From c19a917122bbf19c68596da91f8f57a29785b666 Mon Sep 17 00:00:00 2001
From: yueliuli <1628111725@qq.com>
Date: Fri, 17 Apr 2026 11:19:56 +0800
Subject: [PATCH] new4

---
 .gitignore                              |  59 ++
 Qwen_app.py                             | 301 ++++++++++
 Qwen_cmd test.py                        |  65 ++
 README YOLO.md                          |  25 +
 app_test.py                             |  10 +
 lib/class_list/1.厂房防火.json      |   9 +
 lib/class_list/2.电器线路.json      |   3 +
 lib/class_list/text.json                |   1 +
 lib/get_prompt.py                       |  53 ++
 lib/json_fun.py                         |  47 ++
 lib/qwen_fun copy.py                    | 638 ++++++++++++++++++++
 lib/qwen_fun.py                         | 760 ++++++++++++++++++++++++
 lib/qwen_fun_vid.py                     | 491 +++++++++++++++
 lib/sam3 copy.py                        | 303 ++++++++++
 lib/sam3.py                             | 291 +++++++++
 openh264-1.8.0-win64.dll                | Bin 0 -> 825160 bytes
 prompt/7_检测提示词_视频_new3.md |  87 +++
 readme.md                               |   3 +
 test_optimization.py                    |  32 +
 千问API数据.txt                     |  27 +
 知识库/rule.json                     | 695 ++++++++++++++++++++++
 21 files changed, 3900 insertions(+)
 create mode 100644 .gitignore
 create mode 100644 Qwen_app.py
 create mode 100644 Qwen_cmd test.py
 create mode 100644 README YOLO.md
 create mode 100644 app_test.py
 create mode 100644 lib/class_list/1.厂房防火.json
 create mode 100644 lib/class_list/2.电器线路.json
 create mode 100644 lib/class_list/text.json
 create mode 100644 lib/get_prompt.py
 create mode 100644 lib/json_fun.py
 create mode 100644 lib/qwen_fun copy.py
 create mode 100644 lib/qwen_fun.py
 create mode 100644 lib/qwen_fun_vid.py
 create mode 100644 lib/sam3 copy.py
 create mode 100644 lib/sam3.py
 create mode 100644 openh264-1.8.0-win64.dll
 create mode 100644 prompt/7_检测提示词_视频_new3.md
 create mode 100644 readme.md
 create mode 100644 test_optimization.py
 create mode 100644 千问API数据.txt
 create mode 100644 知识库/rule.json

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..163f57d
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,59 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual Environment
+venv/
+env/
+ENV/
+.venv
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# Output (运行生成的结果)
+output/
+results/
+logs/
+*.log
+runs/
+
+# Model files (大文件模型)
+*.pth
+*.pt
+*.onnx
+*.h5
+models/
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Temporary files
+*.tmp
+temp/
+
+# link
+input
+model
diff --git a/Qwen_app.py b/Qwen_app.py
new file mode 100644
index 0000000..e7132a5
--- /dev/null
+++ b/Qwen_app.py
@@ -0,0 +1,301 @@
+from lib.qwen_fun import get_annnotated_frame_for_ai_without_xyxy, hazard_inspection, merge_conflict_inspection_data, report_generator, search_knowledge_base
+from encodings.punycode import T
+"""
+测试 在给定标注框与类别，原始视频经过转换之后，AI能否准确识别物体特征
+"""
+
+from tkinter import N
+from datetime import datetime
+import json
+import os
+from lib.qwen_fun import save_json_to_file, upload_files_and_get_urls_concurrently
+from lib.sam3 import SAM3
+import cv2
+import json
+import numpy as np
+from pathlib import Path
+import gradio as gr
+
+fps = 0
+<<<<<<< HEAD
+VIDEO_FOLDER: str = "input"
+
+def load_file_list() -> list[str]:
+    """
+    加载指定文件夹下的所有文件名称（不含子目录）
+    返回：文件名列表
+    """
+    file_names: list[str] = []
+
+    if not os.path.isdir(VIDEO_FOLDER):
+        return file_names
+
+    for item in os.listdir(VIDEO_FOLDER):
+        item_path: str = os.path.join(VIDEO_FOLDER, item)
+        if os.path.isfile(item_path):
+            file_names.append(item)
+
+    return file_names
+
+def get_full_vid_path(vid_file: str) -> str:
+    """
+    获取视频绝对路径
+    """
+    full_path: str = os.path.join(os.getcwd(), VIDEO_FOLDER, vid_file)
+    gr.Info(full_path)
+    return full_path
+
+def update_preview(frame_idx: int, vid_file: str):
+    vid_name: str = Path(vid_file).stem
+=======
+
+def update_preview(frame_idx: int, vid_name: str):
+>>>>>>> 7562de4 (2 预览图还有点问题)
+    output_dir: str = f"output/{vid_name}"
+    global fps
+    idx = int(frame_idx // fps)
+    img_path = f"{output_dir}/boxes/frame_{idx:04d}.jpg"
+
+    with open(f"{output_dir}/hazard_inspection.json", "r", encoding="utf-8") as f:
+        result = f.read()
+
+    dic = json.loads(result)
+    class_tag = get_class_tag_by_frame(dic, frame_idx, fps)
+
+    return img_path, class_tag
+
+def run(vid_file: str, run_sam3: bool = True, run_inspection: bool = True, gen_report: bool = True,):
+    #================初始化=================
+    vid_name: str = Path(vid_file).stem
+    vid_end: str = Path(vid_file).suffix
+    vid_path: str = f"./input/{vid_name}{vid_end}"
+
+    cap = cv2.VideoCapture(vid_path)
+    global fps
+    fps = int(cap.get(cv2.CAP_PROP_FPS)) # 获取视频FPS
+    cap.release()
+
+    output_dir: str = f"output/{vid_name}"
+    # interval = int(fps / 5)
+    interval = fps
+    conf = 0.7
+    time_data: dict = {}
+
+    annotated_frames: SAM3 = SAM3()
+    result: dict = {}
+
+    if output_dir:
+        os.makedirs(output_dir, exist_ok=True)  # exist_ok=True 防止重复创建报错
+
+
+
+    #================获取物体信息=================
+    # 保存开始时间字符串
+    time_data["start_time"] = str(datetime.now())
+    with open(f"{output_dir}/time.json", "w") as f:
+        f.write(json.dumps(time_data, ensure_ascii=False, indent=4))
+
+    if run_sam3:
+        # 针对厂房防火分区
+        annotated_frames.run(vid_path, output_dir, "lib/class_list/1.厂房防火.json", interval, conf)
+    else:
+        annotated_frames.load_from_json(f"{output_dir}/frame_all.json")
+        # print(annotated_frames.data)
+
+    # 提取ai能看到的部分
+    ai_frames: dict = get_annnotated_frame_for_ai_without_xyxy(annotated_frames.data(), 1, conf)
+    save_json_to_file(ai_frames, f"{output_dir}/frame_all_ai.json")
+
+    #================隐患检查=================
+
+    if run_inspection:
+        if vid_path.startswith("oss"):
+            video_url = vid_path
+        else:
+            video_url: str|None = None
+            video_url_file = f"{output_dir}/video_url.json"
+
+            # 检查URL是否已存在
+            if os.path.exists(video_url_file):
+                try:
+                    with open(video_url_file, "r", encoding="utf-8") as f:
+                        url_data = json.load(f)
+                    if vid_name in url_data:
+                        video_url = url_data[vid_name]
+                        print(f"使用已存在的URL: {video_url}")
+                except Exception as e:
+                    print(f"读取URL文件失败: {e}")
+
+            # 如果URL不存在，上传文件
+            if video_url is None:
+                print(f"上传视频文件: {vid_path}")
+                video_url = upload_files_and_get_urls_concurrently(
+                    file_path_list=[vid_path],
+                    max_workers=8
+                )[0]
+                if video_url:
+                    # 保存为JSON格式，包含文件名和URL的键值对
+                    url_data = {}
+                    if os.path.exists(video_url_file):
+                        try:
+                            with open(video_url_file, "r", encoding="utf-8") as f:
+                                url_data = json.load(f)
+                        except:
+                            pass
+
+                    url_data[vid_name] = video_url
+                    with open(video_url_file, "w", encoding="utf-8") as f:
+                        json.dump(url_data, f, ensure_ascii=False, indent=4)
+                    print(f"URL已保存: {video_url}")
+
+        if video_url is None:
+            raise ValueError("视频上传失败，无法获取 URL")
+
+<<<<<<< HEAD
+        result_test: str
+        reason_test: str
+        reason_test, result_test = hazard_inspection(ai_frames, video_url, enable_thinking=True, fps=2)
+        result = json.loads(result_test)
+=======
+        result = json.loads(hazard_inspection(ai_frames, video_url, enable_thinking=False, fps=2)[1])
+>>>>>>> 7562de4 (2 预览图还有点问题)
+        # merged_result = merge_conflict_inspection_data(result)
+
+        result["class"] = ai_frames["class_list"]
+
+        with open(f"{output_dir}/hazard_inspection.json", "w", encoding="utf-8") as f:
+            f.write(json.dumps(result, ensure_ascii=False, indent=4))
+        with open(f"{output_dir}/hazard_inspection_reason.json", "w", encoding="utf-8") as f:
+            f.write(json.dumps(reason_test, ensure_ascii=False, indent=4))
+
+        # with open(f"{output_dir}/hazard_inspection_merged.json", "w", encoding="utf-8") as f:
+        #     f.write(json.dumps(merged_result, ensure_ascii=False, indent=4))
+
+    #================生成报告=================
+    if gen_report:
+        if result == {}:
+            with open(f"{output_dir}/hazard_inspection.json", "r", encoding="utf-8") as f:
+                result = json.load(f)
+
+        with open(f"知识库/rule.json", "r", encoding="utf-8") as f:
+            rule_definitions = json.load(f)
+
+        report_generator(
+            video_path=vid_path,        # 视频文件路径
+            detection_data=annotated_frames.data(),      # 物体检测数据
+            hazard_results=result,    # 隐患检查结果
+            rule_definitions=rule_definitions,  # 规则定义
+            output_path=output_dir,              # 输出文件夹
+            frame_interval=interval,                 # 帧间隔（可根据实际视频帧率调整）
+        )
+
+        # 保存结束时间字符串
+        time_data["end_time"] = str(datetime.now())
+        with open(f"{output_dir}/time.json", "w") as f:
+            f.write(json.dumps(time_data, ensure_ascii=False, indent=4))
+
+    #================更新预览=================
+
+    # 获取总帧数
+    cap = cv2.VideoCapture(vid_path)
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    cap.release()
+    img_path, class_tag = update_preview(0, vid_name)
+    return gr.update(maximum=total_frames-1, value=0, step = interval), img_path, class_tag
+
+
+# 创建 Gradio 页面
+with gr.Blocks() as demo:
+    gr.Markdown("# 📸 隐患排查系统 (sam3 + qwen3.5-27b)")
+
+    with gr.Row():
+        with gr.Column():
+<<<<<<< HEAD
+            initial_files: list[str] = load_file_list()
+            default_video = initial_files[0] if initial_files else None
+            vid_file = gr.Dropdown(
+                label="视频",
+                choices=initial_files,
+                value=default_video,  # 默认选中第一个
+                interactive=True,
+            )
+=======
+            vid_name = gr.Textbox(label="视频名称", value="Miehhuoxqih")
+            vid_end = gr.Textbox(label="视频后缀", value=".AVI")
+>>>>>>> 7562de4 (2 预览图还有点问题)
+
+            run_sam3 = gr.Checkbox(label="1. 运行 SAM3 模型", value=True)
+            run_inspection = gr.Checkbox(label="2. 运行隐患排查", value=True)
+            gen_report = gr.Checkbox(label="3. 生成报告", value=True)
+            audio_recognition = gr.Checkbox(label="4. 运行音频识别", value=False)
+
+            run_button = gr.Button("运行", variant="primary")
+
+            get_vid_path_btn = gr.Button("获取视频路径")
+            full_path_text = gr.Textbox(visible=False)
+
+        with gr.Column():
+            preview = gr.Image(label="预览", scale=2)
+            img_slider = gr.Slider(label="帧索引", minimum=0, maximum=0, value=0, step=1)
+            textbox = gr.Textbox(label="隐患结果", lines=10)
+
+
+
+    run_button.click(fn=run, inputs=[vid_file, run_sam3, run_inspection, gen_report], outputs=[img_slider, preview, textbox])
+    img_slider.change(fn=update_preview, inputs=[img_slider, vid_file], outputs=[preview, textbox], show_progress="hidden")
+    get_vid_path_btn.click(fn=get_full_vid_path, inputs=vid_file, outputs=full_path_text)
+
+
+def get_class_tag_by_frame(data, idx, fps):
+    """
+    根据给定的帧索引 (idx)，返回在该帧范围内的所有对象的 class:tag 信息。
+
+    参数:
+    data (dict): 包含 'class', 'tag', 'objects' 的字典数据
+    idx (int): 需要查询的帧索引
+
+    返回:
+    str: 符合条件的 class:tag 列表，多个结果之间用换行符分隔。如果没有匹配项，返回空字符串。
+    """
+    # 参数检查
+    if not isinstance(data, dict):
+        raise ValueError("数据必须是字典类型")
+    if 'class' not in data or 'tag' not in data or 'objects' not in data:
+        raise ValueError("数据必须包含 'class', 'tag' 和 'objects' 键")
+
+    class_list = data['class']
+    tag_list = data['tag']
+    objects = data['objects']
+
+    interval = fps
+    idx = int(idx / interval) #转换
+
+    # 用于存储符合条件的 class:tag 字符串
+    result = []
+    all_class_tag = []
+
+    # 遍历每个物体
+    for obj in objects:
+        # 根据 class_id 和 tag_id 获取对应的字符串
+        class_str = class_list[obj['class_id']]
+        tag_str = tag_list[obj['tag_id']]
+        location = obj.get('location', '')
+        # 检查帧范围是否包含 idx
+        if obj['start_frame'] == idx:
+            result.append(f"{class_str}:{tag_str} (位置: {location})")
+        all_class_tag.append(f"{class_str}:{tag_str} (开始帧: {obj['start_frame']*interval}, 位置: {location})")
+
+    # 使用换行符连接所有结果
+    output = f"当前帧隐患：\n"+"\n".join(result)+"\n\n"+"所有对象的 class:tag 信息：\n"+"\n".join(all_class_tag)
+    return output
+
+
+# 启动应用
+if __name__ == "__main__":
+    demo.launch(
+        debug=True,
+        allowed_paths=[VIDEO_FOLDER]
+    )
+
+
+
diff --git a/Qwen_cmd test.py b/Qwen_cmd test.py
new file mode 100644
index 0000000..f952b99
--- /dev/null
+++ b/Qwen_cmd test.py	
@@ -0,0 +1,65 @@
+from lib.qwen_fun import hazard_inspection, search_knowledge_base
+from encodings.punycode import T
+"""
+测试 在给定标注框与类别，原始视频经过转换之后，AI能否准确识别物体特征
+"""
+
+from tkinter import N
+from datetime import datetime
+import json
+import os
+from lib.qwen_fun import chat, get_annnotated_frame_for_ai, get_unique_track_id_count, save_json_to_file, search_knowledge_base, upload_files_and_get_urls_concurrently
+from lib.qwen_fun_vid import create_mian_vid_for_ai, frame_all_to_obj_vid
+from lib.sam3 import SAM3
+from ultralytics.models.sam import SAM3VideoSemanticPredictor
+import cv2
+import json
+import numpy as np
+from pathlib import Path
+
+def run(output_dir, vid_path, interval: int = 5):
+    if output_dir:
+        os.makedirs(output_dir, exist_ok=True)  # exist_ok=True 防止重复创建报错
+
+    annotated_frames: SAM3 = SAM3()
+    annotated_frames.run(vid_path, output_dir, "lib/class_list/xiaofangz.json")
+    # annotated_frames.load_from_json(f"{output_dir}/frame_all.json")
+    # print(annotated_frames.data)
+
+    # 提取ai能看到的部分
+    ai_frames: dict = get_annnotated_frame_for_ai(annotated_frames.data(), interval)
+    save_json_to_file(ai_frames, f"{output_dir}/frame_all_ai.json")
+
+
+    if vid_path.startswith("oss"):
+        video_url = vid_path
+    else:
+        video_url: str|None = upload_files_and_get_urls_concurrently(
+            file_path_list=[vid_path],
+            max_workers=8
+        )[0]
+        if video_url:
+            with open(f"{output_dir}/video_url.json", "w") as f:
+                f.write(video_url)
+
+    if video_url is None:
+        raise ValueError("视频上传失败，无法获取 URL")
+
+    result = hazard_inspection(ai_frames, video_url)[1]
+
+    with open(f"{output_dir}/hazard_inspection.json", "w", encoding="utf-8") as f:
+        f.write(result)
+
+# 启动应用
+if __name__ == "__main__":
+    VID_NAME: str = "Peihdianhxiang"
+    VID_END: str = ".mp4"
+    interval: int = int(30 / 5)
+
+    output_dir: str = f"output/{VID_NAME}"
+    vid_path: str = f"./input/{VID_NAME}{VID_END}"
+
+    run(output_dir, vid_path, interval)
+
+
+
diff --git a/README YOLO.md b/README YOLO.md
new file mode 100644
index 0000000..42b27dc
--- /dev/null
+++ b/README YOLO.md	
@@ -0,0 +1,25 @@
+**获取miniconda安装包**
+Invoke-WebRequest -Uri "https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe" -OutFile ".\Miniconda3-latest-Windows-x86_64.exe"
+
+**激活conda仓库**
+conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/main
+conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/r
+conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/msys2
+
+**在目录下进入conda环境**
+%WINDIR%\System32\cmd.exe "/K" D:\ProgramData\miniconda3\Scripts\activate.bat D:\ProgramData\miniconda3
+
+**创建虚拟环境**
+conda create -n yolo python=3.13 -y
+**激活**
+conda activate yolo
+**取消激活**
+conda deactivate
+
+**conda中执行**
+pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
+pip install -U ultralytics
+pip install gradio
+
+**使用yolo虚拟环境的python解释器**
+D:\ProgramData\miniconda3\envs\yolo\python.exe
\ No newline at end of file
diff --git a/app_test.py b/app_test.py
new file mode 100644
index 0000000..73c56da
--- /dev/null
+++ b/app_test.py
@@ -0,0 +1,10 @@
+from pathlib import Path
+
+import gradio as gr
+
+gr.set_static_paths(paths=[Path.cwd().absolute()/"input"])
+
+with gr.Blocks() as demo:
+    gr.HTML("<video src='/gradio_api/file=input/Miehhuoxqih.AVI'>")
+
+demo.launch()
\ No newline at end of file
diff --git a/lib/class_list/1.厂房防火.json b/lib/class_list/1.厂房防火.json
new file mode 100644
index 0000000..8a0e9f4
--- /dev/null
+++ b/lib/class_list/1.厂房防火.json
@@ -0,0 +1,9 @@
+{
+    "Fire Extinguisher": "灭火器",
+    "Fire Hydrant": "消防栓",
+    "Safety Exit": "安全出口",
+    "Emergency Lighting": "应急照明灯",
+    "Evacuation Sign": "疏散指示标志",
+    "Obstructions Blocking Fire Lane": "堵塞通道的货物/电瓶车/杂物",
+    "Obstructions Blocking Safety Exit": "封堵安全出口的障碍物"
+}
\ No newline at end of file
