feat: Refactor DpmOverlays to use ShowCard for rendering cards and add ToolStatusToast for operation status notifications

- Moved card rendering logic from DpmOverlays to a new ShowCard component for better reusability.
- Introduced ToolStatusToast to display real-time operation statuses in the top right corner.
- Updated PageHeader to conditionally render credits based on the current path.
- Modified PromptPanel to change tool names and update prompt titles.
- Enhanced ScreenLayout to include ToolStatusToast.
- Updated styles for new components and adjusted existing styles for consistency.
- Implemented statusBus utility for dispatching tool status events.
- Updated useMqttControl to integrate tool status notifications during navigation and card display actions.

backend/scripts/ingest_kb.py

@@ -0,0 +1,63 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""知识库入库脚本：分块 → LLM 生成问答 → 双路向量索引 → 落盘 kb_index.json / kb_qa.json。
+
+知识源 = backend/knowledge/ 下全部 *.md（park.md / opc.md / park_data.md …）。
+- 幂等：缓存有效时直接加载（不重新构建）；--force 强制删除缓存重建。
+- 首次运行会调用 DashScope（text-embedding-v4 向量化 + qwen-plus 生成问答），
+  耗时约数分钟，仅构建一次，之后命中缓存秒开。
+
+用法（在 backend/ 目录下执行）：
+    python scripts/ingest_kb.py            # 有缓存则加载，无缓存则构建
+    python scripts/ingest_kb.py --force    # 强制重建（任一知识文件变更后）
+"""
+import argparse
+import sys
+from pathlib import Path
+
+BACKEND_DIR = Path(__file__).resolve().parent.parent
+if str(BACKEND_DIR) not in sys.path:
+    sys.path.insert(0, str(BACKEND_DIR))
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="知识库入库（分块 + 问答 + 双路向量索引）")
+    parser.add_argument("--force", action="store_true", help="删除缓存并强制重建")
+    parser.add_argument("--dry", action="store_true", help="只做分块预览，不调 API、不落盘")
+    args = parser.parse_args()
+
+    from app import rag
+
+    if args.dry:
+        chunks = rag._chunk_md(rag._kb_text())
+        print(f"[dry] 知识源 {len(list(rag.KB_DIR.glob(rag.KB_GLOB)))} 个文件，全库 "
+              f"{len(rag._kb_text())} 字 → {len(chunks)} 块（最长 {max(map(len, chunks))} 字）")
+        return
+
+    if args.force:
+        for p in (rag.META_FILE, rag.VECTOR_FILE, rag.QA_FILE, rag.LEGACY_INDEX_FILE):
+            if p.exists():
+                p.unlink()
+                print(f"[ingest] 已删除缓存 {p.name}")
+
+    print("[ingest] 知识源：")
+    for f in sorted(rag.KB_DIR.glob(rag.KB_GLOB)):
+        print(f"  - {f.name}  ({f.stat().st_size} B)")
+
+    idx = rag._load_index()
+    n_chunk = len(idx["chunks"])
+    n_vec = len(idx["embed_texts"])
+    dim = idx["embeddings"].shape[1]
+    n_q = sum(1 for qs in idx["questions"] for _ in qs)
+    print("[ingest] 入库完成：")
+    print(f"  - 分块数: {n_chunk}")
+    print(f"  - 生成问句数: {n_q}")
+    print(f"  - 向量数: {n_vec}（dim={dim}，含原句+问句双路）")
+    print(f"  - 向量矩阵: {rag.VECTOR_FILE}")
+    print(f"  - 元数据: {rag.META_FILE}")
+    print(f"  - 问答缓存: {rag.QA_FILE}")
+    print("[ingest] 成功。")
+
+
+if __name__ == "__main__":
+    main()