diff --git a/batch_syncnet.sh b/batch_syncnet.sh
new file mode 100644
index 0000000..a627fb1
--- /dev/null
+++ b/batch_syncnet.sh
@@ -0,0 +1,213 @@
+#!/bin/bash
+# SyncNet 批量处理脚本
+# 用法: ./batch_syncnet.sh [--skip-failed] [--skip-video-failed]
+
+# ==================== 配置 ====================
+PYTHON_SCRIPT="./batch_syncnet.py"  # 您的Python批量脚本
+DATA_ROOT="./data/work"             # 输出数据根目录
+LOG_DIR="./batch_logs"              # 批量日志目录
+
+# ==================== 参数解析 ====================
+SKIP_FAILED=""
+SKIP_VIDEO_FAILED=""
+
+while [[ "$#" -gt 0 ]]; do
+    case $1 in
+        --skip-failed) SKIP_FAILED="--skip-failed" ;;
+        --skip-video-failed) SKIP_VIDEO_FAILED="--skip-video-failed" ;;
+        *) echo "未知参数: $1" && exit 1 ;;
+    esac
+    shift
+done
+
+# ==================== 批量任务定义 ====================
+# 定义要处理的目录列表
+# 格式: "输入目录路径|输出子目录名"
+# 输出目录会创建在 $DATA_ROOT/输出子目录名 下
+tasks=(
+    "/path/to/videos/dir1|output_dir1"
+    "/path/to/videos/dir2|output_dir2"
+    "/path/to/videos/dir3|output_dir3"
+    "/path/to/videos/dir4|output_dir4"
+    "/path/to/videos/dir5|output_dir5"
+    "/path/to/videos/dir6|output_dir6"
+    "/path/to/videos/dir7|output_dir7"
+    "/path/to/videos/dir8|output_dir8"
+    "/path/to/videos/dir9|output_dir9"
+    "/path/to/videos/dir10|output_dir10"
+    "/path/to/videos/dir11|output_dir11"
+    "/path/to/videos/dir12|output_dir12"
+)
+
+# ==================== 初始化 ====================
+echo "========================================"
+echo "SyncNet 批量处理脚本"
+echo "开始时间: $(date)"
+echo "Python脚本: $PYTHON_SCRIPT"
+echo "数据根目录: $DATA_ROOT"
+echo "总任务数: ${#tasks[@]}"
+echo "参数: $SKIP_FAILED $SKIP_VIDEO_FAILED"
+echo "========================================"
+
+# 创建日志目录
+mkdir -p "$LOG_DIR"
+timestamp=$(date +%Y%m%d_%H%M%S)
+batch_log_file="$LOG_DIR/batch_$timestamp.log"
+
+# 写入日志头
+{
+echo "===== SyncNet 批量处理执行日志 ====="
+echo "执行时间: $(date)"
+echo "Python脚本: $PYTHON_SCRIPT"
+echo "数据根目录: $DATA_ROOT"
+echo "总任务数: ${#tasks[@]}"
+echo "参数: $SKIP_FAILED $SKIP_VIDEO_FAILED"
+echo "==================================="
+echo ""
+} | tee -a "$batch_log_file"
+
+# ==================== 处理每个任务 ====================
+total_success=0
+total_failed=0
+failed_tasks=()
+
+for i in "${!tasks[@]}"; do
+    task="${tasks[$i]}"
+    
+    # 解析任务参数
+    IFS='|' read -r input_dir output_subdir <<< "$task"
+    
+    # 构造输出目录
+    output_dir="$DATA_ROOT/$output_subdir"
+    
+    echo ""
+    echo "处理任务 $((i+1))/${#tasks[@]}:"
+    echo "  输入目录: $input_dir"
+    echo "  输出目录: $output_dir"
+    echo "----------------------------------------"
+    
+    # 检查输入目录是否存在
+    if [[ ! -d "$input_dir" ]]; then
+        echo "  ❌ 输入目录不存在，跳过此任务"
+        echo "  ❌ 输入目录不存在，跳过此任务" >> "$batch_log_file"
+        total_failed=$((total_failed + 1))
+        failed_tasks+=("$input_dir (目录不存在)")
+        continue
+    fi
+    
+    # 创建输出目录
+    mkdir -p "$output_dir"
+    
+    # 构建Python命令
+    PYTHON_CMD="python3 \"$PYTHON_SCRIPT\" \
+        --input_dir \"$input_dir\" \
+        --data_dir \"$output_dir\" \
+        $SKIP_FAILED \
+        $SKIP_VIDEO_FAILED"
+    
+    # 添加可选参数（如果需要）
+    # PYTHON_CMD="$PYTHON_CMD --facedet_scale 0.25 --batch_size 20"
+    
+    # 记录开始时间
+    task_start_time=$(date +%s)
+    
+    echo "  开始执行..."