diff --git a/lib/class_list/2.电器线路.json b/lib/class_list/2.电器线路.json
new file mode 100644
index 0000000..ab6b57c
--- /dev/null
+++ b/lib/class_list/2.电器线路.json
@@ -0,0 +1,3 @@
+{
+    "Distribution Box": "配电箱"
+}
diff --git a/lib/class_list/text.json b/lib/class_list/text.json
new file mode 100644
index 0000000..6027779
--- /dev/null
+++ b/lib/class_list/text.json
@@ -0,0 +1 @@
+["object"]
diff --git a/lib/get_prompt.py b/lib/get_prompt.py
new file mode 100644
index 0000000..34d8f75
--- /dev/null
+++ b/lib/get_prompt.py
@@ -0,0 +1,53 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+将Markdown格式提示词转换为AI推理可用格式（换行替换为\n转义符）
+"""
+import os
+
+def get_prompt(md_path):
+    """
+    转换Markdown格式提示词为AI推理可用格式
+    :param input_content: 输入内容（md文件路径 或 md文本字符串）
+    :param is_file: 是否为文件路径（True：文件路径，False：直接传入md文本）
+    :param output_file: 输出文件路径（可选，指定则保存结果到文件）
+    :return: 转换后的AI可用提示词字符串
+    """
+    try:
+        # 1. 获取原始md内容
+        # 校验文件是否存在
+        if not os.path.exists(md_path):
+            raise FileNotFoundError(f"找不到指定的Markdown文件：{md_path}")
+        # 读取md文件（指定utf-8编码，避免中文乱码）
+        with open(md_path, 'r', encoding='utf-8') as f:
+            md_content = f.read()
+
+        # 2. 核心转换：将可视化换行（\r\n 或 \n）替换为转义换行符 \n
+        # 先统一处理Windows换行符 \r\n 为 Unix换行符 \n
+        # ai_prompt = md_content.replace('\r\n', '\n')
+        # （可选）如果需要将连续换行压缩为单个换行（避免AI识别多余空行），解开下面注释
+        # import re
+        # ai_prompt = re.sub(r'\n{2,}', '\n\n', ai_prompt)
+
+        # 3. （可选）保留Markdown关键格式的轻微优化（避免转义破坏md语法）
+        # 例如：保留代码块、标题、列表的格式，仅转换内容中的换行
+        # 此处无需额外处理，\n本身就可以被大多数AI（包括Kimi）识别为md换行
+
+        # 5. 返回转换后的提示词
+        return md_content
+
+    except Exception as e:
+        print(f"转换过程中出现错误：{e}")
+        return None
+
+from datetime import datetime
+def _current_time_str():
+    return datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+
+# 示例用法
+if __name__ == "__main__":
+    md_file_path = "./提示词 v2.md"
+    prompt = get_prompt(md_file_path)
+    if prompt:
+        print(f"[{_current_time_str()}][获取提示词] 转换成功！AI可用提示词如下：")
+        print(prompt)
diff --git a/lib/json_fun.py b/lib/json_fun.py
new file mode 100644
index 0000000..792c188
--- /dev/null
+++ b/lib/json_fun.py
@@ -0,0 +1,47 @@
+import json
+from collections import defaultdict
+
+
+def f_detections_to_objects(detections_path: str, output_path: str):
+    """
+    将输入文件中的yolo检测数据转换为object数据并保存到输出文件中
+    :param detections_path: 输入文件路径
+    :param output_path: 输出文件路径
+    :return: None
+    """
+    with open(detections_path, 'r', encoding='utf-8') as f:
+        detections_data = json.load(f)
+
+    class_set = set()
+    track_info = defaultdict(lambda: {"class_id": None, "start_frame": float('inf'), "end_frame": 0})
+
+    for frame_str, detections in detections_data.items():
+        frame = int(frame_str)
+        for det in detections:
+            track_id = det["track_id"]
+            class_id = det["class_id"]
+            class_str = det["class_str"]
+
+            class_set.add(class_str)
+
+            if track_info[track_id]["class_id"] is None:
+                track_info[track_id]["class_id"] = class_id
+
+            if frame < track_info[track_id]["start_frame"]:
+                track_info[track_id]["start_frame"] = frame
+            if frame > track_info[track_id]["end_frame"]:
+                track_info[track_id]["end_frame"] = frame
+
+    result = {
+        "class_list": list(class_set),
+        "track_id_list": dict(track_info)
+    }
+
+    with open(output_path, 'w', encoding='utf-8') as f:
+        json.dump(result, f, ensure_ascii=False, indent=2)
+
+if __name__ == "__main__":
+    f_detections_to_objects(
+        "output/santai5/frame_detections.json",
+        "output/santai5/objects.json"
+    )
\ No newline at end of file
diff --git a/lib/qwen_fun copy.py b/lib/qwen_fun copy.py
new file mode 100644
index 0000000..ed21b03
--- /dev/null
+++ b/lib/qwen_fun copy.py	
@@ -0,0 +1,638 @@
+import json
+import os
+from pathlib import Path
+import requests
+from openai import BadRequestError, OpenAI
+from concurrent.futures import ThreadPoolExecutor, as_completed
+import requests
+import cv2
+
+# 图片列表：键为下拉选项名称，值为包含网络地址和本地地址的字典
+# 注意：请使用有效的图片URL，或将图片下载到本地后使用本地路径
+file_dict: dict = {}
+# file_info_path: str = "file_dict.json"
+file_info_path: str = r"D:\Userfile\Downloads\export_urls.csv"
+# 设置环境变量（请替换为您自己的 API Key）
+os.environ["DASHSCOPE_API_KEY"] = 'sk-c7ffed1b32794284a5d21b046d7d0008'
+os.environ["MAXKB_API_KEY"] = 'agent-300b841bd6a7cb3a0bf603c093c22398'
+os.environ["MODEL_NAME"] = 'qwen3.5-27b'
+
+def chat(prompt, file_path: str | list, enable_thinking=False, fps=10, thinking_budget: int=81920):
+    """
+    调用 DashScope 接口进行图片理解与提示词检测。
+    参数 img_info 为包含网络和本地路径的字典。
+    """
+    print("开始处理视频...")
+    print("当前文件: ", file_path)
+    print("============提示词============")
+    print(prompt)
+
+    enable_thinking = enable_thinking
+    client = OpenAI(
+        api_key=os.getenv("DASHSCOPE_API_KEY"),
+        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
+    )
+    messages = []
+
+    if type(file_path) == str:
+        if '.jpg' in file_path.lower():
+            file_path = [["img", file_path]]
+        elif '.mp4' or '.avi' in file_path.lower():
+            file_path = [["vid", file_path, fps]]
+
+
+    content: list = []
+    for item in file_path:
+        if item[0] == "img":
+            content.append({"type": "image_url", "image_url": {"url": item[1]}})
+        elif item[0] == "vid":
+            content.append({"type": "video_url", "video_url": {"url": item[1]}, "fps": item[2]})
+        else:
+            raise ValueError(f"不支持的文件类型：{item[0]}", 99999)
+
+    content.append({"type": "text", "text": prompt})
+
+    messages = [{
+        "role": "user",
+        "content": content
+    }]
+
+    # 开启流式输出
+    try:
+        completion = client.chat.completions.create(
+        model=os.getenv("MODEL_NAME"), # type: ignore
+        messages=messages, # type: ignore
+        extra_body={"enable_thinking": enable_thinking, "thinking_budget": thinking_budget},
+        stream=True,
+        # 支持访问图片和视频的OSS协议 URL
+        extra_headers={"X-DashScope-OssResourceResolve": "enable"}
+    ) # type: ignore
+    except BadRequestError as e:
+        # 这里捕捉到特定的 400 错误
+        # 判断是否是图片相关的错误
+        if "Failed to download multimodal content" in str(e):
+            # 返回空的模型回复，并返回错误信息
+            raise ValueError("❌ 图片链接不存在或已过期")
+            # return "", "❌ 图片链接不存在或已过期"
+        else:
+            # 其他错误直接抛出或返回通用错误
+            raise ValueError(f"❌ 发生错误: {str(e)}")
+    except Exception as e:
+        # 捕捉其他未知错误
+        raise ValueError(f"❌ 未知错误: {str(e)}")
+
+    # 捕获流式数据
+    reasoning_text = ""
+    answer_text = ""
+    is_answering = False
+
+    if enable_thinking:
+        print("============思考过程============")
+    for chunk in completion:
+        delta = chunk.choices[0].delta
+        # 思考过程
+        if hasattr(delta, "reasoning_content") and delta.reasoning_content:
+            reasoning_text += delta.reasoning_content
+            print(delta.reasoning_content, end="")
+        # 最终答案
+        if hasattr(delta, "content") and delta.content:
+            if not is_answering:
+                is_answering = True
+                print("============最终答案============")
+            answer_text += delta.content
+            print(delta.content, end="")
+    print() # 换行
+
+    # 返回格式化后的结果
+    return reasoning_text, answer_text
+
+def upload_file_and_get_url(file_path: str) -> str:
+    """
+    单个文件上传到 OSS 并返回 URL（内部使用）
+    """
+    # 1. 获取上传策略
+    policy_resp = requests.get(
+        "https://dashscope.aliyuncs.com/api/v1/uploads",
+        headers={"Authorization": f"Bearer {os.getenv('DASHSCOPE_API_KEY')}"},
+        params={"action": "getPolicy", "model": os.getenv('MODEL_NAME')}
+    )
+
+    # 检查请求是否成功
+    if policy_resp.status_code != 200:
+        raise Exception(f"获取上传策略失败，状态码: {policy_resp.status_code}, 响应: {policy_resp.text}")
+
+    # 尝试获取 'data' 字段
+    policy_json = policy_resp.json()
+    if 'data' not in policy_json:
+        raise Exception(f"获取上传策略失败，返回内容不包含 'data' 字段: {policy_json}")
+
+    policy_data = policy_json['data']
+
+    # 2. 上传文件到 OSS
+    file_name = Path(file_path).name
+    key = f"{policy_data['upload_dir']}/{file_name}"
+
+    with open(file_path, 'rb') as f:
+        files = {
+            'OSSAccessKeyId': (None, policy_data['oss_access_key_id']),
+            'Signature': (None, policy_data['signature']),
+            'policy': (None, policy_data['policy']),
+            'x-oss-object-acl': (None, policy_data['x_oss_object_acl']),
+            'x-oss-forbid-overwrite': (None, policy_data['x_oss_forbid_overwrite']),
+            'key': (None, key),
+            'success_action_status': (None, '200'),
+            'file': (file_name, f)
+        }
+        response = requests.post(policy_data['upload_host'], files=files)
+        if response.status_code != 200:
+            raise Exception(f"文件上传失败: {file_path}, 状态码: {response.status_code}, 响应: {response.text}")
+
+    return f"oss://{key}"
+
+def upload_files_and_get_urls_concurrently(
+    file_path_list: list[str],
+    max_workers: int | None = None,
+    timeout: int = 300
+) -> list[str | None]:
+    """
+    并发上传多个文件到 OSS 并返回对应的 URL 列表（顺序对应）
+    """
+    # 初始化一个与文件列表长度相同的结果列表
+    results: list[str | None] = [None] * len(file_path_list)
+
+    with ThreadPoolExecutor(max_workers=max_workers) as executor:
+        # 使用字典存储 future 与其对应的索引
+        future_to_index = {
+            executor.submit(
+                upload_file_and_get_url,
+                file_path
+            ): idx
+            for idx, file_path in enumerate(file_path_list)
+        }
+
+        for future in as_completed(future_to_index, timeout=timeout):
+            idx = future_to_index[future]
+            file_path = file_path_list[idx]
+            try:
+                url = future.result()
+                results[idx] = url  # 将结果放回对应的索引位置
+                print(f"[成功] {file_path} -> {url}")
+            except Exception as exc:
+                # 捕获异常并打印错误信息
+                print(f"[错误] {file_path} 上传失败: {exc}")
+                results[idx] = None  # 保持为 None 或者设为错误标识
+
+    return results
+
+def save_json_to_file(json_data, file_path: str) -> None:
+    """
+    保存 JSON 数据到文件
+
+    参数:
+        json_data: 要保存的 JSON 数据
+        file_path: 文件路径
+    """
+    # 1. 提取目录路径
+    dir_path = os.path.dirname(file_path)
+
+    # 2. 如果有目录路径，确保它存在
+    if dir_path:
+        os.makedirs(dir_path, exist_ok=True)  # exist_ok=True 防止重复创建报错
+
+    # 3. 写入文件
+    try:
+        with open(file_path, 'w', encoding='utf-8') as f:
+            json.dump(json_data, f, ensure_ascii=False, indent=4)
+    except Exception as e:
+        print(e)
+        return
+
+    print(f"已保存 JSON 数据到: {file_path}")
+
+def load_json_data(json_path: str):
+    with open(json_path, 'r', encoding='utf-8') as f:
+        return json.load(f)
+
+def get_unique_track_id_count(json_path: str) -> int:
+    """
+    提取并计算JSON文件中不重复的track_id总数
+
+    参数:
+        json_path: JSON文件路径
+
+    返回:
+        不重复的track_id总数
+    """
+    # 加载JSON数据
+    data = load_json_data(json_path)
+
+    # 存储唯一的track_id
+    unique_track_ids = set()
+
+    # 遍历所有帧
+    for frame_data in data.values():
+        # 遍历当前帧的所有检测结果
+        for detection in frame_data:
+            # 提取track_id并添加到集合中
+            if 'track_id' in detection:
+                unique_track_ids.add(detection['track_id'])
+
+    # 返回唯一track_id的数量
+    return len(unique_track_ids)
+
+def get_annnotated_frame_for_ai(json_data: dict, interval: int = 5) -> dict:
+    """
+    从 frame_all.json 文件中提取ai能看到的部分，并重新计算帧号
+    按照指定格式重构返回结果
+
+    输出json_data格式：
+    {
+        "class_list": ["class_name1", "class_name2", ..."],  # 类别名称
+        "frame_list": {
+            "0": [ # frame_idx
+                {
+                    "xyxy": [
+                        1903.0,
+                        167.0,
+                        1919.0,
+                        335.0
+                    ],
+                    "track_id": 8,
+                    "class_id": 0
+                },
+                ...
+            ],
+        },
+    }
+    """
+    # 抽取0,5,10...并重命名帧号为0,1,2...
+    frame_ids: list = list(json_data.keys())
+    selected_frames: list = frame_ids[::interval]
+
+    # 提取所有类别名称（去重）
+    class_set = set()
+    for frame_id in selected_frames:
+        frame_data = json_data[frame_id]
+        for obj in frame_data:
+            class_set.add(obj["class_str"])
+    class_list = sorted(list(class_set))
+
+    # 建立类别名称到ID的映射
+    class_name_to_id = {name: idx for idx, name in enumerate(class_list)}
+
+    # 构建frame_list
+    frame_list = {}
+    for new_idx, frame_id in enumerate(selected_frames):
+        frame_data = json_data[frame_id]
+        obj_list = []
+        for obj in frame_data:
+            obj_list.append({
+                "xyxy": obj["xyxy"],
+                "track_id": obj["track_id"],
+                "class_id": class_name_to_id[obj["class_str"]]
+            })
+        frame_list[str(new_idx)] = obj_list
+
+    # 重构返回结果
+    result = {
+        "class_list": class_list,
+        "frame_list": frame_list
+    }
+
+    return result
+
+def get_annnotated_frame_for_ai_without_xyxy(json_data: dict, interval: int = 5) -> dict:
+    """
+    从 frame_all.json 文件中提取ai能看到的部分，并重新计算帧号
+    按照指定格式重构返回结果
+
+    输出json_data格式：
+    {
+        "class_list": ["class_name1", "class_name2", ..."],  # 类别名称
+        "frame_list": {
+            "0": [ # frame_idx
+                {
+                    "track_id": 8,
+                    "class_id": 0
+                },
+                ...
+            ],
+        },
+    }
+    """
+    # 抽取0,5,10...并重命名帧号为0,1,2...
+    frame_ids: list = list(json_data.keys())
+    selected_frames: list = frame_ids[::interval]
+
+    # 提取所有类别名称（去重）
+    class_set = set()
+    for frame_id in selected_frames:
+        frame_data = json_data[frame_id]
+        for obj in frame_data:
+            class_set.add(obj["class_str"])
+    class_list = sorted(list(class_set))
+
+    # 建立类别名称到ID的映射
+    class_name_to_id = {name: idx for idx, name in enumerate(class_list)}
+
+    # 构建frame_list
+    frame_list = {}
+    for new_idx, frame_id in enumerate(selected_frames):
+        frame_data = json_data[frame_id]
+        obj_list = []
+        for obj in frame_data:
+            obj_list.append({
+                "track_id": obj["track_id"],
+                "class_id": class_name_to_id[obj["class_str"]]
+            })
+        frame_list[str(new_idx)] = obj_list
+
+    # 重构返回结果
+    result = {
+        "class_list": class_list,
+        "frame_list": frame_list
+    }
+
+    return result
+
+def search_knowledge_base(
+    question: str,
+    system_prompt: str = "你是科技有限公司 MaxKB 知识问答系统的智能小，你的工作是 MaxKB 用户解答中，用户找你回答问题时，你要把主题放在 MaxKB 知识问答系统身上",
+    **kwargs
+):
+    """
+    通过 MaxKB 知识库问答系统获取关于 'MaxKB 是什么？' 的答案。
+    """
+    # 1. 请求 URL
+    url = "http://localhost:8080/chat/api/019d5265-e90e-7663-b202-fc5a47d8601c/chat/completions"
+
+    # 2. 请求头
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": "Bearer agent-300b841bd6a7cb3a0bf603c093c22398"
+    }
+
+    # 3. 请求体（Payload）
+    payload = {
+        "model": "qwen3.5-27b",
+        "messages": [
+            {
+                "role": system_prompt,
+                "content": question
+            }
+        ]
+    }
+
+    # 4. 发送 POST 请求
+    response = requests.post(url, headers=headers, json=payload)
+
+    # 5. 处理响应
+    if response.status_code == 200:
+        try:
+            # 尝试解析为 JSON
+            data = response.json()
+            # 打印完整的响应结构（调试用）
+            print("Full Response:")
+            print(json.dumps(data, ensure_ascii=False, indent=2))
+
+            # 尝试提取回答内容（根据常见的结构）
+            # 这里假设答案在 data['choices'][0]['message']['content'] 中
+            if 'choices' in data and data['choices']:
+                answer = data['choices'][0].get('message', {}).get('content', '')
+                print("\n=== MaxKB 回答 ===")
+                print(answer.strip())
+
+                return answer.strip()
+            else:
+                print("\n未在响应中找到 'choices' 字段。")
+                return ""
+        except Exception as e:
+            print("解析响应时出错:", e)
+            print("原始响应:", response.text)
+            return ""
+    else:
+        print(f"请求失败，状态码: {response.status_code}")
+        print("错误信息:", response.text)
+        return ""
+
+def hazard_inspection(
+    class_dict: dict,
+    video_url: str,
+    enable_thinking: bool = True,
+    fps: int = 10
+) -> tuple[str, str]:
+    """
+    检查视频中给定类型物体的隐患
+    2026.04.07
+
+    参数:
+        class_dict: 类别字典
+        input_video_path: 视频路径
+        enable_thinking: 是否开启思考模式
+        fps
+
+    class_dict:结构:
+    {
+        "class_list": ["class_name1", "class_name2", ..."],  # 类别名称
+        "frame_list": {
+            "0": [ # frame_idx
+                {
+                    "track_id": 8,
+                    "class_id": 0
+                },
+                ...
