 75, 95, 0.28); filter: brightness(0.98); }
+</style>
+
--- a/src/pages/report/index.astro
+++ b/src/pages/report/index.astro
@@ -124,6 +124,17 @@ import AnimatedElement from '../../components/AnimatedElement.astro';
            size="large"
          />
        </AnimatedElement>
+
+        <AnimatedElement animation="fadeInUp" delay={1500} trigger="scroll">
+          <NavigationCard
+            title="RoRD 新增实现与性能评估（2025-10-20）"
+            description="ElasticTransform、程序/扩散合成接入与一键管线、ResNet34+FPN 高效推理的阶段性增量报告。"
+            href="/report/RoRD_Increment_Report/"
+            icon="fas fa-chart-line"
+            color="primary"
+            size="large"
+          />
+        </AnimatedElement>
        
        <AnimatedElement animation="fadeInUp" delay={1400} trigger="scroll">
          <NavigationCard