+    echo "  命令: $PYTHON_CMD"
+    echo "  任务开始时间: $(date)"
+    echo ""
+    
+    # 执行命令并记录到日志文件
+    {
+    echo "========================================"
+    echo "任务 $((i+1))/${#tasks[@]} 开始"
+    echo "输入目录: $input_dir"
+    echo "输出目录: $output_dir"
+    echo "开始时间: $(date)"
+    echo "========================================"
+    } >> "$batch_log_file"
+    
+    # 执行Python脚本
+    eval $PYTHON_CMD 2>&1 | tee -a "$batch_log_file"
+    exit_code=${PIPESTATUS[0]}
+    
+    # 记录结束时间
+    task_end_time=$(date +%s)
+    task_duration=$((task_end_time - task_start_time))
+    
+    # 统计结果
+    if [[ $exit_code -eq 0 ]]; then
+        echo "  ✅ 任务完成 (耗时: ${task_duration}秒)"
+        {
+        echo "========================================"
+        echo "任务 $((i+1))/${#tasks[@]} 完成"
+        echo "状态: 成功"
+        echo "耗时: ${task_duration}秒"
+        echo "结束时间: $(date)"
+        echo "========================================"
+        } >> "$batch_log_file"
+        total_success=$((total_success + 1))
+    else
+        echo "  ❌ 任务失败 (耗时: ${task_duration}秒)"
+        {
+        echo "========================================"
+        echo "任务 $((i+1))/${#tasks[@]} 失败"
+        echo "状态: 失败"
+        echo "耗时: ${task_duration}秒"
+        echo "结束时间: $(date)"
+        echo "========================================"
+        } >> "$batch_log_file"
+        total_failed=$((total_failed + 1))
+        failed_tasks+=("$input_dir (退出码: $exit_code)")
+        
+        # 如果开启了skip-video-failed，则终止整个批处理
+        if [[ -n "$SKIP_VIDEO_FAILED" ]]; then
+            echo "  ⚠️ 已开启--skip-video-failed，终止批量处理"
+            echo "  ⚠️ 已开启--skip-video-failed，终止批量处理" >> "$batch_log_file"
+            break
+        fi
+    fi
+    
+    echo "----------------------------------------"
+done
+
+# ==================== 生成汇总报告 ====================
+echo ""
+echo "========================================"
+echo "批量处理完成！"
+echo "总任务数: ${#tasks[@]}"
+echo "成功任务: $total_success"
+echo "失败任务: $total_failed"
+echo "批量日志: $batch_log_file"
+echo "完成时间: $(date)"
+echo "========================================"
+
+if [[ ${#failed_tasks[@]} -gt 0 ]]; then
+    echo ""
+    echo "失败任务详情:"
+    for failed_task in "${failed_tasks[@]}"; do
+        echo "  - $failed_task"
+    done
+fi
+
+# 写入汇总到日志文件
+{
+echo ""
+echo "===== 批量处理汇总 ====="
+echo "总任务数: ${#tasks[@]}"
+echo "成功任务: $total_success"
+echo "失败任务: $total_failed"
+echo "完成时间: $(date)"
+echo "批量日志: $batch_log_file"
+if [[ ${#failed_tasks[@]} -gt 0 ]]; then
+    echo ""
+    echo "失败任务列表:"
+    for failed_task in "${failed_tasks[@]}"; do
+        echo "  - $failed_task"
+    done
+fi
+echo "========================"
+} >> "$batch_log_file"