+            ],
+        },
+    }
+    """
+
+    class_data: str = str(class_dict)
+
+    # 筛选检查规则 知识库\rule.json
+    all_check_rule_dict: dict = load_json_data(r'知识库\rule.json')
+    check_rule_list: list = []
+
+    # 筛选出当前视频中存在的检查规则
+    for class_str in class_dict["class_list"]:
+        if class_str in all_check_rule_dict:
+            check_rule_list.append(all_check_rule_dict[class_str])
+
+    # 读取提示词文件 7_检测提示词_视频.md
+    prompt_str: str = ""
+    with open(r'prompt\7_检测提示词_视频_new3.md', 'r', encoding='utf-8') as file:
+        prompt_str = file.read()
+
+    prompt: str = f"""
+# 每帧存在的物体类别\n
+{class_data}
+\n
+# 检查规则\n
+{check_rule_list}
+\n
+{prompt_str}
+"""
+
+    # 获取大模型对话结果
+    return chat(prompt, video_url, enable_thinking, fps)
+
+def merge_conflict_inspection_data(data: dict) -> dict:
+    """
+    合并冲突数据（相同 tag_id 和 class_id 的重叠时间段）。
+
+    参数:
+    - data: 包含 "class", "tag", "objects" 键的字典。
+
+    返回:
+    - 合并后的数据字典，结构与输入相同，但 objects 已合并。
+    """
+
+    # 1. 按照 (tag_id, class_id) 对对象进行分组
+    groups = {}
+    for obj in data["objects"]:
+        key = (obj["tag_id"], obj["class_id"])
+        groups.setdefault(key, []).append(obj)
+
+    merged_objects = []
+    new_id = 1
+
+    # 2. 对每个分组进行合并
+    for (tag_id, class_id), objs in groups.items():
+        # 按 start_frame 排序
+        sorted_objs = sorted(objs, key=lambda x: x["start_frame"])
+
+        # 初始化第一个区间
+        cur_start = sorted_objs[0]["start_frame"]
+        cur_end = sorted_objs[0]["end_frame"]
+        cur_level = sorted_objs[0]["level"]
+
+        for obj in sorted_objs[1:]:
+            # 如果当前区间与下一个区间重叠或相连（end >= start）
+            if obj["start_frame"] <= cur_end:
+                # 合并区间：取结束帧的最大值
+                cur_end = max(cur_end, obj["end_frame"])
+                # 级别取最大值（如果级别不同）
+                cur_level = max(cur_level, obj["level"])
+            else:
+                # 当前区间结束，保存合并结果
+                merged_objects.append({
+                    "hazard_track_id": new_id,
+                    "tag_id": tag_id,
+                    "class_id": class_id,
+                    "level": cur_level,
+                    "start_frame": cur_start,
+                    "end_frame": cur_end
+                })
+                new_id += 1
+                # 开始新的区间
+                cur_start = obj["start_frame"]
+                cur_end = obj["end_frame"]
+    data["objects"] = merged_objects
+    return data
+
+def report_generator(
+    video_path: str,
+    detection_data: dict,
+    hazard_results: dict,
+    rule_definitions: dict,
+    output_path: str = "output",
+    frame_interval: float = 1.0
+):
+    """
+    生成隐患报告的主函数。
+
+    参数:
+        video_path (str): 视频文件路径
+        detection_data (dict): 物体检测数据，格式为 {frame_id: [detection_info, ...]}
+        hazard_results (dict): 隐患检查结果
+        rule_definitions (dict): 隐患规则定义
+        output_path (str): 输出文件夹路径，默认 'output'
+        frame_interval (float): 帧间隔时间（秒），用于计算实际帧号，默认 1.0
+
+    功能:
+        1. 创建必要的文件夹结构
+        2. 根据隐患时间范围截取对应的图片
+        3. 生成 Markdown 报告
+    """
+
+    # ----------------------- 1. 准备工作 -----------------------
+    # 创建文件夹结构
+    report_dir = os.path.join(output_path, "report")
+    assets_dir = os.path.join(report_dir, "_assets")
+    os.makedirs(assets_dir, exist_ok=True)
+
+    # 打开视频文件
+    cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        raise IOError(f"无法打开视频文件: {video_path}")
+
+    # 获取视频总帧数（用于安全检查）
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    # ----------------------- 2. 处理隐患数据并截取图片 -----------------------
+    # 用于存储 Markdown 表格的行数据
+    md_table_rows = []
+
+    # 遍历所有隐患对象
+    for idx, obj in enumerate(hazard_results["objects"], start=1):
+        # 计算实际的起始帧（考虑帧间隔）
+        start_frame = int(obj["start_frame"] * frame_interval)
+
+        # 防止帧号越界
+        start_frame = max(0, min(start_frame, total_frames - 1))
+
+        # 获取隐患对应的类名和标签
+        class_name = hazard_results["class"][obj["class_id"]]
+        tag_name = hazard_results["tag"][obj["tag_id"]]
+        level = obj["level"]
+        location = obj.get("location", "")
+
+        # ----------------------- 2.1. 截取图片 -----------------------
+        # 默认选择 start_frame 作为截图帧
+        target_frame = start_frame
+
+        # 截图并保存
+        img_filename = f"{idx}_{target_frame}.jpg"
+        img_path = os.path.join(assets_dir, img_filename)
+
+        # 读取对应帧并保存整帧图片
+        cap.set(cv2.CAP_PROP_POS_FRAMES, target_frame)
+        ret, frame = cap.read()
+        if ret:
+            cv2.imwrite(img_path, frame)
+
+        # ----------------------- 2.2. 生成 Markdown 表格行 -----------------------
+        # 生成图片引用的 Markdown 语法
+        img_markdown = f"![](./_assets/{img_filename})"
+
+        # 隐患依据
+        rule_info = rule_definitions.get(class_name, {})
+        # 默认取第一个风险等级（如 '重大隐患'）
+        level_key = next(iter(rule_info.keys())) if rule_info else ""
+        # 获取具体的隐患描述信息
+        hazard_desc = rule_info.get(level_key, {}).get(tag_name, {})
+        basis = hazard_desc.get("依据", [])
+        # 只取前 2 条依据作为展示
+        basis_text = "<br>".join(basis[:2]) if basis else "暂无依据"
+
+        # 组装表格行
+        row = f"|{idx}|{level}|{basis_text}|{obj['start_frame']}|{location}|{img_markdown}|"
+        md_table_rows.append(row)
+
+    # ----------------------- 3. 生成 Markdown 报告 -----------------------
+    md_content = f"""# 隐患报告
+
+## 隐患清单
+
+| 隐患序号 | 隐患等级 | 依据 | 开始帧 | 位置描述 | 照片 |
+| :---: | :---: | :--- | :---: | :--- | :---: |
+"""
+    md_content += "\n".join(md_table_rows)
+
+    # 写入报告文件
+    report_path = os.path.join(report_dir, "report.md")
+    with open(report_path, "w", encoding="utf-8") as f:
+        f.write(md_content)
+
+    # 释放资源
+    cap.release()
+    print(f"报告已生成: {report_path}")
diff --git a/lib/qwen_fun.py b/lib/qwen_fun.py
new file mode 100644
index 0000000..8c8ac19
--- /dev/null
+++ b/lib/qwen_fun.py
@@ -0,0 +1,760 @@
+import json
+import os
+from pathlib import Path
+import requests
+from openai import BadRequestError, OpenAI
+from concurrent.futures import ThreadPoolExecutor, as_completed
+import requests
+import cv2
+
+# 图片列表：键为下拉选项名称，值为包含网络地址和本地地址的字典
+# 注意：请使用有效的图片URL，或将图片下载到本地后使用本地路径
+file_dict: dict = {}
+# file_info_path: str = "file_dict.json"
+file_info_path: str = r"D:\Userfile\Downloads\export_urls.csv"
+# 设置环境变量（请替换为您自己的 API Key）
+os.environ["DASHSCOPE_API_KEY"] = 'sk-c7ffed1b32794284a5d21b046d7d0008'
+os.environ["MAXKB_API_KEY"] = 'agent-300b841bd6a7cb3a0bf603c093c22398'
+os.environ["MODEL_NAME"] = 'qwen3.5-27b'
+
+def chat(prompt, file_path: str | list, enable_thinking=False, fps=10, thinking_budget: int=81920):
+    """
+    调用 DashScope 接口进行图片理解与提示词检测。
+    参数 img_info 为包含网络和本地路径的字典。
+    """
+    print("开始处理视频...")
+    print("当前文件: ", file_path)
+    print("============提示词============")
+    print(prompt)
+
+    enable_thinking = enable_thinking
+    client = OpenAI(
+        api_key=os.getenv("DASHSCOPE_API_KEY"),
+        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
+    )
+    messages = []
+
+    if type(file_path) == str:
+        if '.jpg' in file_path.lower():
+            file_path = [["img", file_path]]
+        elif '.mp4' or '.avi' in file_path.lower():
+            file_path = [["vid", file_path, fps]]
+
+
+    content: list = []
+    for item in file_path:
+        if item[0] == "img":
+            content.append({"type": "image_url", "image_url": {"url": item[1]}})
+        elif item[0] == "vid":
+            content.append({"type": "video_url", "video_url": {"url": item[1]}, "fps": item[2]})
+        else:
+            raise ValueError(f"不支持的文件类型：{item[0]}", 99999)
+
+    content.append({"type": "text", "text": prompt})
+
+    messages = [{
+        "role": "user",
+        "content": content
+    }]
+
+    # 开启流式输出
+    try:
+        completion = client.chat.completions.create(
+        model=os.getenv("MODEL_NAME"), # type: ignore
+        messages=messages, # type: ignore
+        extra_body={"enable_thinking": enable_thinking, "thinking_budget": thinking_budget},
+        stream=True,
+        # 支持访问图片和视频的OSS协议 URL
+        extra_headers={"X-DashScope-OssResourceResolve": "enable"}
+    ) # type: ignore
+    except BadRequestError as e:
+        # 这里捕捉到特定的 400 错误
+        # 判断是否是图片相关的错误
+        if "Failed to download multimodal content" in str(e):
+            # 返回空的模型回复，并返回错误信息
+            raise ValueError("❌ 图片链接不存在或已过期")
+            # return "", "❌ 图片链接不存在或已过期"
+        else:
+            # 其他错误直接抛出或返回通用错误
+            raise ValueError(f"❌ 发生错误: {str(e)}")
+    except Exception as e:
+        # 捕捉其他未知错误
+        raise ValueError(f"❌ 未知错误: {str(e)}")
+
+    # 捕获流式数据
+    reasoning_text = ""
+    answer_text = ""
+    is_answering = False
+
+    if enable_thinking:
+        print("============思考过程============")
+    for chunk in completion:
+        delta = chunk.choices[0].delta
+        # 思考过程
+        if hasattr(delta, "reasoning_content") and delta.reasoning_content:
+            reasoning_text += delta.reasoning_content
+            print(delta.reasoning_content, end="")
+        # 最终答案
+        if hasattr(delta, "content") and delta.content:
+            if not is_answering:
+                is_answering = True
+                print("============最终答案============")
+            answer_text += delta.content
+            print(delta.content, end="")
+    print() # 换行
+
+    # 返回格式化后的结果
+    return reasoning_text, answer_text
+
+def upload_file_and_get_url(file_path: str) -> str:
+    """
+    单个文件上传到 OSS 并返回 URL（内部使用）
+    """
+    # 1. 获取上传策略
+    policy_resp = requests.get(
+        "https://dashscope.aliyuncs.com/api/v1/uploads",
+        headers={"Authorization": f"Bearer {os.getenv('DASHSCOPE_API_KEY')}"},
+        params={"action": "getPolicy", "model": os.getenv('MODEL_NAME')}
+    )
+
+    # 检查请求是否成功
+    if policy_resp.status_code != 200:
+        raise Exception(f"获取上传策略失败，状态码: {policy_resp.status_code}, 响应: {policy_resp.text}")
+
+    # 尝试获取 'data' 字段
+    policy_json = policy_resp.json()
+    if 'data' not in policy_json:
+        raise Exception(f"获取上传策略失败，返回内容不包含 'data' 字段: {policy_json}")
+
+    policy_data = policy_json['data']
+
+    # 2. 上传文件到 OSS
+    file_name = Path(file_path).name
+    key = f"{policy_data['upload_dir']}/{file_name}"
+
+    with open(file_path, 'rb') as f:
+        files = {
+            'OSSAccessKeyId': (None, policy_data['oss_access_key_id']),
+            'Signature': (None, policy_data['signature']),
+            'policy': (None, policy_data['policy']),
+            'x-oss-object-acl': (None, policy_data['x_oss_object_acl']),
+            'x-oss-forbid-overwrite': (None, policy_data['x_oss_forbid_overwrite']),
+            'key': (None, key),
+            'success_action_status': (None, '200'),
+            'file': (file_name, f)
+        }
+        response = requests.post(policy_data['upload_host'], files=files)
+        if response.status_code != 200:
+            raise Exception(f"文件上传失败: {file_path}, 状态码: {response.status_code}, 响应: {response.text}")
+
+    return f"oss://{key}"
+
+def upload_files_and_get_urls_concurrently(
+    file_path_list: list[str],
+    max_workers: int | None = None,
+    timeout: int = 300
+) -> list[str | None]:
+    """
+    并发上传多个文件到 OSS 并返回对应的 URL 列表（顺序对应）
+    """
+    # 初始化一个与文件列表长度相同的结果列表
+    results: list[str | None] = [None] * len(file_path_list)
+
+    with ThreadPoolExecutor(max_workers=max_workers) as executor:
+        # 使用字典存储 future 与其对应的索引
+        future_to_index = {
+            executor.submit(
+                upload_file_and_get_url,
+                file_path
+            ): idx
+            for idx, file_path in enumerate(file_path_list)
+        }
+
+        for future in as_completed(future_to_index, timeout=timeout):
+            idx = future_to_index[future]
+            file_path = file_path_list[idx]
+            try:
+                url = future.result()
+                results[idx] = url  # 将结果放回对应的索引位置
+                print(f"[成功] {file_path} -> {url}")
+            except Exception as exc:
+                # 捕获异常并打印错误信息
+                print(f"[错误] {file_path} 上传失败: {exc}")
+                results[idx] = None  # 保持为 None 或者设为错误标识
+
+    return results
+
+def save_json_to_file(json_data, file_path: str) -> None:
+    """
+    保存 JSON 数据到文件
+
+    参数:
+        json_data: 要保存的 JSON 数据
+        file_path: 文件路径
+    """
+    # 1. 提取目录路径
+    dir_path = os.path.dirname(file_path)
+
+    # 2. 如果有目录路径，确保它存在
+    if dir_path:
+        os.makedirs(dir_path, exist_ok=True)  # exist_ok=True 防止重复创建报错
+
+    # 3. 写入文件
+    try:
+        with open(file_path, 'w', encoding='utf-8') as f:
+            json.dump(json_data, f, ensure_ascii=False, indent=4)
+    except Exception as e:
+        print(e)
+        return
+
+    print(f"已保存 JSON 数据到: {file_path}")
+
+def load_json_data(json_path: str):
+    with open(json_path, 'r', encoding='utf-8') as f:
+        return json.load(f)
+
+def get_unique_track_id_count(json_path: str) -> int:
+    """
+    提取并计算JSON文件中不重复的track_id总数
+
+    参数:
+        json_path: JSON文件路径
+
+    返回:
+        不重复的track_id总数
+    """
+    # 加载JSON数据
+    data = load_json_data(json_path)
+
+    # 存储唯一的track_id
+    unique_track_ids = set()
+
+    # 遍历所有帧
+    for frame_data in data.values():
+        # 遍历当前帧的所有检测结果
+        for detection in frame_data:
+            # 提取track_id并添加到集合中
+            if 'track_id' in detection:
+                unique_track_ids.add(detection['track_id'])
+
+    # 返回唯一track_id的数量
+    return len(unique_track_ids)
+
+def get_annnotated_frame_for_ai_without_xyxy(json_data: dict, interval: int = 5, conf: float = 0.7) -> dict:
+    """
+    从 frame_all.json 文件中提取ai能看到的部分，并重新计算帧号
+    按照指定格式重构返回结果
+
+    输出json_data格式：
+    {
+        "class_list": ["class_name1", "class_name2", ..."],  # 类别名称
+        "track_id_list": {
+            "0": {  # track_id
+                "class_id": 0,
+                "start_frame": 0,
+                "end_frame": 10
+            },
+            ...
+        },
+    }
+    """
+    # 抽取0,5,10...并重命名帧号为0,1,2...
+    frame_ids: list = list(json_data.keys())
+    selected_frames: list = frame_ids[::interval]
+
+    # 提取所有类别名称（去重）
+    class_set = set()
+    for frame_id in selected_frames:
+        frame_data = json_data[frame_id]
+        for obj in frame_data:
+            class_set.add(obj["class_str"])
+    class_list = sorted(list(class_set))
+
+    # 建立类别名称到ID的映射
+    class_name_to_id = {name: idx for idx, name in enumerate(class_list)}
+
+    # 构建track_id_list
+    track_id_dict = {}
+    for new_idx, frame_id in enumerate(selected_frames):
+        frame_data = json_data[frame_id]
+        for obj in frame_data:
+            if obj["confidence"] < conf:
+                continue
+
+            track_id = obj["track_id"]
+            class_id = class_name_to_id[obj["class_str"]]
+
+            if track_id not in track_id_dict:
+                track_id_dict[track_id] = {
+                    "class_id": class_id,
+                    "start_frame": new_idx,
+                    "end_frame": new_idx
+                }
+            else:
+                track_id_dict[track_id]["end_frame"] = new_idx
+
+    # 重构返回结果
+    result = {
+        "class_list": class_list,
+        "track_id_list": track_id_dict
+    }
+
+    return result
+
+def search_knowledge_base(
+    question: str,
+    system_prompt: str = "你是科技有限公司 MaxKB 知识问答系统的智能小，你的工作是 MaxKB 用户解答中，用户找你回答问题时，你要把主题放在 MaxKB 知识问答系统身上",
+    **kwargs
+):
+    """
+    通过 MaxKB 知识库问答系统获取关于 'MaxKB 是什么？' 的答案。
+    """
+    # 1. 请求 URL
+    url = "http://localhost:8080/chat/api/019d5265-e90e-7663-b202-fc5a47d8601c/chat/completions"
+
+    # 2. 请求头
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": "Bearer agent-300b841bd6a7cb3a0bf603c093c22398"
+    }
+
+    # 3. 请求体（Payload）
+    payload = {
+        "model": "qwen3.5-27b",
+        "messages": [
+            {
+                "role": system_prompt,
+                "content": question
+            }
+        ]
+    }
+
+    # 4. 发送 POST 请求
+    response = requests.post(url, headers=headers, json=payload)
+
+    # 5. 处理响应
+    if response.status_code == 200:
+        try:
+            # 尝试解析为 JSON
+            data = response.json()
+            # 打印完整的响应结构（调试用）
+            print("Full Response:")
+            print(json.dumps(data, ensure_ascii=False, indent=2))
+
+            # 尝试提取回答内容（根据常见的结构）
+            # 这里假设答案在 data['choices'][0]['message']['content'] 中
+            if 'choices' in data and data['choices']:
+                answer = data['choices'][0].get('message', {}).get('content', '')
+                print("\n=== MaxKB 回答 ===")
+                print(answer.strip())
+
+                return answer.strip()
+            else:
+                print("\n未在响应中找到 'choices' 字段。")
+                return ""
+        except Exception as e:
+            print("解析响应时出错:", e)
+            print("原始响应:", response.text)
+            return ""
+    else:
+        print(f"请求失败，状态码: {response.status_code}")
+        print("错误信息:", response.text)
+        return ""
+
+def hazard_inspection(
+    class_dict: dict,
+    video_url: str,
+    enable_thinking: bool = True,
+    fps: int = 10
+) -> tuple[str, str]:
+    """
+    检查视频中给定类型物体的隐患
+    2026.04.07
+
+    参数:
+        class_dict: 类别字典
+        input_video_path: 视频路径
+        enable_thinking: 是否开启思考模式
+        fps
+
+    class_dict:结构:
+    {
+        "class_list": ["class_name1", "class_name2", ..."],  # 类别名称
+        "track_id_list": {
+            "0": {  # track_id
+                "class_id": 0,
+                "start_frame": 0,
+                "end_frame": 10
+            },
+            ...
+        },
+    }
+
+    返回:
+        tuple[str, str]: (reasoning_text, json_result)
+        json_result: JSON格式的隐患检测结果
+    """
+
+    # 筛选检查规则 知识库\rule.json
+    all_check_rule_dict: dict = load_json_data(r'知识库\rule.json')
+
+    # 读取提示词文件 7_检测提示词_视频.md
+    prompt_str: str = ""
+    with open(r'prompt\7_检测提示词_视频_new3.md', 'r', encoding='utf-8') as file:
+        prompt_str = file.read()
+
+    # 存储所有类别的结果
+    all_reasoning_text = ""
+
+    # 存储所有类别的 JSON 结果
+    all_tags = []
+    all_bases = []
+    all_objects = []
+
+    # 保存每个类别的原始 tag 和 base 映射，用于后续重新映射
+    class_tag_mapping = {}
+    class_base_mapping = {}
+
+    # 遍历每个类别，分别进行 chat
+<<<<<<< HEAD
+    print("类别列表:", class_dict["class_list"])
+    for class_str in class_dict["class_list"]:
+        print(f"当前类别: {class_str}")
+        # 检查该类别是否有检查规则
+        class_check_rule: str = ""
+        scene_list: list[str] = list(all_check_rule_dict.keys())
+        print(f"场景列表: {scene_list}")
+        for scene in scene_list:
+            if class_str in all_check_rule_dict[scene].keys():
+                class_check_rule = all_check_rule_dict[scene][class_str]
+                break
+        if class_check_rule == "":
+            # 该类别没有检查规则
+            continue
+
+=======
+    for class_str in class_dict["class_list"]:
+        # 检查该类别是否有检查规则
+        if class_str not in all_check_rule_dict["（一）厂房防火分区"].keys():
+            continue
+
+        # 获取该类别的规则
+        class_check_rule = all_check_rule_dict["（一）厂房防火分区"][class_str]
+
+>>>>>>> 7562de4 (2 预览图还有点问题)
+        # 筛选出该类别的 track_id_list
+        class_track_info = []
+        for track_id, track_data in class_dict["track_id_list"].items():
+            track_class_name = class_dict["class_list"][track_data["class_id"]]
+            if track_class_name == class_str:
+                class_track_info.append(f"Track ID {track_id}: {track_class_name} (帧 {track_data['start_frame']}-{track_data['end_frame']})")
+
+        # 如果该类别没有跟踪对象，跳过
+        if not class_track_info:
+            continue
+
+        track_data_str = "\n".join(class_track_info)
+
+        # 构建该类别的 prompt
+        prompt: str = f"""
+# 类别: {class_str}
+
+# 跟踪对象信息
+{track_data_str}
+
+# 检查规则
+{class_check_rule}
+
+{prompt_str}
+"""
+
+        # 调用 chat 函数
+        reasoning_text, answer_text = chat(prompt, video_url, enable_thinking, fps)
+
+        # 整合思考过程
+        all_reasoning_text += f"\n\n# 类别: {class_str}\n{reasoning_text}"
+
+        # 解析 JSON 结果
+        try:
+            # 提取 JSON 部分（去除可能的代码块标记）
+            json_str = answer_text.strip()
+            if json_str.startswith("```json"):
+                json_str = json_str[7:]
+            if json_str.startswith("```"):
+                json_str = json_str[3:]
+            if json_str.endswith("```"):
+                json_str = json_str[:-3]
+            json_str = json_str.strip()
+
+            class_result = json.loads(json_str)
+
+            # 收集该类别的结果
+            if "tag" in class_result:
+                # 保存该类别的 tag 映射
+                class_tag_mapping[class_str] = class_result["tag"]
+                all_tags.extend(class_result["tag"])
+            if "base" in class_result:
+                # 保存该类别的 base 映射
+                class_base_mapping[class_str] = class_result["base"]
+                all_bases.extend(class_result["base"])
+            if "objects" in class_result:
+                all_objects.extend(class_result["objects"])
+
+        except json.JSONDecodeError as e:
+            print(f"解析类别 {class_str} 的 JSON 结果失败: {e}")
+            print(f"原始输出: {answer_text}")
+            continue
+
+    # 合并去重 tag 和 base
+    unique_tags = []
+    seen_tags = set()
+    for tag in all_tags:
+        if tag not in seen_tags:
+            seen_tags.add(tag)
+            unique_tags.append(tag)
+
+    unique_bases = []
+    seen_bases = set()
+    for base in all_bases:
+        if base not in seen_bases:
+            seen_bases.add(base)
+            unique_bases.append(base)
+
+    # 重新分配 tag_id 和 base_id
+    # 由于每个类别是独立处理的，需要根据 class_id 找到对应的类别，然后重新映射
+    # 但是 object 中的 class_id 是相对于 class_list 的，需要找到对应的类别名称
+    final_objects = []
+    for obj in all_objects:
+        new_obj = obj.copy()
+        new_obj["hazard_track_id"] = len(final_objects)
+
+        # 通过 class_id 找到类别名称
+        class_id = obj.get("class_id", 0)
+        if class_id < len(class_dict["class_list"]):
+            class_name = class_dict["class_list"][class_id]
+
+            # 重新映射 tag_id
+            if "tag_id" in obj and class_name in class_tag_mapping:
+                original_tags = class_tag_mapping[class_name]
+                if obj["tag_id"] < len(original_tags):
+                    original_tag = original_tags[obj["tag_id"]]
+                    if original_tag in unique_tags:
+                        new_obj["tag_id"] = unique_tags.index(original_tag)
+
+            # 重新映射 base_id
+            if "base_id" in obj and class_name in class_base_mapping:
+                original_bases = class_base_mapping[class_name]
+                if obj["base_id"] < len(original_bases):
+                    original_base = original_bases[obj["base_id"]]
+                    if original_base in unique_bases:
+                        new_obj["base_id"] = unique_bases.index(original_base)
+
+        final_objects.append(new_obj)
+
+    # 构建最终结果
+    final_result = {
+        "tag": unique_tags,
+        "base": unique_bases,
+        "objects": final_objects
+    }
+
+    # 转换为 JSON 字符串
+    json_result = json.dumps(final_result, ensure_ascii=False, indent=2)
+
+    return all_reasoning_text, json_result
+
+def merge_conflict_inspection_data(data: dict) -> dict:
+    """
+    合并冲突数据（相同 tag_id 和 class_id 的重叠时间段）。
+
+    参数:
+    - data: 包含 "class", "tag", "objects" 键的字典。
+
+    返回:
+    - 合并后的数据字典，结构与输入相同，但 objects 已合并。
+    """
+
+    # 1. 按照 (tag_id, class_id) 对对象进行分组
+    groups = {}
+    for obj in data["objects"]:
+        key = (obj["tag_id"], obj["class_id"])
+        groups.setdefault(key, []).append(obj)
+
+    merged_objects = []
+    new_id = 1
+
+    # 2. 对每个分组进行合并
+    for (tag_id, class_id), objs in groups.items():
+        # 按 start_frame 排序
+        sorted_objs = sorted(objs, key=lambda x: x["start_frame"])
+
+        # 初始化第一个区间
+        cur_start = sorted_objs[0]["start_frame"]
+        cur_end = sorted_objs[0]["end_frame"]
+        cur_level = sorted_objs[0]["level"]
+
+        for obj in sorted_objs[1:]:
+            # 如果当前区间与下一个区间重叠或相连（end >= start）
+            if obj["start_frame"] <= cur_end:
+                # 合并区间：取结束帧的最大值
+                cur_end = max(cur_end, obj["end_frame"])
+                # 级别取最大值（如果级别不同）
+                cur_level = max(cur_level, obj["level"])
+            else:
+                # 当前区间结束，保存合并结果
+                merged_objects.append({
+                    "hazard_track_id": new_id,
+                    "tag_id": tag_id,
+                    "class_id": class_id,
+                    "level": cur_level,
+                    "start_frame": cur_start,
+                    "end_frame": cur_end
+                })
+                new_id += 1
+                # 开始新的区间
+                cur_start = obj["start_frame"]
+                cur_end = obj["end_frame"]
+    data["objects"] = merged_objects
+    return data
+
+def get_hazard_level_from_rule(rule_definitions: dict, class_name: str, tag_name: str) -> str:
+    """
+    从知识库规则中获取隐患等级。
+
+    参数:
+        rule_definitions (dict): 隐患规则定义
+        class_name (str): 隐患类别名称（如"灭火器"）
+        tag_name (str): 隐患标签名称（如"灭火器被遮挡"）
+
+    返回:
+        str: 隐患等级（如"重大隐患"），未找到时返回"一般隐患"
+    """
+    class_rules = rule_definitions.get(class_name, {})
+    if not class_rules:
+        return "一般隐患"
+
+    for level_name, level_content in class_rules.items():
+        if isinstance(level_content, dict) and tag_name in level_content:
+            return level_name
+
+    return "一般隐患"
+
+
+def map_original_level(level: int) -> str:
+    """
+    将原始 level 映射为相似度描述。
+
+    参数:
+        level (int): 原始等级（1表示一般，2表示明显）
+
+    返回:
+        str: 相似度描述（"疑似"或"高度相似"）
+    """
+    return "高度相似" if level == 2 else "疑似"
+
+
+def report_generator(
+    video_path: str,
+    detection_data: dict,
+    hazard_results: dict,
+    rule_definitions: dict,
+    output_path: str = "output",
+    frame_interval: float = 1.0
+):
+    """
+    生成隐患报告的主函数。
+
+    参数:
+        video_path (str): 视频文件路径
+        detection_data (dict): 物体检测数据，格式为 {frame_id: [detection_info, ...]}
+        hazard_results (dict): 隐患检查结果
+        rule_definitions (dict): 隐患规则定义
+        output_path (str): 输出文件夹路径，默认 'output'
+        frame_interval (float): 帧间隔时间（秒），用于计算实际帧号，默认 1.0
+
+    功能:
+        1. 创建必要的文件夹结构
+        2. 根据隐患时间范围截取对应的图片
+        3. 生成 Markdown 报告
+    """
+
+    # ----------------------- 1. 准备工作 -----------------------
+    # 创建文件夹结构
+    report_dir = os.path.join(output_path, "report")
+    assets_dir = os.path.join(report_dir, "_assets")
+    os.makedirs(assets_dir, exist_ok=True)
+
+    # 打开视频文件
+    cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        raise IOError(f"无法打开视频文件: {video_path}")
+
+    # 获取视频总帧数（用于安全检查）
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    # ----------------------- 2. 处理隐患数据并截取图片 -----------------------
+    # 用于存储 Markdown 表格的行数据
+    md_table_rows = []
+
+    # 遍历所有隐患对象
+    for idx, obj in enumerate(hazard_results["objects"], start=1):
+        # 计算实际的起始帧（考虑帧间隔）
+        start_frame = int(obj["start_frame"] * frame_interval)
+
+        # 防止帧号越界
+        start_frame = max(0, min(start_frame, total_frames - 1))
+
+        # 获取隐患对应的类名和标签
+        class_name = hazard_results["class"][obj["class_id"]]
+        tag_name = hazard_results["tag"][obj["tag_id"]]
+        original_level = obj["level"]
+        location = obj.get("location", "")
+
+        # 从知识库规则中获取隐患等级
+        rule_level = get_hazard_level_from_rule(rule_definitions, class_name, tag_name)
+        # 将原始 level 映射为相似度描述
+        similarity = map_original_level(original_level)
+
+        # ----------------------- 2.1. 截取图片 -----------------------
+        # 默认选择 start_frame 作为截图帧
+        target_frame = start_frame
+
+        # 截图并保存
+        img_filename = f"{idx}_{target_frame}.jpg"
+        img_path = os.path.join(assets_dir, img_filename)
+
+        # 读取对应帧并保存整帧图片
+        cap.set(cv2.CAP_PROP_POS_FRAMES, target_frame)
+        ret, frame = cap.read()
+        if ret:
+            cv2.imwrite(img_path, frame)
+
+        # ----------------------- 2.2. 生成 Markdown 表格行 -----------------------
+        # 生成图片引用的 Markdown 语法
+        img_markdown = f"![](./_assets/{img_filename})"
+
+        # 只取前 2 条依据作为展示
+        base_text = hazard_results["base"][obj["base_id"]]
+
+        # 组装表格行
+        row = f"|{idx}|{rule_level}|{similarity}|{base_text}|{start_frame}|{location}|{img_markdown}|"
+        md_table_rows.append(row)
+
+    # ----------------------- 3. 生成 Markdown 报告 -----------------------
+    md_content = f"""# 隐患报告
+
+## 隐患清单
+
+| 隐患序号 | 隐患等级 | 相似度 | 依据 | 开始帧 | 位置描述 | 照片 |
+| :---: | :---: | :---: | :--- | :---: | :--- | :---: |
+"""
+    md_content += "\n".join(md_table_rows)
+
+    # 写入报告文件
+    report_path = os.path.join(report_dir, "report.md")
+    with open(report_path, "w", encoding="utf-8") as f:
+        f.write(md_content)
+
+    # 释放资源
+    cap.release()
+    print(f"报告已生成: {report_path}")
\ No newline at end of file
diff --git a/lib/qwen_fun_vid.py b/lib/qwen_fun_vid.py
new file mode 100644
index 0000000..5346a2e
--- /dev/null
+++ b/lib/qwen_fun_vid.py
@@ -0,0 +1,491 @@
+import os
+from pathlib import Path
+import numpy as np
+import supervision as sv
+import cv2
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+def generate_video_with_boxes(
+    boxes_data: list[dict],
+    input_video_path: str,
+    output_video_path: str,
+    frame_annotation_interval: int
+) -> None:
+    """
+    将提供的标注数据渲染到视频上，支持按帧间隔进行标注。
+
+    :param boxes_data: 包含标注信息的列表，每个元素结构为:
+        {
+            "frame_id": int,
+            "boxes": List[Tuple[int, int, int, int, str]]  # (x1, y1, x2, y2, label)
+        }
+    :param input_video_path: 输入视频文件路径
+    :param output_video_path: 输出视频文件路径
+    :param frame_annotation_interval: 标注间隔（单位：帧），默认为 1（每帧标注）
+    :return: 无
+    """
+    # -------------------------------------------------
+    # 1. 视频读取与基本配置
+    # -------------------------------------------------
+    cap = cv2.VideoCapture(input_video_path)
+    if not cap.isOpened():
+        raise FileNotFoundError(f"无法打开视频文件或流: {input_video_path}")