+
+# 输出日志文件路径（方便后续查看）
+echo ""
+echo "详细日志已保存到: $batch_log_file"
diff --git a/multi_run_automation.py b/multi_run_automation.py
new file mode 100644
index 0000000..3d2a5d8
--- /dev/null
+++ b/multi_run_automation.py
@@ -0,0 +1,301 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+import argparse
+import subprocess
+import os
+import time
+import sys
+from pathlib import Path
+import glob
+
+# ==================== 基础配置 ====================
+# 日志目录
+LOG_DIR = Path("./logs")
+# 要执行的脚本列表
+SCRIPTS = [
+    "run_pipeline.py",
+    "run_syncnet.py",
+    "run_visualise.py"
+]
+# 支持的视频格式（可自行扩展）
+SUPPORTED_VIDEO_EXT = ['.mp4', '.avi', '.mov', '.mkv', '.flv', '.wmv']
+
+# ==================== 工具函数 ====================
+def get_timestamp():
+    """生成时间戳（YYYYMMDD_HHMMSS）"""
+    return time.strftime("%Y%m%d_%H%M%S", time.localtime())
+
+def init_log_dir():
+    """初始化日志目录"""
+    LOG_DIR.mkdir(exist_ok=True, parents=True)
+
+def get_video_files(input_dir, recursive=True):
+    """递归/非递归获取目录下所有支持的视频文件"""
+    video_files = []
+    input_path = Path(input_dir).resolve()
+    
+    if not input_path.exists():
+        print(f"❌ 输入目录不存在: {input_dir}")
+        return video_files
+    
+    # 遍历所有支持的视频格式
+    for ext in SUPPORTED_VIDEO_EXT:
+        glob_pattern = f"**/*{ext}" if recursive else f"*{ext}"
+        files = glob.glob(str(input_path / glob_pattern), recursive=recursive)
+        video_files.extend(files)
+    
+    # 去重并排序
+    video_files = sorted(list(set(video_files)))
+    return video_files
+
+def run_command(cmd, log_file):
+    """执行命令并记录日志"""
+    # 记录命令执行信息
+    log_content = f"\n{'='*50}\n执行命令: {' '.join(cmd)}\n开始时间: {time.ctime()}\n{'='*50}\n"
+    log_file.write(log_content)
+    log_file.flush()
+
+    # 执行命令并捕获输出
+    process = subprocess.Popen(
+        cmd,
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+        text=True,
+        encoding="utf-8"
+    )
+
+    # 实时输出日志
+    while True:
+        line = process.stdout.readline()
+        if not line and process.poll() is not None:
+            break
+        if line:
+            log_file.write(line)
+            log_file.flush()
+            # 同时输出到控制台
+            sys.stdout.write(line)
+            sys.stdout.flush()
+
+    # 记录执行结果
+    return_code = process.returncode
+    result = "成功" if return_code == 0 else "失败"
+    end_log = f"\n{'='*50}\n命令执行{result}，返回码: {return_code}\n结束时间: {time.ctime()}\n{'='*50}\n"
+    log_file.write(end_log)
+    log_file.flush()
+
+    return return_code
+
+# ==================== 参数解析 ====================
+def parse_args():
+    parser = argparse.ArgumentParser(description="SyncNet 批量全管线自动化脚本",
+                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    