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    video_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    video_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+    # -------------------------------------------------
+    # 2. 初始化 VideoWriter
+    # -------------------------------------------------
+    Path(os.path.dirname(output_video_path)).mkdir(parents=True, exist_ok=True)
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v') # type: ignore
+    out = cv2.VideoWriter(output_video_path, fourcc, fps, (video_width, video_height))
+
+    # -------------------------------------------------
+    # 3. 初始化标注工具
+    # -------------------------------------------------
+    box_annotator = sv.BoxAnnotator()
+    label_annotator = sv.RichLabelAnnotator(
+        font_path="C:/Windows/Fonts/simhei.ttf",
+        text_color=sv.Color.WHITE,
+        text_padding=5,
+        font_size=20
+    )
+
+    # -------------------------------------------------
+    # 4. 数据预处理：构建 frame_id -> boxes 的映射
+    # -------------------------------------------------
+    frame_to_boxes: dict[int, list[tuple[int, int, int, int, str]]] = {}
+    for entry in boxes_data:
+        frame_id = entry["frame_id"]
+        boxes = entry["boxes"]
+        # 确保每个框都有正确的结构
+        cleaned_boxes = []
+        for box in boxes:
+            if len(box) == 5:
+                cleaned_boxes.append(box)  # (x1, y1, x2, y2, label)
+        frame_to_boxes[frame_id] = cleaned_boxes
+
+    # -------------------------------------------------
+    # 5. 主循环：逐帧读取并渲染
+    # -------------------------------------------------
+    frame_idx = 0
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        # 计算当前帧对应的标注帧索引（考虑间隔）
+        annotation_frame_idx = frame_idx // frame_annotation_interval
+
+        # 如果当前帧没有标注数据，直接写入原始帧
+        if annotation_frame_idx not in frame_to_boxes:
+            out.write(frame)
+            frame_idx += 1
+            continue
+
+        # 获取当前帧的所有框信息
+        boxes_info = frame_to_boxes[annotation_frame_idx]
+
+        boxes = []
+        labels = []
+        for _, (x1, y1, x2, y2, label) in enumerate(boxes_info):
+            # 坐标
+            boxes.append([x1, y1, x2, y2])
+            # 使用索引作为唯一标识符，或直接使用 label
+            labels.append(label)
+
+        # 转换为 NumPy 数组
+        boxes_np = np.array(boxes, dtype=np.float64)
+
+        # 构建 Detections 对象
+        detections = sv.Detections(
+            xyxy=boxes_np,
+            confidence=np.ones(len(boxes_np)),  # 默认置信度为 1.0
+            class_id=np.zeros(len(boxes_np), dtype=int)  # 类别 ID 在此场景下不重要
+        )
+        detections.tracker_id = np.arange(len(boxes_np), dtype=int)  # 使用索引作为 ID
+
+        # 绘制边框和标签
+        annotated_frame = box_annotator.annotate(scene=frame.copy(), detections=detections)
+        annotated_frame = label_annotator.annotate(
+            scene=annotated_frame,
+            detections=detections,
+            labels=labels
+        )
+
+        # 写入帧
+        out.write(annotated_frame)
+        frame_idx += 1
+
+    # -------------------------------------------------
+    # 6. 资源释放
+    # -------------------------------------------------
+    cap.release()
+    out.release()
+    cv2.destroyAllWindows()
+    print(f"视频渲染完成，已保存至: {output_video_path}")
+
+def process_track_id(
+    track_id: int,
+    frame_list: list[tuple[int, list[int]]],
+    input_video_path: str,
+    output_video_root: str,
+    frame_width: int,
+    frame_height: int,
+    target_fps: int,
+    frame_interval: int
+) -> str:
+    """
+    处理单个track_id，生成对应的视频
+    """
+    # 计算需要生成的总帧数（确保覆盖所有物体帧且不少于两秒）
+    min_frames_for_2s = 25 * 2  # 2秒 @ 25fps
+    object_based_frames = len(frame_list) * frame_interval
+    max_output_frame = max(object_based_frames, min_frames_for_2s)
+
+    # 创建输出视频路径
+    output_path = os.path.join(output_video_root, f"{track_id}.mp4")
+
+    # 尝试使用GPU硬件编码
+    try:
+        # 对于不同平台的GPU编码，使用不同的fourcc
+        # Windows平台使用h264_nvenc或h264_amf
+        # 如果GPU编码不可用，会回退到CPU编码
+        fourcc = cv2.VideoWriter_fourcc(*'h264') # type: ignore
+        out = cv2.VideoWriter(output_path, fourcc, target_fps, (frame_width, frame_height))
+        # 检查是否成功打开
+        if not out.isOpened():
+            # 尝试其他编码方式
+            fourcc = cv2.VideoWriter_fourcc(*'mp4v') # type: ignore
+            out = cv2.VideoWriter(output_path, fourcc, target_fps, (frame_width, frame_height))
+    except Exception:
+        # 异常时使用CPU编码
+        fourcc = cv2.VideoWriter_fourcc(*'mp4v') # type: ignore
+        out = cv2.VideoWriter(output_path, fourcc, target_fps, (frame_width, frame_height))
+
+    if not out.isOpened():
+        print(f"无法创建视频文件: {output_path}")
+        return f"失败: {output_path}"
+
+    # 打开原视频（每个线程独立打开，避免线程安全问题）
+    cap = cv2.VideoCapture(input_video_path)
+    if not cap.isOpened():
+        out.release()
+        return f"失败: 无法打开视频 {input_video_path}"
+
+    # 生成视频帧
+    current_output_frame = 0
+    obj_frame_idx = 0
+
+    while current_output_frame < max_output_frame:
+        # 检查当前输出帧是否是5的倍数
+        if current_output_frame % frame_interval == 0 and obj_frame_idx < len(frame_list):
+            # 这是需要放置物体帧的位置
+            original_frame_id, xyxy = frame_list[obj_frame_idx]
+
+            # 设置原视频读取位置
+            cap.set(cv2.CAP_PROP_POS_FRAMES, original_frame_id)
+            ret, frame = cap.read()
+
+            if not ret:
+                # 读取失败，使用黑色帧
+                output_frame = np.zeros((frame_height, frame_width, 3), dtype=np.uint8)
+            else:
+                # 有数据，截取对应区域
+                x1, y1, x2, y2 = map(int, xyxy)
+                # 确保坐标在有效范围内
+                x1 = max(0, min(x1, frame_width))
+                y1 = max(0, min(y1, frame_height))
+                x2 = max(0, min(x2, frame_width))
+                y2 = max(0, min(y2, frame_height))
+
+                # 创建黑色背景
+                output_frame = np.zeros((frame_height, frame_width, 3), dtype=np.uint8)
+                # 将截取的区域放到输出帧中（保持原位置）
+                if x2 > x1 and y2 > y1:
+                    cropped = frame[y1:y2, x1:x2]
+                    output_frame[y1:y2, x1:x2] = cropped
+
+            # 移到下一个物体帧
+            obj_frame_idx += 1
+        else:
+            # 剩余帧留黑
+            output_frame = np.zeros((frame_height, frame_width, 3), dtype=np.uint8)
+
+        # 写入帧
+        out.write(output_frame)
+        current_output_frame += 1
+
+    # 释放资源
+    out.release()
+    cap.release()
+    print(f"已生成视频: {output_path}, 共 {current_output_frame} 帧")
+    return f"成功: {output_path}"
+
+
+def frame_all_to_obj_vid(
+    json_data: dict,
+    input_video_path: str,
+    output_video_root: str,
+) -> None:
+    """
+    根据标注数据从原视频中截取物体，生成ai读取专用视频
+
+    参数:
+        json_data: 标注数据
+        input_video_path: 原视频路径
+        output_video_root: 输出视频根目录
+    """
+    # 确保输出目录存在
+    os.makedirs(output_video_root, exist_ok=True)
+
+    # 1. 从 json_data 中提取数据，按 track_id 组织
+    track_dict: dict[int, list[tuple[int, list[int]]]] = {}
+    # 遍历每一帧
+    for frame_id_str, detections in json_data.items():
+        frame_id = int(frame_id_str)
+        for det in detections:
+            track_id = det.get("track_id", -1)
+            xyxy = det.get("xyxy", [0, 0, 0, 0])
+            if track_id not in track_dict:
+                track_dict[track_id] = []
+            track_dict[track_id].append((frame_id, xyxy))
+
+    # 2. 获取视频信息（只需要获取一次）
+    temp_cap = cv2.VideoCapture(input_video_path)
+    if not temp_cap.isOpened():
+        raise ValueError(f"无法打开视频: {input_video_path}")
+
+    frame_width = int(temp_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    frame_height = int(temp_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    temp_cap.release()
+
+    # 目标fps为25，每5帧取一帧（0, 5, 10...）
+    target_fps = 25
+    frame_interval = 5  # 每隔5帧取一帧
+
+    # 3. 使用多线程并行处理多个track_id
+    # 根据CPU核心数设置线程池大小
+    max_workers = min(os.cpu_count() or 4, len(track_dict))
+    print(f"使用 {max_workers} 个线程并行处理")
+
+    with ThreadPoolExecutor(max_workers=max_workers) as executor:
+        # 提交所有任务
+        future_to_track = {
+            executor.submit(
+                process_track_id,
+                track_id,
+                frame_list,
+                input_video_path,
+                output_video_root,
+                frame_width,
+                frame_height,
+                target_fps,
+                frame_interval
+            ):
+            track_id for track_id, frame_list in track_dict.items()
+        }
+
+        # 等待所有任务完成
+        for future in as_completed(future_to_track):
+            track_id = future_to_track[future]
+            try:
+                result = future.result()
+                print(f"Track ID {track_id}: {result}")
+            except Exception as e:
+                print(f"Track ID {track_id} 处理失败: {str(e)}")
+
+# def frame_all_to_obj_vid(
+#     json_data: dict,
+#     input_video_path: str,
+#     output_video_root: str,
+# ) -> None:
+#     """
+#     根据标注数据从原视频中截取物体，生成ai读取专用视频
+
+#     参数:
+#         json_data: 标注数据
+#         input_video_path: 原视频路径
+#         output_video_root: 输出视频根目录
+#     """
+#     # 确保输出目录存在
+#     os.makedirs(output_video_root, exist_ok=True)
+
+#     # 1. 从 json_data 中提取数据，按 track_id 组织
+#     track_dict: dict[int, list[tuple[int, list[int]]]] = {}
+#     # 遍历每一帧
+#     for frame_id_str, detections in json_data.items():
+#         frame_id = int(frame_id_str)
+#         for det in detections:
+#             track_id = det.get("track_id", -1)
+#             xyxy = det.get("xyxy", [0, 0, 0, 0])
+#             if track_id not in track_dict:
+#                 track_dict[track_id] = []
+#             track_dict[track_id].append((frame_id, xyxy))
+
+#     # 2. 打开原视频
+#     cap = cv2.VideoCapture(input_video_path)
+#     if not cap.isOpened():
+#         raise ValueError(f"无法打开视频: {input_video_path}")
+
+#     # 获取原视频信息
+#     original_fps = cap.get(cv2.CAP_PROP_FPS)
+#     total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+#     frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+#     frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+#     # 目标fps为25，每5帧取一帧（0, 5, 10...）
+#     target_fps = 25
+#     frame_interval = 5  # 每隔5帧取一帧
+
+#     # 为每个 track_id 生成视频
+#     for track_id, frame_list in track_dict.items():
+#         # 计算需要生成的总帧数（确保覆盖所有物体帧且不少于两秒）
+#         min_frames_for_2s = 25 * 2  # 2秒 @ 25fps
+#         object_based_frames = len(frame_list) * frame_interval
+#         max_output_frame = max(object_based_frames, min_frames_for_2s)
+
+#         # 创建输出视频路径
+#         output_path = os.path.join(output_video_root, f"{track_id}.mp4")
+
+#         # 创建视频写入器
+#         fourcc = cv2.VideoWriter_fourcc(*'mp4v') # type: ignore
+#         out = cv2.VideoWriter(output_path, fourcc, target_fps, (frame_width, frame_height))
+
+#         if not out.isOpened():
+#             print(f"无法创建视频文件: {output_path}")
+#             continue
+
+#         # 生成视频帧
+#         current_output_frame = 0
+#         obj_frame_idx = 0
+
+#         while current_output_frame < max_output_frame:
+#             # 检查当前输出帧是否是5的倍数
+#             if current_output_frame % frame_interval == 0 and obj_frame_idx < len(frame_list):
+#                 # 这是需要放置物体帧的位置
+#                 original_frame_id, xyxy = frame_list[obj_frame_idx]
+
+#                 # 设置原视频读取位置
+#                 cap.set(cv2.CAP_PROP_POS_FRAMES, original_frame_id)
+#                 ret, frame = cap.read()
+
+#                 if not ret:
+#                     # 读取失败，使用黑色帧
+#                     output_frame = np.zeros((frame_height, frame_width, 3), dtype=np.uint8)
+#                 else:
+#                     # 有数据，截取对应区域
+#                     x1, y1, x2, y2 = map(int, xyxy)
+#                     # 确保坐标在有效范围内
+#                     x1 = max(0, min(x1, frame_width))
+#                     y1 = max(0, min(y1, frame_height))
+#                     x2 = max(0, min(x2, frame_width))
+#                     y2 = max(0, min(y2, frame_height))
+
+#                     # 创建黑色背景
+#                     output_frame = np.zeros((frame_height, frame_width, 3), dtype=np.uint8)
+#                     # 将截取的区域放到输出帧中（保持原位置）
+#                     if x2 > x1 and y2 > y1:
+#                         cropped = frame[y1:y2, x1:x2]
+#                         output_frame[y1:y2, x1:x2] = cropped
+
+#                 # 移到下一个物体帧
+#                 obj_frame_idx += 1
+#             else:
+#                 # 剩余帧留黑
+#                 output_frame = np.zeros((frame_height, frame_width, 3), dtype=np.uint8)
+
+#             # 写入帧
+#             out.write(output_frame)
+#             current_output_frame += 1
+
+#         # 释放视频写入器
+#         out.release()
+#         print(f"已生成视频: {output_path}, 共 {current_output_frame} 帧")
+
+#     # 释放原视频
+#     cap.release()
+
+
+def create_mian_vid_for_ai(
+    input_video_path: str,
+    output_folder: str
+) -> str:
+    """
+    将原始视频的第0,1,2...帧映射到新视频的0,5,10...帧，其他帧留黑
+
+    参数:
+        input_video_path: 原始视频路径
+        output_folder: 输出文件夹路径
+    返回:
+        str: 输出视频路径
+    """
+    # 确保输出目录存在
+    os.makedirs(output_folder, exist_ok=True)
+
+    # 构建输出视频路径
+    output_video_path = os.path.join(output_folder, "mian_vid_ai.mp4")
+
+    # 打开原视频
+    cap = cv2.VideoCapture(input_video_path)
+    if not cap.isOpened():
+        raise ValueError(f"无法打开视频: {input_video_path}")
+
+    # 获取原视频信息
+    original_fps = cap.get(cv2.CAP_PROP_FPS)
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+    # 目标fps为25，每5帧取一帧（0, 5, 10...）
+    target_fps = 25
+    frame_interval = 5
+
+    # 计算输出视频的总帧数
+    # 确保覆盖所有原始帧且不少于两秒
+    min_frames_for_2s = 25 * 2  # 2秒 @ 25fps
+    max_output_frame = max(total_frames * frame_interval, min_frames_for_2s)
+
+    # 创建视频写入器
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v') # type: ignore
+    out = cv2.VideoWriter(output_video_path, fourcc, target_fps, (frame_width, frame_height))
+
+    if not out.isOpened():
+        raise ValueError(f"无法创建视频文件: {output_video_path}")
+
+    # 生成视频帧
+    current_output_frame = 0
+    original_frame_idx = 0
+
+    while current_output_frame < max_output_frame:
+        # 检查当前输出帧是否是5的倍数
+        if current_output_frame % frame_interval == 0 and original_frame_idx < total_frames:
+            # 这是需要放置原始帧的位置
+            # 设置原视频读取位置
+            cap.set(cv2.CAP_PROP_POS_FRAMES, original_frame_idx)
+            ret, frame = cap.read()
+
+            if not ret:
+                # 读取失败，使用黑色帧
+                output_frame = np.zeros((frame_height, frame_width, 3), dtype=np.uint8)
+            else:
+                # 有数据，使用原始帧
+                output_frame = frame
+
+            # 移到下一个原始帧
+            original_frame_idx += 1
+        else:
+            # 剩余帧留黑
+            output_frame = np.zeros((frame_height, frame_width, 3), dtype=np.uint8)
+
+        # 写入帧
+        out.write(output_frame)
+        current_output_frame += 1
+
+    # 释放资源
+    cap.release()
+    out.release()
+    print(f"已生成视频: {output_video_path}, 共 {current_output_frame} 帧")
+
+    return output_video_path
\ No newline at end of file
diff --git a/lib/sam3 copy.py b/lib/sam3 copy.py
new file mode 100644
index 0000000..364164d
--- /dev/null
+++ b/lib/sam3 copy.py	
@@ -0,0 +1,303 @@
+import os
+from tkinter import N
+from ultralytics.models.sam import SAM3VideoSemanticPredictor
+import cv2
+import json
+import numpy as np
+from pathlib import Path
+
+class SAM3:
+    def __init__(self):
+        self._data: dict = {}
+
+    def run(self, vid_path: str, output_dir: str, class_file_path: str, conf: float = 0.6) -> dict:
+        """
+        运行 SAM3 模型，返回视频中每个帧的检测结果。
+        参数:
+            vid_path: 视频路径
+            output_dir: 输出目录
+            class_file_path: 类别文件路径
+        返回:
+            每个帧的检测结果字典，键为帧索引，值为检测结果列表
+
+        字典结构:
+        {
+            "0": [ # frame_idx
+                {
+                    "xyxy": [x1, y1, x2, y2],  # 检测框坐标
+                    "confidence": 0.9,  # 置信度
+                    "track_id": 123,  # 跟踪ID
+                    "class_id": 0,  # 类别ID
+                    "class_str": "class_name"  # 类别名称
+                },
+                ...
+            ]
+        ,
+        }
+        """
+        # new_vid_path = extract_frames_to_video(vid_path, output_dir, interval)
+
+        self._data = {}
+        text_dict = json.load(open(class_file_path, "r", encoding="utf-8"))
+        text_list_en = list(text_dict.keys())
+        text_list_cn = list(text_dict.values())
+
+        overrides = dict(conf=conf, task="segment", mode="predict", imgsz=640, model="./model/sam3.pt", half=True, save=True, device=0)
+        predictor = SAM3VideoSemanticPredictor(overrides=overrides)
+
+        results = predictor(
+            source=vid_path,
+            text=text_list_en,
+            stream=True,
+        )
+
+        frame_idx = 0
+
+
+
+        # Path(f"{output_dir}/json").mkdir(parents=True, exist_ok=True)
+        annotated_frames: dict = {}
+        for r in results:
+            frame_data = []
+
+            if r.boxes is not None:
+                boxes = r.boxes.xyxy.cpu().numpy()
+                confs = r.boxes.conf.cpu().numpy()
+                cls_ids = r.boxes.cls.cpu().numpy()
+                track_ids = r.boxes.id.cpu().numpy()
+
+                for i in range(len(boxes)):
+                    frame_data.append({
+                        "xyxy": boxes[i].tolist(),
+                        "confidence": float(confs[i]),
+                        "track_id": int(track_ids[i]),
+                        "class_id": int(cls_ids[i]),
+                        "class_str": text_list_cn[int(cls_ids[i])]
+                    })
+
+            annotated_frames[frame_idx] = frame_data
+            # with open(f"./output/json/frame_{frame_idx:04d}.json", "w", encoding="utf-8") as f:
+            #     json.dump(frame_data, f, ensure_ascii=False, indent=2)
+
+            # 保存源图
+            source_frame = r.orig_img
+            source_path = f"{output_dir}/source/frame_{frame_idx:04d}.jpg"
+            Path(f"{output_dir}/source").mkdir(parents=True, exist_ok=True)
+            cv2.imwrite(source_path, source_frame)
+
+            # 保存标记图
+            mask_frame = r.plot()
+            mask_path = f"{output_dir}/boxes/frame_{frame_idx:04d}.jpg"
+            Path(f"{output_dir}/boxes").mkdir(parents=True, exist_ok=True)
+            cv2.imwrite(mask_path, mask_frame)
+
+            print(f"Frame {frame_idx}: {len(frame_data)} boxes")
+
+            # annotated_frame = r.plot()
+            frame_idx += 1
+
+        with open(f"{output_dir}/frame_all.json", "w", encoding="utf-8") as f:
+                json.dump(annotated_frames, f, ensure_ascii=False, indent=2)
+
+
+
+        print(f"\n总共处理 {frame_idx} 帧")
+        print(f"结果已保存到 {output_dir}/frame_all.json")
+        print(f"标记图片已保存到 {output_dir}/boxes/")
+
+        cv2.destroyAllWindows()
+
+        self._data = annotated_frames
+        return annotated_frames
+
+    def load_from_json(self, json_path: str) -> dict:
+        """
+        从 JSON 文件加载检测结果
+        参数:
+            json_path: JSON 文件路径
+        返回:
+            每个帧的检测结果字典，键为帧索引，值为检测结果列表
+
+        字典结构:
+        {
+            "0": [ # frame_idx
+                {
+                    "xyxy": [x1, y1, x2, y2],  # 检测框坐标
+                    "confidence": 0.9,  # 置信度
+                    "track_id": 123,  # 跟踪ID
+                    "class_id": 0,  # 类别ID
+                    "class_str": "class_name"  # 类别名称
+                },
+                ...
+            ]
+        ,
+        }
+        """
+        with open(json_path, "r", encoding="utf-8") as f:
+            self._data = json.load(f)
+            return self._data
+
+    def get_class_dict(self):
+        """
+        只保留每帧的类列表，不包含其他信息
+
+        格式1：
+        {
+            "class_list": ["class_name1", "class_name2", ..."],
+            "frame_list": {
+                "0": [ # frame_idx
+                    0,  # 类别ID
+                    1,  # 类别ID
+                    ...
+                ],
+            },
+        }
+        """
+        if self._data is None:
+            raise ValueError("请先调用 run() 方法运行模型")
+
+        # 收集所有类别名称并去重
+        class_set = set() # 无序，唯一
+        for frame_data in self._data.values():
+            for item in frame_data:
+                class_set.add(item["class_str"])
+        class_list = sorted(list(class_set))
+
+        # 构建每帧的类别ID列表
+        frame_list = {}
+        for frame_idx, frame_data in self._data.items():
+            class_ids = set() # 无序，唯一
+            for item in frame_data:
+                class_name = item["class_str"]
+                class_id = class_list.index(class_name)
+                class_ids.add(class_id)
+            frame_list[str(frame_idx)] = sorted(list(class_ids))
+
+        return {
+            "class_list": class_list,
+            "frame_list": frame_list
+        }
+
+    def data(self):
+        return self._data
+
+def extract_frames_to_video(video_path, output_dir, interval=1):
+    """
+    抽帧生成新视频（无任何图像处理）
+
+    参数:
+    - video_path: 原始视频文件路径
+    - output_dir: 输出文件夹路径（新视频将保存在此文件夹）
+    - interval: 抽帧间隔（如 6 表示每隔 5 帧抽取 1 帧）
+    """
+    print(f"开始抽帧: {video_path}")
+    print(f"抽帧间隔: {interval}")
+    print(f"输出目录: {output_dir}")
+    # 确保输出目录存在
+    Path(output_dir).mkdir(parents=True, exist_ok=True)
+
+    # 1. 打开原始视频
+    # 尝试使用硬件加速后端
+    backends = [
+        (cv2.CAP_FFMPEG, 'FFmpeg'),     # 通用后端，支持硬件加速
+        (cv2.CAP_DSHOW, 'DirectShow'),  # Windows 硬件加速
+        (cv2.CAP_ANY, 'Default')        # 默认后端
+    ]
+
+    cap = None
+    for backend, backend_name in backends:
+        try:
+            cap = cv2.VideoCapture(video_path, backend)
+            if cap.isOpened():
+                # 尝试启用硬件加速
+                if backend == cv2.CAP_FFMPEG:
+                    # 设置FFmpeg硬件加速
+                    try:
+                        cap.set(cv2.CAP_PROP_HW_ACCELERATION, cv2.VIDEO_ACCELERATION_ANY)
+                        # 检查硬件加速是否启用
+                        hw_accel = cap.get(cv2.CAP_PROP_HW_ACCELERATION)
+                        print(f"FFmpeg硬件加速: {'已启用' if hw_accel > 0 else '未启用'}")
+                    except Exception as e:
+                        print(f"设置硬件加速失败: {e}")
+                print(f"使用后端: {backend_name}")
+                break
+        except Exception as e:
+            print(f"尝试{backend_name}后端失败: {e}")
+            continue
+
+    if not cap or not cap.isOpened():
+        raise Exception(f"无法打开视频文件: {video_path}")
+
+    # 2. 获取视频参数
+    fps = cap.get(cv2.CAP_PROP_FPS)          # 原始帧率
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    # 3. 计算新视频的帧率（保持原始播放速度）
+    # 新视频的帧率 = 原始帧率 / 抽帧间隔
+    new_fps = fps / max(interval, 1)
+
+    # 4. 初始化视频写入器
+    output_path = os.path.join(output_dir, "output_video.mp4")
+    # 使用更高效的编码格式
+    fourcc = cv2.VideoWriter_fourcc(*'avc1') # type: ignore # H.264 编码，更高效
+
+    # 尝试启用硬件加速写入
+    try:
+        # 尝试使用硬件加速的VideoWriter
+        out = cv2.VideoWriter(output_path, fourcc, new_fps, (width, height), isColor=True)
+        print("视频写入器初始化成功")
+    except Exception as e:
+        print(f"硬件加速写入失败，使用默认方式: {e}")
+        out = cv2.VideoWriter(output_path, fourcc, new_fps, (width, height))
+
+    # 5. 开始抽帧并写入新视频
+    frame_idx = 0
+    saved_frames = 0
+    print_interval = max(100, total_frames // 10)  # 每处理100帧或10%进度打印一次
+
+    while cap.isOpened():
+        # 优化：对于不需要的帧，使用grab()跳过，只对需要的帧使用retrieve()
+        if frame_idx % interval == 0:
+            # 需要处理的帧，使用read()获取
+            ret, frame = cap.read()
+            if not ret:
+                break
+            out.write(frame)  # 写入新视频
+            saved_frames += 1
+        else:
+            # 不需要处理的帧，使用grab()跳过（更快）
+            ret = cap.grab()
+            if not ret:
+                break
+
+        frame_idx += 1
+
+        # 减少打印频率
+        if frame_idx % print_interval == 0:
+            progress = (frame_idx / total_frames) * 100
+            print(f"已处理帧 {frame_idx}/{total_frames} ({progress:.1f}%)")
+
+    # 6. 释放资源
+    cap.release()
+    out.release()
+    cv2.destroyAllWindows()
+
+    print(f"抽帧完成: 原视频 {total_frames} 帧, 抽取后 {saved_frames} 帧")
+    print(f"新视频已保存至: {output_path}")
+    print(f"新视频帧率: {new_fps:.2f} fps (原始帧率: {fps:.2f} fps)")
+
+    return output_path
+
+
+if __name__ == "__main__":
+    VID_NAME: str = "Peihdianhxiang"
+    VID_END: str = ".mp4"
+    conf: float = 0.6
+
+    output_dir: str = f"output/{VID_NAME}"
+    vid_path: str = f"./input/{VID_NAME}{VID_END}"
+
+    sam3: SAM3 = SAM3()
+    sam3.run(vid_path, output_dir, "1.厂房防火.json", conf)
\ No newline at end of file
diff --git a/lib/sam3.py b/lib/sam3.py
new file mode 100644
index 0000000..d92b7b2
--- /dev/null
+++ b/lib/sam3.py
@@ -0,0 +1,291 @@
+import os
+from tkinter import N
+from ultralytics.models.sam import SAM3VideoSemanticPredictor
+import cv2
+import json
+import numpy as np
+from pathlib import Path
+
+class SAM3:
+    def __init__(self):
+        self._data: dict = {}
+
+    def run(self, vid_path: str, output_dir: str, class_file_path: str, interval: int = 1, conf: float = 0.6) -> dict:
+        """
+        运行 SAM3 模型，返回视频中每个帧的检测结果。
+        参数:
+            vid_path: 视频路径
+            output_dir: 输出目录
+            class_file_path: 类别文件路径
+        返回:
+            每个帧的检测结果字典，键为帧索引，值为检测结果列表
+
+        字典结构:
+        {
+            "0": [ # frame_idx
+                {
+                    "xyxy": [x1, y1, x2, y2],  # 检测框坐标
+                    "confidence": 0.9,  # 置信度
+                    "track_id": 123,  # 跟踪ID
+                    "class_id": 0,  # 类别ID
+                    "class_str": "class_name"  # 类别名称
+                },
+                ...
+            ]
+        ,
+        }
+        """
+        new_vid_path = extract_frames_to_video(vid_path, output_dir, interval)
+
+        self._data = {}
+        text_dict = json.load(open(class_file_path, "r", encoding="utf-8"))
+        text_list_en = list(text_dict.keys())
+        text_list_cn = list(text_dict.values())
+
+        overrides = dict(conf=conf, task="segment", mode="predict", imgsz=640, model="./model/sam3.pt", half=True, save=True, device=0)
+        predictor = SAM3VideoSemanticPredictor(overrides=overrides)
+
+        results = predictor(
+            source=new_vid_path,
+            text=text_list_en,
+            stream=True,
+        )
+
+        frame_idx = 0
+
+
+
+        # Path(f"{output_dir}/json").mkdir(parents=True, exist_ok=True)
+        annotated_frames: dict = {}
+        for r in results:
+            frame_data = []
+
+            if r.boxes is not None:
+                boxes = r.boxes.xyxy.cpu().numpy()
+                confs = r.boxes.conf.cpu().numpy()
+                cls_ids = r.boxes.cls.cpu().numpy()
+                track_ids = r.boxes.id.cpu().numpy()
+
+                for i in range(len(boxes)):
+                    frame_data.append({
+                        "xyxy": boxes[i].tolist(),
+                        "confidence": float(confs[i]),
+                        "track_id": int(track_ids[i]),
+                        "class_id": int(cls_ids[i]),
+                        "class_str": text_list_cn[int(cls_ids[i])]
+                    })
+
+            annotated_frames[frame_idx] = frame_data
+            # with open(f"./output/json/frame_{frame_idx:04d}.json", "w", encoding="utf-8") as f:
+            #     json.dump(frame_data, f, ensure_ascii=False, indent=2)
+
+            # 保存源图
+            source_frame = r.orig_img
+            source_path = f"{output_dir}/source/frame_{frame_idx:04d}.jpg"
+            Path(f"{output_dir}/source").mkdir(parents=True, exist_ok=True)
+            cv2.imwrite(source_path, source_frame)
+
+            # 保存标记图
+            mask_frame = r.plot()
+            mask_path = f"{output_dir}/boxes/frame_{frame_idx:04d}.jpg"
+            Path(f"{output_dir}/boxes").mkdir(parents=True, exist_ok=True)
+            cv2.imwrite(mask_path, mask_frame)
+
+            print(f"Frame {frame_idx}: {len(frame_data)} boxes")
+
+            # annotated_frame = r.plot()
+            frame_idx += 1
+
+        with open(f"{output_dir}/frame_all.json", "w", encoding="utf-8") as f:
+                json.dump(annotated_frames, f, ensure_ascii=False, indent=2)
+
+
+
+        print(f"\n总共处理 {frame_idx} 帧")
+        print(f"结果已保存到 {output_dir}/frame_all.json")
+        print(f"标记图片已保存到 {output_dir}/boxes/")
+
+        cv2.destroyAllWindows()
+
+        self._data = annotated_frames
+        return annotated_frames
+
+    def load_from_json(self, json_path: str) -> dict:
+        """
+        从 JSON 文件加载检测结果
+        参数:
+            json_path: JSON 文件路径
+        返回:
+            每个帧的检测结果字典，键为帧索引，值为检测结果列表
+
+        字典结构:
+        {
+            "0": [ # frame_idx
+                {
+                    "xyxy": [x1, y1, x2, y2],  # 检测框坐标
+                    "confidence": 0.9,  # 置信度
+                    "track_id": 123,  # 跟踪ID
+                    "class_id": 0,  # 类别ID
+                    "class_str": "class_name"  # 类别名称
+                },
+                ...
+            ]
+        ,
+        }
+        """
+        with open(json_path, "r", encoding="utf-8") as f:
+            self._data = json.load(f)
+            return self._data
+
+    def get_class_dict(self):
+        """
+        只保留每帧的类列表，不包含其他信息
+
+        格式1：
+        {
+            "class_list": ["class_name1", "class_name2", ..."],
+            "frame_list": {
+                "0": [ # frame_idx
+                    0,  # 类别ID
+                    1,  # 类别ID
+                    ...
+                ],
+            },
+        }
+        """
+        if self._data is None:
+            raise ValueError("请先调用 run() 方法运行模型")
+
+        # 收集所有类别名称并去重
+        class_set = set() # 无序，唯一
+        for frame_data in self._data.values():
+            for item in frame_data:
+                class_set.add(item["class_str"])
+        class_list = sorted(list(class_set))
+
+        # 构建每帧的类别ID列表
+        frame_list = {}
+        for frame_idx, frame_data in self._data.items():
+            class_ids = set() # 无序，唯一
+            for item in frame_data:
+                class_name = item["class_str"]
+                class_id = class_list.index(class_name)
+                class_ids.add(class_id)
+            frame_list[str(frame_idx)] = sorted(list(class_ids))
+
+        return {
+            "class_list": class_list,
+            "frame_list": frame_list
+        }
+
+    def data(self):
+        return self._data
+
+def extract_frames_to_video(video_path, output_dir, interval=1):
+    """
+    抽帧生成新视频（无任何图像处理）
+
+    参数:
+    - video_path: 原始视频文件路径
+    - output_dir: 输出文件夹路径（新视频将保存在此文件夹）
+    - interval: 抽帧间隔（如 6 表示每隔 5 帧抽取 1 帧）
+    """
+    print(f"开始抽帧: {video_path}")
+    print(f"抽帧间隔: {interval}")
+    print(f"输出目录: {output_dir}")
+    # 确保输出目录存在
+    Path(output_dir).mkdir(parents=True, exist_ok=True)
+
+    # 1. 打开原始视频
+    # 尝试使用硬件加速后端
+    backends = [
+        (cv2.CAP_FFMPEG, 'FFmpeg'),     # 通用后端，支持硬件加速
+        (cv2.CAP_DSHOW, 'DirectShow'),  # Windows 硬件加速
+        (cv2.CAP_ANY, 'Default')        # 默认后端
+    ]
+
+    cap = None
+    for backend, backend_name in backends:
+        try:
+            cap = cv2.VideoCapture(video_path, backend)
+            if cap.isOpened():
+                # 尝试启用硬件加速
+                if backend == cv2.CAP_FFMPEG:
+                    # 设置FFmpeg硬件加速
+                    try:
+                        cap.set(cv2.CAP_PROP_HW_ACCELERATION, cv2.VIDEO_ACCELERATION_ANY)
+                        # 检查硬件加速是否启用
+                        hw_accel = cap.get(cv2.CAP_PROP_HW_ACCELERATION)
+                        print(f"FFmpeg硬件加速: {'已启用' if hw_accel > 0 else '未启用'}")
+                    except Exception as e:
+                        print(f"设置硬件加速失败: {e}")
+                print(f"使用后端: {backend_name}")
+                break
+        except Exception as e:
+            print(f"尝试{backend_name}后端失败: {e}")
+            continue
+
+    if not cap or not cap.isOpened():
+        raise Exception(f"无法打开视频文件: {video_path}")
+
+    # 2. 获取视频参数
+    fps = cap.get(cv2.CAP_PROP_FPS)          # 原始帧率
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    # 3. 计算新视频的帧率（保持原始播放速度）
+    # 新视频的帧率 = 原始帧率 / 抽帧间隔
+    new_fps = fps / max(interval, 1)
+
+    # 4. 初始化视频写入器
+    output_path = os.path.join(output_dir, "output_video.mp4")
+    # 使用更高效的编码格式
+    fourcc = cv2.VideoWriter_fourcc(*'avc1') # type: ignore # H.264 编码，更高效
+
+    # 尝试启用硬件加速写入
+    try:
+        # 尝试使用硬件加速的VideoWriter
+        out = cv2.VideoWriter(output_path, fourcc, new_fps, (width, height), isColor=True)
+        print("视频写入器初始化成功")
+    except Exception as e:
+        print(f"硬件加速写入失败，使用默认方式: {e}")
+        out = cv2.VideoWriter(output_path, fourcc, new_fps, (width, height))
+
+    # 5. 开始抽帧并写入新视频
+    frame_idx = 0
+    saved_frames = 0
+    print_interval = max(100, total_frames // 10)  # 每处理100帧或10%进度打印一次
+
+    while cap.isOpened():
+        # 优化：对于不需要的帧，使用grab()跳过，只对需要的帧使用retrieve()
+        if frame_idx % interval == 0:
+            # 需要处理的帧，使用read()获取
+            ret, frame = cap.read()
+            if not ret:
+                break
+            out.write(frame)  # 写入新视频
+            saved_frames += 1
+        else:
+            # 不需要处理的帧，使用grab()跳过（更快）
+            ret = cap.grab()
+            if not ret:
+                break
+
+        frame_idx += 1
+
+        # 减少打印频率
+        if frame_idx % print_interval == 0:
+            progress = (frame_idx / total_frames) * 100
+            print(f"已处理帧 {frame_idx}/{total_frames} ({progress:.1f}%)")
+
+    # 6. 释放资源
+    cap.release()
+    out.release()
+    cv2.destroyAllWindows()
+
+    print(f"抽帧完成: 原视频 {total_frames} 帧, 抽取后 {saved_frames} 帧")
+    print(f"新视频已保存至: {output_path}")
+    print(f"新视频帧率: {new_fps:.2f} fps (原始帧率: {fps:.2f} fps)")
+
+    return output_path
\ No newline at end of file
diff --git a/openh264-1.8.0-win64.dll b/openh264-1.8.0-win64.dll
new file mode 100644
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diff --git a/prompt/7_检测提示词_视频_new3.md b/prompt/7_检测提示词_视频_new3.md
new file mode 100644
index 0000000..083ef01
--- /dev/null
+++ b/prompt/7_检测提示词_视频_new3.md
@@ -0,0 +1,87 @@
+# json输出数据模板
+{
+  "tag": [
+    "这里是隐患标签",
+    "这里是隐患标签_1"
+  ],
+  "base": [
+    "隐患依据1",
+    "隐患依据2",
+    ...