+    # ---------------- 批量处理核心参数 ----------------
+    parser.add_argument("--input_dir", type=str, required=True,
+                        help="视频文件目录（批量处理必填，会递归查找所有视频）")
+    parser.add_argument("--no-recursive", action="store_true",
+                        help="是否禁用递归查找（仅处理input_dir下一级目录）")
+    
+    # ---------------- 共用核心参数 ----------------
+    parser.add_argument("--data_dir", type=str, default="data/work",
+                        help="输出数据根目录（每个视频会在该目录下按文件名创建子目录）")
+    
+    # ---------------- run_pipeline.py 特有参数 ----------------
+    parser.add_argument("--facedet_scale", type=float, default=0.25,
+                        help="[pipeline] 人脸检测缩放因子")
+    parser.add_argument("--crop_scale", type=float, default=0.40,
+                        help="[pipeline] 裁剪框缩放因子")
+    parser.add_argument("--min_track", type=int, default=100,
+                        help="[pipeline] 最小人脸跟踪时长")
+    parser.add_argument("--num_failed_det", type=int, default=25,
+                        help="[pipeline] 允许的最大检测失败次数")
+    parser.add_argument("--min_face_size", type=int, default=100,
+                        help="[pipeline] 最小人脸尺寸（像素）")
+    
+    # ---------------- run_syncnet.py 特有参数 ----------------
+    parser.add_argument("--initial_model", type=str, default="data/syncnet_v2.model",
+                        help="[syncnet] 初始模型路径")
+    parser.add_argument("--batch_size", type=int, default=20,
+                        help="[syncnet] 批处理大小")
+    parser.add_argument("--vshift", type=int, default=15,
+                        help="[syncnet] 视频偏移量")
+    
+    # ---------------- run_visualise.py 特有参数 ----------------
+    parser.add_argument("--frame_rate", type=int, default=25,
+                        help="[visualise/pipeline] 帧率")
+    
+    # ---------------- 脚本控制参数 ----------------
+    parser.add_argument("--skip-failed", action="store_true",
+                        help="某个脚本执行失败时，是否跳过该视频的后续脚本")
+    parser.add_argument("--skip-video-failed", action="store_true",
+                        help="某个视频处理失败时，是否跳过下一个视频")
+
+    return parser.parse_args()
+
+# ==================== 主执行逻辑 ====================
+def main():
+    # 1. 解析参数
+    args = parse_args()
+    
+    # 2. 初始化日志目录
+    init_log_dir()
+    
+    # 3. 生成批量日志文件名（带时间戳）
+    timestamp = get_timestamp()
+    batch_log_file_path = LOG_DIR / f"syncnet_batch_automation_{timestamp}.log"
+    
+    # 4. 获取所有视频文件
+    video_files = get_video_files(args.input_dir, not args.no_recursive)
+    if not video_files:
+        print(f"❌ 在目录 {args.input_dir} 下未找到支持的视频文件（支持格式：{SUPPORTED_VIDEO_EXT}）")
+        sys.exit(1)
+    print(f"✅ 共找到 {len(video_files)} 个视频文件，开始批量处理...")