+  ]
+  "objects": [
+    {
+      "hazard_track_id": 0,
+      "tag_id": 0,
+      "class_id": 0,
+      "level": 0,
+      "start_frame": 0,
+      "base_id": 0,
+      "location": "这里是隐患位置描述"
+    },
+    ...
+  ]
+}
+
+# json输出示例
+{
+  "tag": [
+    "灭火器未点检",
+    "灭火器被遮挡"
+  ],
+  "base": [
+    "灭火器未点检依据1",
+    "灭火器被遮挡依据2",
+  ],
+  "objects": [
+    {
+      "hazard_track_id": 0,
+      "tag_id": 0,
+      "class_id": 0,
+      "level": 0,
+      "start_frame": 0,
+      "base_id": 0,
+      "location": "这里是隐患位置描述"
+    },
+    {
+      "hazard_track_id": 1,
+      "tag_id": 1,
+      "class_id": 1,
+      "level": 1,
+      "start_frame": 0,
+      "base_id": 1,
+      "location": "这里是隐患位置描述"
+    }
+  ]
+}
+
+# 输出格式注意事项
+- 你的输出只能包含tag、base和objects三个键。
+- tag是一个字符串数组，每个元素是隐患标签，必须为中文，不能使用英文。每个隐患点只能有一个标签，如果规则中存在多个标签，必须选择最符合视频中情况的一个标签。
+- objects是一个字典列表，每个字典必须包含hazard_track_id（整数）、tag_id（整数）、class_id（整数）、level（整数）、start_frame（整数）、base_id（整数）、location（字符串）。
+- hazard_track_id分配规则：根据视频画面，每个连续出现的类型+隐患组合应分配单独的hazard_track_id。相同的tag_id+class_id组合在时间上不应相交，应确保只存在一份连续的记录。绝对禁止出现多个hazard_track_id具有相同的tag_id和class_id且时间重叠的情况。
+- level必须为0或1或2，不能为其他整数。为0表示隐患等级为疑似，为1表示隐患等级为低，为2表示隐患等级为高。
+- 不要输出bbox_2d、label或任何中文标点。
+- 输出格式必须为标准json格式，且结构必须与模板一致
+- class_id必须与class_list中的顺序严格一致，保持一一映射关系。
+- 所有hazard_track_id都为独立隐患点，不存在误检，不得合并或拆分
+- 有因为图像分辨率不足或视角问题导致的无法检测，level必须为0
+- 输出时只允许输出json内容，不允许输出其他内容（如```json ```）
+- 绝对禁止在输出中包含```json```或```等任何代码块标记
+- start_frame表示该隐患在视频中的开始帧，必须为整数
+- location表示该隐患在视频画面中的位置描述，必须为中文，描述要准确、清晰，能够明确指出隐患在画面中的相对位置。
+
+# 任务1
+- **帧级分析**：根据提供的物体class、出现时间点（提供的数据为每帧对应的物体列表）与隐患识别规则，在视频中，同track_id的物体需持续观察，对隐患进行识别，每个隐患点分配一个hazard_track_id，杜绝在同一个物体上重复识别隐患点
+- **汇总处理**：在完成所有帧的分析后，基于各帧的分析结果，为每个hazard_track_id确定最终的隐患标签、等级、位置描述以及开始帧位置
+- **基本要求**：只检测由class确定的物体，每个hazard_track_id对应的字典中必须包含该hazard_track_id的tag_id、class_id、level、start_frame、base_id、location信息
+- **匹配规则**：如果物体与检测条目匹配，就将该检测条目添加到objects列表中，并设置相应的tag_id
+- **关键约束**：
+  1. **逐帧分析**：必须分析每一帧中的每个物体，用class匹配相应的检测规则，根据隐患识别规则进行隐患识别
+  2. **语音识别**：必须对视频中的语音进行识别，辅助隐患识别
+  3. **规则参考**：严格参考知识库中的规则结构进行隐患识别，规则结构参考 `知识库/rule.json`
+  4. **全面识别**：必须对所有提供的物体进行隐患识别，不得遗漏任何物体
+  5. **准确匹配**：根据物体的class与隐患识别规则进行准确匹配，确定隐患标签和等级
+  6. **等级判定**：根据规则中的匹配条件和依据，合理判定匹配等级（0-疑似，1-确定）
+  7. **hazard_track_id分配**：根据视频画面，每个隐患点应分配单独的hazard_track_id。
+  8. **位置描述**：大模型需在输出时提供隐患点相对于视频画面的位置，location字段必须准确描述隐患在画面中的位置，例如："画面左上角"、"画面中央偏右"等。
diff --git a/readme.md b/readme.md
new file mode 100644
index 0000000..12c228b
--- /dev/null
+++ b/readme.md
@@ -0,0 +1,3 @@
+### qwen试用
+账号: 18237294717
+https://bailian.console.aliyun.com/cn-beijing/?spm=a2c4g.11186623.0.0.2c2b2217h6fbio&tab=app#/app-center
\ No newline at end of file
diff --git a/test_optimization.py b/test_optimization.py
new file mode 100644
index 0000000..fd53889
--- /dev/null
+++ b/test_optimization.py
@@ -0,0 +1,32 @@
+import json
+import time
+from lib.qwen_fun_vid import frame_all_to_obj_vid
+
+# 测试数据路径
+input_video_path = "runs/segment/predict/04-01.avi"
+json_path = "output_2/04-01/frame_all.json"
+output_video_root = "output_2/04-01/test_optimized"
+
+# 读取标注数据
+print("读取标注数据...")
+with open(json_path, 'r', encoding='utf-8') as f:
+    json_data = json.load(f)
+
+print(f"标注数据包含 {len(json_data)} 帧")
+
+# 统计track_id数量
+track_ids = set()
+for frame_id_str, detections in json_data.items():
+    for det in detections:
+        track_id = det.get("track_id", -1)
+        track_ids.add(track_id)
+print(f"共包含 {len(track_ids)} 个track_id")
+
+# 测试优化后的函数
+print("\n开始测试优化后的函数...")
+start_time = time.time()
+frame_all_to_obj_vid(json_data, input_video_path, output_video_root)
+end_time = time.time()
+
+print(f"\n测试完成，耗时: {end_time - start_time:.2f} 秒")
+print(f"输出视频保存到: {output_video_root}")
diff --git a/千问API数据.txt b/千问API数据.txt
new file mode 100644
index 0000000..7c9441f
--- /dev/null
+++ b/千问API数据.txt
@@ -0,0 +1,27 @@
+Qwen3.5-27B
+
+api_key="sk-c7ffed1b32794284a5d21b046d7d0008"
+base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
+
+
+import os
+from openai import OpenAI
+
+try:
+    client = OpenAI(
+        # 若没有配置环境变量，请用阿里云百炼API Key将下行替换为: api_key="sk-xxx",
+        api_key=os.getenv("DASHSCOPE_API_KEY"),
+        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
+    )
+
+    completion = client.chat.completions.create(
+        model="qwen3.5-27b",  # 模型列表: https://help.aliyun.com/model-studio/getting-started/models
+        messages=[
+            {'role': 'system', 'content': 'You are a helpful assistant.'},
+            {'role': 'user', 'content': '你是谁？'}
+        ]
+    )
+    print(completion.choices[0].message.content)
+except Exception as e:
+    print(f"错误信息：{e}")
+    print("请参考文档：https://help.aliyun.com/model-studio/developer-reference/error-code")
\ No newline at end of file
diff --git a/知识库/rule.json b/知识库/rule.json
new file mode 100644
index 0000000..b41b755
--- /dev/null
+++ b/知识库/rule.json
@@ -0,0 +1,695 @@
+{
+  "（一）厂房防火分区": {
+    "消防通道": {
+      "重大隐患": {
+        "安全出口堵塞": {
+          "依据": [
+            "*42. 人员密集场所的疏散走道、楼梯间、疏散门或安全出口设置影响疏散的栅栏、卷帘门。",
+            "*83. 存在 “两个通道” 堵塞情形：存在严重占用防火间距、严重破坏防火分区、严重影响人员安全疏散和消防救援的情形。"
+          ],
+          "匹配条件": ["安全出口被货物、杂物、设备等堵塞，无法正常通行"]
+        },
+        "安全出口被上锁": {
+          "依据": [
+            "*42. 人员密集场所的疏散走道、楼梯间、疏散门或安全出口设置影响疏散的栅栏、卷帘门。"
+          ],
+          "匹配条件": ["安全出口门被锁闭、封堵，紧急情况下无法从内部开启"]
+        },
+        "安全出口设置卷帘门": {
+          "依据": [
+            "*42. 人员密集场所的疏散走道、楼梯间、疏散门或安全出口设置影响疏散的栅栏、卷帘门。"
+          ],
+          "匹配条件": ["安全出口采用卷帘门、转门、吊门、侧拉门等影响疏散的门型"]
+        },
+        "安全出口数量不足": {
+          "依据": [
+            "*42. 人员密集场所的疏散走道、楼梯间、疏散门或安全出口设置影响疏散的栅栏、卷帘门。"
+          ],
+          "匹配条件": ["生产车间、仓库等场所建筑面积超规，仅设置一个安全出口"]
+        },
+        "安全出口未安装安全出口标识": {
+          "依据": [
+            "*42. 人员密集场所的疏散走道、楼梯间、疏散门或安全出口设置影响疏散的栅栏、卷帘门。"
+          ],
+          "匹配条件": ["安全出口位置未设置醒目的安全出口指示标志"]
+        },
+        "疏散走道被占用、堵塞": {
+          "依据": [
+            "*42. 人员密集场所的疏散走道、楼梯间、疏散门或安全出口设置影响疏散的栅栏、卷帘门。",
+            "*83. 存在 “两个通道” 堵塞情形：存在严重占用防火间距、严重破坏防火分区、严重影响人员安全疏散和消防救援的情形。"
+          ],
+          "匹配条件": ["疏散走道堆放货物、设备、杂物，净宽不足，影响人员疏散"]
+        },
+        "楼梯间堆放杂物": {
+          "依据": [
+            "*42. 人员密集场所的疏散走道、楼梯间、疏散门或安全出口设置影响疏散的栅栏、卷帘门。"
+          ],
+          "匹配条件": ["封闭楼梯间、防烟楼梯间内堆放物品，占用疏散空间"]
+        },
+        "疏散通道设置栅栏、障碍物": {
+          "依据": [
+            "*42. 人员密集场所的疏散走道、楼梯间、疏散门或安全出口设置影响疏散的栅栏、卷帘门。"
+          ],
+          "匹配条件": ["疏散走道、安全出口处设置铁栅栏、障碍物，阻碍人员疏散"]
+        },
+        "消防通道被占用、堵塞": {
+          "依据": [
+            "*55. 多产权建筑，具有多个使用方的甲、乙、丙类厂房或仓库，多业态混合生产经营场所未明确消防安全管理职责，未对消防车通道、涉及公共消防安全的疏散设施和其他消防设施明确统一管理单位。",
+            "*83. 存在 “两个通道” 堵塞情形：存在严重占用防火间距、严重破坏防火分区、严重影响人员安全疏散和消防救援的情形。"
+          ],
+          "匹配条件": [
+            "厂区消防车通道停放车辆、堆放物品、设置障碍物，宽度不足 4 米"
+          ]
+        },
+        "消防通道违章搭建": {
+          "依据": [
+            "*83. 存在 “两个通道” 堵塞情形：存在严重占用防火间距、严重破坏防火分区、严重影响人员安全疏散和消防救援的情形。"
+          ],
+          "匹配条件": ["消防通道范围内违章搭建建筑物、构筑物，影响消防车通行"]
+        },
+        "安全出口被电气线路遮挡": {
+          "依据": [
+            "*42. 人员密集场所的疏散走道、楼梯间、疏散门或安全出口设置影响疏散的栅栏、卷帘门。"
+          ],
+          "匹配条件": ["电气线路、桥架、管线遮挡安全出口，影响疏散通行"]
+        },
+        "疏散门开启方向错误": {
+          "依据": [
+            "*42. 人员密集场所的疏散走道、楼梯间、疏散门或安全出口设置影响疏散的栅栏、卷帘门。"
+          ],
+          "匹配条件": ["疏散门未向疏散方向开启，紧急情况下难以推开"]
+        },
+        "安全出口标识缺失、损坏": {
+          "依据": [
+            "*42. 人员密集场所的疏散走道、楼梯间、疏散门或安全出口设置影响疏散的栅栏、卷帘门。"
+          ],
+          "匹配条件": ["安全出口指示标志脱落、损坏、不清晰，无法起到指示作用"]
+        },
+        "疏散通道净宽不足": {
+          "依据": [
+            "*42. 人员密集场所的疏散走道、楼梯间、疏散门或安全出口设置影响疏散的栅栏、卷帘门。"
+          ],
+          "匹配条件": ["疏散走道、安全出口净宽度不符合规范要求，小于规定值"]
+        },
+        "厂房内设置宿舍占用疏散通道": {
+          "依据": [
+            "*40. 劳动密集型企业的厂房、仓库内设置员工宿舍。",
+            "*87. 存在 “三合一” 情形：在厂房、库房、商场中设置员工宿舍，或是在居住等民用建筑中从事生产、储存、经营等活动，且不符合 GA703 的规定。"
+          ],
+          "匹配条件": ["厂房、仓库内设置员工宿舍，占用、堵塞疏散通道与安全出口"]
+        }
+      }
+    },
+    "灭火器": {
+      "重大隐患": {
+        "灭火器未点检": {
+          "依据": [
+            "*55.多产权建筑，具有多个使用方的甲、乙、丙类厂房或仓库，多业态混合生产经营场所未明确消防安全管理职责，未对消防车通道、涉及公共消防安全的疏散设施和其他消防设施明确统一管理单位。",
+            "*60.具有火灾、爆炸风险的电池生产、储存场所或储能电站防火间距不足，未采取防火分隔措施，或未设置事故通风系统或自动灭火系统。",
+            "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。",
+            "*89.企业在用的特种设备存在重大事故隐患的。",
+            "*85.企业在用的特种设备存在重大事故隐患的。",
+            "*79.在用的特种设备是未取得许可进行设计、制造、安装、改造、重大修理的。",
+            "*5.未对承包单位、承租单位定期进行安全检查。"
+          ],
+          "匹配条件": ["灭火器上无检验单"]
+        },
+        "灭火器标识与实物不符": {
+          "依据": [
+            "*55.多产权建筑，具有多个使用方的甲、乙、丙类厂房或仓库，多业态混合生产经营场所未明确消防安全管理职责，未对消防车通道、涉及公共消防安全的疏散设施和其他消防设施明确统一管理单位。"
+          ]
+        },
+        "灭火器喷管缠绕": {
+          "依据": [
+            "*55.多产权建筑，具有多个使用方的甲、乙、丙类厂房或仓库，多业态混合生产经营场所未明确消防安全管理职责，未对消防车通道、涉及公共消防安全的疏散设施和其他消防设施明确统一管理单位。"
+          ]
+        },
+        "灭火器被遮挡": {
+          "依据": [
+            "*92.燃气管道、阀门、接头有泄漏、腐蚀、老化现象，或紧急切断阀不灵敏可靠。",
+            "*60.具有火灾、爆炸风险的电池生产、储存场所或储能电站防火间距不足，未采取防火分隔措施，或未设置事故通风系统或自动灭火系统。",
+            "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+          ]
+        },
+        "灭火器压力不足": {
+          "依据": [
+            "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+          ]
+        },
+        "灭火器喷管卷法不规范": {
+          "依据": [
+            "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。",
+            "*3.生产、储存、经营易燃易爆危险品的场所与人员密集场所、居住场所设置在同一建筑物内，或与人员密集场所、居住场所的防火间距小于国家工程建设消防技术标准规定值的75%。"
+          ]
+        },
+        "灭火器被随意放置在地面上": {
+          "依据": [
+            "*89.企业在用的特种设备存在重大事故隐患的。",
+            "*78.存在未经许可，擅自从事移动式压力容器或者气瓶充装活动。"
+          ]
+        },
+        "灭火器过期": {
+          "依据": [
+            "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+          ]
+        },
+        "灭火器压力指示表损坏": {
+          "依据": [
+            "*5.易燃可燃液体、可燃气体储罐(区)未按国家工程建设消防技术标准的规定设置固定灭火、冷却、可燃气体浓度报警、火灾报警设施。",
+            "*40.压力管道安全阀、压力表、爆破片等安全附件、安全保护装置等缺少、失效或失灵。"
+          ]
+        },
+        "灭火器破旧损坏": {
+          "依据": [
+            "*5.易燃可燃液体、可燃气体储罐(区)未按国家工程建设消防技术标准的规定设置固定灭火、冷却、可燃气体浓度报警、火灾报警设施。"
+          ]
+        },
+        "灭火器点检频次不足": {
+          "依据": [
+            "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+          ]
+        },
+        "积尘严重": {
+          "依据": [
+            "*20.存在粉尘爆炸危险的工贸企业未落实粉尘清理制度，造成作业现场积尘严重。"
+          ],
+          "匹配条件": ["灭火器上可见明显灰尘"]
+        }
+      }
+    },
+    "消火栓": {
+      "重大隐患": {
+        "消火栓未点检": {
+          "依据": [
+            "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。",
+            "*55.多产权建筑，具有多个使用方的甲、乙、丙类厂房或仓库，多业态混合生产经营场所未明确消防安全管理职责，未对消防车通道、涉及公共消防安全的疏散设施和其他消防设施明确统一管理单位。"
+          ],
+          "匹配条件": ["消火栓箱体无定期检查记录、无检验标签"]
+        },
+        "消火栓被遮挡、被封堵": {
+          "依据": [
+            "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。",
+            "*83.存在“两个通道”堵塞情形:存在严重占用防火间距、严重破坏防火分区、严重影响人员安全疏散和消防救援的情形。"
+          ],
+          "匹配条件": ["消火栓被货物、设备、杂物、围挡遮挡，无法正常开启使用"]
+        },
+        "消火栓无水/水压不足": {
+          "依据": [
+            "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。",
+            "*60.具有火灾、爆炸风险的电池生产、储存场所或储能电站防火间距不足，未采取防火分隔措施，或未设置事故通风系统或自动灭火系统。"
+          ],
+          "匹配条件": ["打开消火栓阀门无出水，或出水压力微弱，无法满足灭火要求"]
+        },
+        "消火栓水带缺失/破损/老化": {
+          "依据": [
+            "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+          ],
+          "匹配条件": ["消火栓箱内无消防水带，或水带破损、漏水、老化严重"]
+        },
+        "消火栓水枪缺失/损坏": {
+          "依据": [
+            "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+          ],
+          "匹配条件": ["消火栓箱内无消防水枪，或水枪断裂、接口损坏无法使用"]
+        },
+        "消火栓箱门损坏/无法开启": {
+          "依据": [
+            "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+          ],
+          "匹配条件": ["消火栓箱体变形、门锁损坏、箱门无法正常打开"]
+        },
+        "消火栓接口漏水/损坏": {
+          "依据": [
+            "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+          ],
+          "匹配条件": ["消火栓出水口、接口破损、锈蚀，连接水带时严重漏水"]
+        },
+        "消火栓积尘严重/未清理": {
+          "依据": [
+            "*20.存在粉尘爆炸危险的工贸企业未落实粉尘清理制度，造成作业现场积尘严重。",
+            "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+          ],
+          "匹配条件": ["消火栓箱体、水带、水枪表面积尘严重，未定期清理"]
+        },
+        "消火栓标识缺失/模糊不清": {
+          "依据": [
+            "*42.人员密集场所的疏散走道、楼梯间、疏散门或安全出口设置影响疏散的栅栏、卷帘门。",
+            "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+          ],
+          "匹配条件": ["消火栓无标识、标识脱落、文字模糊，无法快速识别"]
+        },
+        "消火栓随意堆放杂物": {
+          "依据": [
+            "*83.存在“两个通道”堵塞情形:存在严重占用防火间距、严重破坏防火分区、严重影响人员安全疏散和消防救援的情形。",
+            "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+          ],
+          "匹配条件": ["消火栓箱内/周边堆放工具、垃圾、物料等杂物"]
+        },
+        "消火栓阀门损坏/无法开关": {
+          "依据": [
+            "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+          ],
+          "匹配条件": ["消火栓控制阀门锈蚀、卡死、断裂，无法正常开启或关闭"]
+        }
+      }
+    },
+    "安全出口": {
+      "重大隐患": {
+        "安全出口数量不足": {
+          "依据": [
+            "*42.人员密集场所的疏散走道、楼梯间、疏散门或安全出口设置影响疏散的栅栏、卷帘门。"
+          ],
+          "匹配条件": [
+            "厂房、仓库建筑面积超规，仅设置1个安全出口，不满足规范要求"
+          ]
+        },
+        "安全出口上锁无法开启": {
+          "依据": [
+            "*42.人员密集场所的疏散走道、楼梯间、疏散门或安全出口设置影响疏散的栅栏、卷帘门。"
+          ],
+          "匹配条件": ["安全出口安装门锁、链条锁闭，紧急情况下无法从内部开启"]