+    
+    # 5. 打开批量日志文件
+    with open(batch_log_file_path, "a", encoding="utf-8") as batch_log_file:
+        # 写入批量执行头部信息
+        batch_log_file.write(f"===== SyncNet 批量自动化管线执行日志 =====\n")
+        batch_log_file.write(f"执行时间: {time.ctime()}\n")
+        batch_log_file.write(f"输入目录: {args.input_dir}\n")
+        batch_log_file.write(f"递归查找: {not args.no_recursive}\n")
+        batch_log_file.write(f"输出根目录: {args.data_dir}\n")
+        batch_log_file.write(f"视频文件数量: {len(video_files)}\n")
+        batch_log_file.write(f"日志文件: {batch_log_file_path}\n")
+        batch_log_file.write(f"==========================================\n\n")
+        batch_log_file.flush()
+
+        # 6. 遍历处理每个视频
+        total_success = 0
+        total_failed = 0
+        failed_videos = []
+        
+        for idx, videofile in enumerate(video_files, 1):
+            # 生成reference（视频文件名，不含路径和后缀）
+            video_path = Path(videofile)
+            reference = video_path.stem  # 核心：用文件名作为reference
+            # 替换特殊字符（避免目录创建失败）
+            reference = reference.replace('/', '_').replace('\\', '_').replace(':', '_').replace('*', '_').replace('?', '_').replace('"', '_').replace('<', '_').replace('>', '_').replace('|', '_')
+            
+            batch_log_file.write(f"\n\n{'='*60}\n开始处理第 {idx}/{len(video_files)} 个视频:\n文件路径: {videofile}\nReference: {reference}\n{'='*60}\n")
+            batch_log_file.flush()
+            print(f"\n\n📌 开始处理第 {idx}/{len(video_files)} 个视频: {videofile} (reference: {reference})")
+
+            # 标记当前视频是否处理成功
+            video_success = True
+
+            # 构造每个脚本的执行命令
+            # 6.1 run_pipeline.py 命令
+            pipeline_cmd = [
+                sys.executable, "run_pipeline.py",
+                "--videofile", videofile,
+                "--reference", reference,
+                "--data_dir", args.data_dir,
+                "--facedet_scale", str(args.facedet_scale),
+                "--crop_scale", str(args.crop_scale),
+                "--min_track", str(args.min_track),
+                "--frame_rate", str(args.frame_rate),
+                "--num_failed_det", str(args.num_failed_det),
+                "--min_face_size", str(args.min_face_size)
+            ]
+
+            # 6.2 run_syncnet.py 命令
+            syncnet_cmd = [
+                sys.executable, "run_syncnet.py",
+                "--videofile", videofile,
+                "--reference", reference,
+                "--data_dir", args.data_dir,
+                "--initial_model", args.initial_model,
+                "--batch_size", str(args.batch_size),
+                "--vshift", str(args.vshift)
+            ]
+
+            # 6.3 run_visualise.py 命令
+            visualise_cmd = [
+                sys.executable, "run_visualise.py",
+                "--videofile", videofile,
+                "--reference", reference,
+                "--data_dir", args.data_dir,
+                "--frame_rate", str(args.frame_rate)
+            ]
+
+            # 按顺序执行脚本
+            scripts_cmds = [
+                ("run_pipeline.py", pipeline_cmd),
+                ("run_syncnet.py", syncnet_cmd),
+                ("run_visualise.py", visualise_cmd)
+            ]
+
+            for script_name, cmd in scripts_cmds:
+                batch_log_file.write(f"\n\n========== 开始执行 {script_name} ==========\n")
+                batch_log_file.flush()
+                
+                # 执行命令
+                return_code = run_command(cmd, batch_log_file)
+                
+                # 检查执行结果
+                if return_code != 0:
+                    video_success = False
+                    batch_log_file.write(f"\n❌ {script_name} 执行失败 (视频: {videofile})\n")
+                    batch_log_file.flush()
+                    print(f"\n❌ {script_name} 执行失败 (视频: {videofile})")
+                    
+                    # 若开启skip-failed，跳过该视频后续脚本
+                    if args.skip_failed:
+                        batch_log_file.write(f"\n⚠️  已开启--skip-failed，跳过该视频后续脚本\n")
+                        batch_log_file.flush()
+                        print(f"\n⚠️  已开启--skip-failed，跳过该视频后续脚本")
+                        break
+
+            # 统计结果
+            if video_success:
+                total_success += 1
+                batch_log_file.write(f"\n✅ 视频 {videofile} 处理完成\n")
+                print(f"\n✅ 视频 {videofile} 处理完成")
+            else:
+                total_failed += 1
+                failed_videos.append(videofile)
+                batch_log_file.write(f"\n❌ 视频 {videofile} 处理失败\n")
+                print(f"\n❌ 视频 {videofile} 处理失败")
+                
+                # 若开启skip-video-failed，跳过下一个视频
+                if args.skip_video_failed:
+                    batch_log_file.write(f"\n⚠️  已开启--skip-video-failed，终止批量处理\n")
+                    batch_log_file.flush()
+                    print(f"\n⚠️  已开启--skip-video-failed，终止批量处理")
+                    break
+
+        # 7. 批量处理完成，写入汇总信息
+        batch_log_file.write(f"\n\n===== 批量处理汇总 =====\n")
+        batch_log_file.write(f"总视频数: {len(video_files)}\n")
+        batch_log_file.write(f"成功数: {total_success}\n")
+        batch_log_file.write(f"失败数: {total_failed}\n")
+        if failed_videos:
+            batch_log_file.write(f"失败视频列表: {failed_videos}\n")
+        batch_log_file.write(f"完成时间: {time.ctime()}\n")
+        batch_log_file.write(f"批量日志文件: {batch_log_file_path}\n")
+        batch_log_file.write(f"========================\n")
+        batch_log_file.flush()
+
+        # 控制台输出汇总
+        print(f"\n\n===== 批量处理汇总 =====")
+        print(f"总视频数: {len(video_files)}")
+        print(f"成功数: {total_success}")
+        print(f"失败数: {total_failed}")
+        if failed_videos:
+            print(f"失败视频列表: {failed_videos}")
+        print(f"批量日志文件: {batch_log_file_path}")
+        print(f"========================")
+
+if __name__ == "__main__":
+    try:
+        main()
+    except Exception as e:
+        print(f"\n❌ 批量脚本执行出错: {str(e)}")
+        # 错误信息写入日志
+        timestamp = get_timestamp()
+        init_log_dir()
+        error_log_path = LOG_DIR / f"syncnet_batch_automation_error_{timestamp}.log"
+        with open(error_log_path, "a", encoding="utf-8") as f:
+            f.write(f"执行出错时间: {time.ctime()}\n")
+            f.write(f"错误信息: {str(e)}\n")
+        sys.exit(1)
diff --git a/run_automation.py b/run_automation.py
new file mode 100644
index 0000000..2fb7c9e
--- /dev/null
+++ b/run_automation.py
@@ -0,0 +1,209 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+import argparse
+import subprocess
+import os
+import time
+import sys
+from pathlib import Path
+
+# ==================== 基础配置 ====================
+# 日志目录
+LOG_DIR = Path("./logs")
+# 要执行的脚本列表
+SCRIPTS = [
+    "run_pipeline.py",
+    "run_syncnet.py",
+    "run_visualise.py"
+]
+
+# ==================== 工具函数 ====================
+def get_timestamp():
+    """生成时间戳（YYYYMMDD_HHMMSS）"""
+    return time.strftime("%Y%m%d_%H%M%S", time.localtime())
+
+def init_log_dir():
+    """初始化日志目录"""
+    LOG_DIR.mkdir(exist_ok=True, parents=True)
+
+def run_command(cmd, log_file):
+    """执行命令并记录日志"""
+    # 记录命令执行信息
+    log_content = f"\n{'='*50}\n执行命令: {' '.join(cmd)}\n开始时间: {time.ctime()}\n{'='*50}\n"
+    log_file.write(log_content)
+    log_file.flush()
+
+    # 执行命令并捕获输出
+    process = subprocess.Popen(
+        cmd,
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+        text=True,
+        encoding="utf-8"
+    )
+
+    # 实时输出日志
+    while True:
+        line = process.stdout.readline()