+        },
+        "安全出口设置卷帘门/转门": {
+          "依据": [
+            "*42.人员密集场所的疏散走道、楼梯间、疏散门或安全出口设置影响疏散的栅栏、卷帘门。"
+          ],
+          "匹配条件": ["安全出口采用卷帘门、转门、吊门、侧拉门，影响人员疏散"]
+        },
+        "安全出口净宽不足": {
+          "依据": [
+            "*42.人员密集场所的疏散走道、楼梯间、疏散门或安全出口设置影响疏散的栅栏、卷帘门。"
+          ],
+          "匹配条件": ["安全出口宽度小于规范要求，无法满足人员疏散通行"]
+        },
+        "安全出口开启方向错误": {
+          "依据": [
+            "*42.人员密集场所的疏散走道、楼梯间、疏散门或安全出口设置影响疏散的栅栏、卷帘门。"
+          ],
+          "匹配条件": ["安全出口门未向疏散方向开启，紧急情况下难以推开"]
+        }
+      }
+    },
+    "应急照明灯": {
+      "重大隐患": {
+        "应急照明灯缺失": {
+          "依据": [
+            "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+          ],
+          "匹配条件": ["疏散走道、楼梯间、安全出口位置未安装应急照明灯"]
+        },
+        "应急照明灯损坏/不亮": {
+          "依据": [
+            "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+          ],
+          "匹配条件": ["应急照明灯断电后无法点亮、灯光微弱、灯泡损坏"]
+        },
+        "应急照明灯遮挡/安装位置不当": {
+          "依据": [
+            "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+          ],
+          "匹配条件": ["应急照明灯被货物、设备遮挡，无法起到照明作用"]
+        },
+        "应急照明灯未定期点检": {
+          "依据": [
+            "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+          ],
+          "匹配条件": ["无应急照明点检记录，未定期测试功能"]
+        }
+      }
+    },
+    "疏散指示标志": {
+      "重大隐患": {
+        "疏散指示标志缺失": {
+          "依据": [
+            "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+          ],
+          "匹配条件": ["疏散走道、转角、安全出口未设置疏散指示标志"]
+        },
+        "疏散指示标志损坏/不亮": {
+          "依据": [
+            "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+          ],
+          "匹配条件": ["疏散指示标志断电不亮、指示灯损坏、方向指示错误"]
+        },
+        "疏散指示标志遮挡/脱落": {
+          "依据": [
+            "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+          ],
+          "匹配条件": ["疏散指示标志被遮挡、掉落、安装高度不符合要求"]
+        },
+        "疏散指示标志模糊不清": {
+          "依据": [
+            "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+          ],
+          "匹配条件": ["标志文字、箭头模糊不清，无法识别疏散方向"]
+        }
+      }
+    },
+    "堵塞通道的货物/电瓶车/杂物": {
+      "重大隐患": {
+        "疏散通道堆放货物堵塞": {
+          "依据": [
+            "*83.存在“两个通道”堵塞情形:存在严重占用防火间距、严重破坏防火分区、严重影响人员安全疏散和消防救援的情形。"
+          ],
+          "匹配条件": [
+            "疏散走道、楼梯间堆放原材料、成品、半成品等货物，占用疏散空间"
+          ]
+        },
+        "电瓶车停放在疏散通道": {
+          "依据": [
+            "*87.在厂房、库房、商场中设置员工宿舍，或是在居住等民用建筑中从事生产、储存、经营等活动，且不符合GA703的规定。"
+          ],
+          "匹配条件": [
+            "电动自行车、电动三轮车停放在疏散走道、安全出口、楼梯间内"
+          ]
+        },
+        "疏散通道堆放杂物堵塞": {
+          "依据": [
+            "*83.存在“两个通道”堵塞情形:存在严重占用防火间距、严重破坏防火分区、严重影响人员安全疏散和消防救援的情形。"
+          ],
+          "匹配条件": ["疏散通道堆放垃圾、工具、纸箱、周转箱等杂物，净宽不足"]
+        },
+        "设备占用疏散通道": {
+          "依据": [
+            "*83.存在“两个通道”堵塞情形:存在严重占用防火间距、严重破坏防火分区、严重影响人员安全疏散和消防救援的情形。"
+          ],
+          "匹配条件": ["生产设备、货架、工作台摆放在疏散通道内，阻碍通行"]
+        }
+      }
+    },
+    "封堵安全出口的障碍物": {
+      "重大隐患": {
+        "安全出口设置铁栅栏封堵": {
+          "依据": [
+            "*42.人员密集场所的疏散走道、楼梯间、疏散门或安全出口设置影响疏散的栅栏、卷帘门。"
+          ],
+          "匹配条件": ["安全出口外/内安装铁栅栏、防护网，完全封堵出口"]
+        },
+        "安全出口被墙体/板材封堵": {
+          "依据": [
+            "*83.存在“两个通道”堵塞情形:存在严重占用防火间距、严重破坏防火分区、严重影响人员安全疏散和消防救援的情形。"
+          ],
+          "匹配条件": ["安全出口被砌墙、封板、钉死，无法通行"]
+        },
+        "安全出口前设置固定障碍物": {
+          "依据": [
+            "*83.存在“两个通道”堵塞情形:存在严重占用防火间距、严重破坏防火分区、严重影响人员安全疏散和消防救援的情形。"
+          ],
+          "匹配条件": ["安全出口正前方放置货架、柜子、设备等固定障碍物"]
+        },
+        "安全出口被围挡/隔离栏封堵": {
+          "依据": [
+            "*83.存在“两个通道”堵塞情形:存在严重占用防火间距、严重破坏防火分区、严重影响人员安全疏散和消防救援的情形。"
+          ],
+          "匹配条件": ["安全出口被隔离栏、施工围挡、广告牌完全封堵"]
+        }
+      }
+    }
+  },
+  "（二）电气线路及配电设施": {
+        "配电箱": {
+            "重大隐患": {
+                "配电箱未使用防爆型": {
+                    "依据": [
+                        "*7.除尘器、收尘仓等划分为20区的粉尘爆炸危险场所电气设备不符合防爆要求。",
+                        "*35.直接关系生产安全的监控、报警、防护等设施、设备、装置未保证正常运行、使用，失效或者无效。"
+                    ],
+                    "匹配条件": [
+                        "爆炸危险区域、喷漆间、粉尘场所、氢气使用场所未安装防爆配电箱"
+                    ]
+                },
+                "配电箱门损坏/缺失": {
+                    "依据": [
+                        "*89.企业在用的特种设备存在重大事故隐患的。",
+                        "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+                    ],
+                    "匹配条件": [
+                        "配电箱箱体破损、箱门缺失、无法关闭"
+                    ]
+                },
+                "配电箱门口堆放杂物/易燃物": {
+                    "依据": [
+                        "*55.多产权建筑，具有多个使用方的甲、乙、丙类厂房或仓库，多业态混合生产经营场所未明确消防安全管理职责，未对消防车通道、涉及公共消防安全的疏散设施和其他消防设施明确统一管理单位。",
+                        "*83.存在“两个通道”堵塞情形:存在严重占用防火间距、严重破坏防火分区、严重影响人员安全疏散和消防救援的情形。"
+                    ],
+                    "匹配条件": [
+                        "配电箱前1米内堆放纸箱、化学品、设备、垃圾等杂物"
+                    ]
+                },
+                "配电箱无警示标识/标识褪色": {
+                    "依据": [
+                        "*4.未对有限空间进行辨识、建立安全管理台账，并且未设置明显的安全警示标志。",
+                        "*79.在用的特种设备是未取得许可进行设计、制造、安装、改造、重大修理的。"
+                    ],
+                    "匹配条件": [
+                        "配电箱未张贴“有电危险”警示标识，或标识模糊、脱落"
+                    ]
+                },
+                "配电箱内积尘严重": {
+                    "依据": [
+                        "*20.存在粉尘爆炸危险的工贸企业未落实粉尘清理制度，造成作业现场积尘严重。"
+                    ],
+                    "匹配条件": [
+                        "配电箱内部元器件表面积尘厚重，未清理"
+                    ]
+                },
+                "配电箱一闸多机/私拉乱接": {
+                    "依据": [
+                        "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+                    ],
+                    "匹配条件": [
+                        "一个回路连接多台设备，电线杂乱搭接"
+                    ]
+                }
+            }
+        },
+        "电线电缆": {
+            "重大隐患": {
+                "电线电缆老化/破损/裸露": {
+                    "依据": [
+                        "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+                    ],
+                    "匹配条件": [
+                        "电源线绝缘层开裂、铜线裸露、老化发黑"
+                    ]
+                },
+                "电线未穿管保护/明敷拖地": {
+                    "依据": [
+                        "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+                    ],
+                    "匹配条件": [
+                        "电线直接敷设在地面、设备边缘，未套管防护，易被碾压破损"
+                    ]
+                },
+                "防爆区域电线未做防爆密封": {
+                    "依据": [
+                        "*7.除尘器、收尘仓等划分为20区的粉尘爆炸危险场所电气设备不符合防爆要求。"
+                    ],
+                    "匹配条件": [
+                        "粉尘、易燃易爆场所电线未防爆穿管、未密封"
+                    ]
+                },
+                "电线私拉乱接/飞线": {
+                    "依据": [
+                        "*35.直接关系生产安全的监控、报警、防护等设施、设备、装置未保证正常运行、使用，失效或者无效。"
+                    ],
+                    "匹配条件": [
+                        "现场存在临时搭接、跨通道、跨设备的无规则电线"
+                    ]
+                },
+                "电线高温区域敷设未隔热": {
+                    "依据": [
+                        "*1.食品制造企业烘制、油炸设备未设置防过热自动切断装置。"
+                    ],
+                    "匹配条件": [
+                        "电线靠近烘道、烤箱、加热设备，未做防火隔热处理"
+                    ]
+                }
+            }
+        },
+        "插座": {
+            "重大隐患": {
+                "插座破损/松动/裸露": {
+                    "依据": [
+                        "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+                    ],
+                    "匹配条件": [
+                        "插座外壳破裂、铜片松动、接线裸露"
+                    ]
+                },
+                "防爆区域使用非防爆插座": {
+                    "依据": [
+                        "*7.除尘器、收尘仓等划分为20区的粉尘爆炸危险场所电气设备不符合防爆要求。"
+                    ],
+                    "匹配条件": [
+                        "喷漆、粉尘、易燃易爆场所使用普通民用插座"
+                    ]
+                },
+                "插座超负荷使用/串联插排": {
+                    "依据": [
+                        "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+                    ],
+                    "匹配条件": [
+                        "一个插座串联多个插排，带大功率设备"
+                    ]
+                },
+                "插座无漏电保护": {
+                    "依据": [
+                        "*1.存在粉尘爆炸危险的除尘系统采用重力沉降室除尘，或者采用干式巷道式构筑物作为除尘风道。"
+                    ],
+                    "匹配条件": [
+                        "车间、潮湿场所插座回路未安装漏电保护器"
+                    ]
+                }
+            }
+        },
+        "开关": {
+            "重大隐患": {
+                "开关破损/裸露/失效": {
+                    "依据": [
+                        "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+                    ],
+                    "匹配条件": [
+                        "开关外壳破损、接线裸露、无法正常通断"
+                    ]
+                },
+                "防爆区域使用普通开关": {
+                    "依据": [
+                        "*7.除尘器、收尘仓等划分为20区的粉尘爆炸危险场所电气设备不符合防爆要求。"
+                    ],
+                    "匹配条件": [
+                        "爆炸危险区域安装普通墙壁开关、拉线开关"
+                    ]
+                },
+                "开关控制多台大功率设备": {
+                    "依据": [
+                        "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+                    ],
+                    "匹配条件": [
+                        "单开关控制多台电机、加热器等设备，超负荷运行"
+                    ]
+                }
+            }
+        },
+        "接地装置": {
+            "重大隐患": {
+                "设备未做保护接地": {
+                    "依据": [
+                        "*40.压力管道安全阀、压力表、爆破片等安全附件、安全保护装置等缺少、失效或失灵。"
+                    ],
+                    "匹配条件": [
+                        "金属设备、配电箱、电机未连接PE保护接地线"
+                    ]
+                },
+                "接地线松动/脱落/断裂": {
+                    "依据": [
+                        "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+                    ],
+                    "匹配条件": [
+                        "接地端子松动、导线断裂、未可靠连接"
+                    ]
+                },
+                "易燃易爆区域未做防静电接地": {
+                    "依据": [
+                        "*5.易燃可燃液体、可燃气体储罐(区)未按国家工程建设消防技术标准的规定设置固定灭火、冷却、可燃气体浓度报警、火灾报警设施。"
+                    ],
+                    "匹配条件": [
+                        "化学品仓、粉尘设备、储罐未做防静电接地装置"
+                    ]
+                }
+            }
+        },
+        "大功率电器": {
+            "重大隐患": {
+                "大功率设备无专用回路": {
+                    "依据": [
+                        "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+                    ],
+                    "匹配条件": [
+                        "烤箱、焊机、加热器等大功率设备共用普通回路"
+                    ]
+                },
+                "大功率设备线路过热/老化": {
+                    "依据": [
+                        "*1.食品制造企业烘制、油炸设备未设置防过热自动切断装置。"
+                    ],
+                    "匹配条件": [
+                        "大功率设备电源线发热、绝缘层融化、老化"
+                    ]
+                },
+                "大功率设备无过热保护": {
+                    "依据": [
+                        "*35.直接关系生产安全的监控、报警、防护等设施、设备、装置未保证正常运行、使用，失效或者无效。"
+                    ],
+                    "匹配条件": [
+                        "烘道、加热设备未安装超温断电、过热保护装置"
+                    ]
+                }
+            }
+        },
+        "空气开关": {
+            "重大隐患": {
+                "空气开关缺失/损坏/失灵": {
+                    "依据": [
+                        "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+                    ],
+                    "匹配条件": [
+                        "空开无法合闸、跳闸失效、内部烧毁"
+                    ]
+                },
+                "空气开关规格不符/以大代小": {
+                    "依据": [
+                        "*227.消防设施设备缺失损坏，未定期开展检验，安装、配置不合理，处于不良好可用状态。"
+                    ],
+                    "匹配条件": [
+                        "用大电流空开替代小电流保护，失去过载保护作用"
+                    ]
+                },
+                "空开无漏电保护功能": {
+                    "依据": [
+                        "*1.存在粉尘爆炸危险的除尘系统采用重力沉降室除尘，或者采用干式巷道式构筑物作为除尘风道。"
+                    ],
+                    "匹配条件": [
+                        "车间、移动设备回路仅装空开，未装漏保"
+                    ]
+                },
+                "空气开关积尘严重/短路风险": {
+                    "依据": [
+                        "*20.存在粉尘爆炸危险的工贸企业未落实粉尘清理制度，造成作业现场积尘严重。"
+                    ],
+                    "匹配条件": [
+                        "空开表面粉尘厚重，易造成漏电、短路"
+                    ]
+                }
+            }
+        },
+        "防触电警示标识": {
+            "重大隐患": {
+                "防触电警示标识缺失": {
+                    "依据": [
+                        "*4.未对有限空间进行辨识、建立安全管理台账，并且未设置明显的安全警示标志。"
+                    ],
+                    "匹配条件": [
+                        "配电箱、变压器、高压设备未张贴防触电警示标志"
+                    ]
+                },
+                "警示标识模糊/破损/缺失": {
+                    "依据": [
+                        "*79.在用的特种设备是未取得许可进行设计、制造、安装、改造、重大修理的。"
+                    ],
+                    "匹配条件": [
+                        "警示标识褪色、破损、被遮挡，无法识别"
+                    ]
+                },
+                "危险电气区域无警示隔离": {
+                    "依据": [
+                        "*35.直接关系生产安全的监控、报警、防护等设施、设备、装置未保证正常运行、使用，失效或者无效。"
+                    ],
+                    "匹配条件": [
+                        "变压器、配电房未设置警示围栏和防触电标识"
+                    ]
+                }
+            }
+        }
+    }
+}