+        if not line and process.poll() is not None:
+            break
+        if line:
+            log_file.write(line)
+            log_file.flush()
+            # 同时输出到控制台
+            sys.stdout.write(line)
+            sys.stdout.flush()
+
+    # 记录执行结果
+    return_code = process.returncode
+    result = "成功" if return_code == 0 else "失败"
+    end_log = f"\n{'='*50}\n命令执行{result}，返回码: {return_code}\n结束时间: {time.ctime()}\n{'='*50}\n"
+    log_file.write(end_log)
+    log_file.flush()
+
+    return return_code
+
+# ==================== 参数解析 ====================
+def parse_args():
+    parser = argparse.ArgumentParser(description="SyncNet 全管线自动化脚本",
+                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    
+    # ---------------- 共用核心参数 ----------------
+    parser.add_argument("--videofile", type=str, required=True,
+                        help="输入视频文件路径（必填）")
+    parser.add_argument("--reference", type=str, required=True,
+                        help="视频标识名称（必填）")
+    parser.add_argument("--data_dir", type=str, default="data/work",
+                        help="输出数据目录")
+    
+    # ---------------- run_pipeline.py 特有参数 ----------------
+    parser.add_argument("--facedet_scale", type=float, default=0.25,
+                        help="[pipeline] 人脸检测缩放因子")
+    parser.add_argument("--crop_scale", type=float, default=0.40,
+                        help="[pipeline] 裁剪框缩放因子")
+    parser.add_argument("--min_track", type=int, default=100,
+                        help="[pipeline] 最小人脸跟踪时长")
+    parser.add_argument("--num_failed_det", type=int, default=25,
+                        help="[pipeline] 允许的最大检测失败次数")
+    parser.add_argument("--min_face_size", type=int, default=100,
+                        help="[pipeline] 最小人脸尺寸（像素）")
+    
+    # ---------------- run_syncnet.py 特有参数 ----------------
+    parser.add_argument("--initial_model", type=str, default="data/syncnet_v2.model",
+                        help="[syncnet] 初始模型路径")
+    parser.add_argument("--batch_size", type=int, default=20,
+                        help="[syncnet] 批处理大小")
+    parser.add_argument("--vshift", type=int, default=15,
+                        help="[syncnet] 视频偏移量")
+    
+    # ---------------- run_visualise.py 特有参数 ----------------
+    parser.add_argument("--frame_rate", type=int, default=25,
+                        help="[visualise/pipeline] 帧率")
+    
+    # ---------------- 脚本控制参数 ----------------
+    parser.add_argument("--skip-failed", action="store_true",
+                        help="某个脚本执行失败时，是否跳过后续脚本")
+    parser.add_argument("--log-level", type=str, default="all",
+                        choices=["all", "error"],
+                        help="日志级别：all(全部输出) / error(仅错误)")
+
+    return parser.parse_args()
+
+# ==================== 主执行逻辑 ====================
+def main():
+    # 1. 解析参数
+    args = parse_args()
+    
+    # 2. 初始化日志目录
+    init_log_dir()
+    
+    # 3. 生成日志文件名（带时间戳）
+    timestamp = get_timestamp()
+    log_file_path = LOG_DIR / f"syncnet_automation_{timestamp}.log"
+    
+    # 4. 打开日志文件
+    with open(log_file_path, "a", encoding="utf-8") as log_file:
+        # 写入脚本执行头部信息
+        log_file.write(f"===== SyncNet 自动化管线执行日志 =====\n")
+        log_file.write(f"执行时间: {time.ctime()}\n")
+        log_file.write(f"视频文件: {args.videofile}\n")
+        log_file.write(f"参考名称: {args.reference}\n")
+        log_file.write(f"数据目录: {args.data_dir}\n")
+        log_file.write(f"日志文件: {log_file_path}\n")
+        log_file.write(f"=======================================\n\n")
+        log_file.flush()
+
+        # 5. 构造每个脚本的执行命令
+        # 5.1 run_pipeline.py 命令
+        pipeline_cmd = [
+            sys.executable, "run_pipeline.py",
+            "--videofile", args.videofile,
+            "--reference", args.reference,
+            "--data_dir", args.data_dir,
+            "--facedet_scale", str(args.facedet_scale),
+            "--crop_scale", str(args.crop_scale),
+            "--min_track", str(args.min_track),
+            "--frame_rate", str(args.frame_rate),
+            "--num_failed_det", str(args.num_failed_det),
+            "--min_face_size", str(args.min_face_size)
+        ]
+
+        # 5.2 run_syncnet.py 命令
+        syncnet_cmd = [
+            sys.executable, "run_syncnet.py",
+            "--videofile", args.videofile,
+            "--reference", args.reference,
+            "--data_dir", args.data_dir,
+            "--initial_model", args.initial_model,
+            "--batch_size", str(args.batch_size),
+            "--vshift", str(args.vshift)
+        ]
+
+        # 5.3 run_visualise.py 命令
+        visualise_cmd = [
+            sys.executable, "run_visualise.py",
+            "--videofile", args.videofile,
+            "--reference", args.reference,
+            "--data_dir", args.data_dir,
+            "--frame_rate", str(args.frame_rate)
+        ]
+
+        # 6. 按顺序执行脚本
+        scripts_cmds = [
+            ("run_pipeline.py", pipeline_cmd),
+            ("run_syncnet.py", syncnet_cmd),
+            ("run_visualise.py", visualise_cmd)
+        ]
+
+        for script_name, cmd in scripts_cmds:
+            log_file.write(f"\n\n========== 开始执行 {script_name} ==========\n")
+            log_file.flush()
+            
+            # 执行命令
+            return_code = run_command(cmd, log_file)
+            
+            # 检查执行结果，若失败且开启skip-failed则退出
+            if return_code != 0 and args.skip_failed:
+                log_file.write(f"\n{script_name} 执行失败，已开启--skip-failed，终止后续执行\n")
+                print(f"\n❌ {script_name} 执行失败，日志文件: {log_file_path}")
+                sys.exit(return_code)
+
+        # 7. 执行完成
+        log_file.write(f"\n\n===== 所有脚本执行完成 =====\n")
+        log_file.write(f"完成时间: {time.ctime()}\n")
+        log_file.write(f"日志文件: {log_file_path}\n")
+        print(f"\n✅ 全管线执行完成！日志文件: {log_file_path}")
+
+if __name__ == "__main__":
+    try:
+        main()
+    except Exception as e:
+        print(f"\n❌ 脚本执行出错: {str(e)}")
+        # 错误信息写入日志
+        timestamp = get_timestamp()
+        init_log_dir()
+        error_log_path = LOG_DIR / f"syncnet_automation_error_{timestamp}.log"
+        with open(error_log_path, "a", encoding="utf-8") as f:
+            f.write(f"执行出错时间: {time.ctime()}\n")
+            f.write(f"错误信息: {str(e)}\n")
+        sys.exit(1)
diff --git a/run_syncnet_update.py b/run_syncnet_update.py
new file mode 100644
index 0000000..3514a93
--- /dev/null
+++ b/run_syncnet_update.py
@@ -0,0 +1,75 @@
+#!/usr/bin/python
+#-*- coding: utf-8 -*-
+
+import time, pdb, argparse, subprocess, pickle, os, gzip, glob
+import numpy as np  # 新增：导入numpy
+
+from SyncNetInstance import *
+
+# ==================== PARSE ARGUMENT ====================
+
+parser = argparse.ArgumentParser(description = "SyncNet");
+parser.add_argument('--initial_model', type=str, default="data/syncnet_v2.model", help='');
+parser.add_argument('--batch_size', type=int, default='20', help='');
+parser.add_argument('--vshift', type=int, default='15', help='');
+parser.add_argument('--data_dir', type=str, default='data/work', help='');
+parser.add_argument('--videofile', type=str, default='', help='');
+parser.add_argument('--reference', type=str, default='', help='');
+opt = parser.parse_args();
+
+setattr(opt,'avi_dir',os.path.join(opt.data_dir,'pyavi'))
+setattr(opt,'tmp_dir',os.path.join(opt.data_dir,'pytmp'))
+setattr(opt,'work_dir',os.path.join(opt.data_dir,'pywork'))
+setattr(opt,'crop_dir',os.path.join(opt.data_dir,'pycrop'))
+
+
+# ==================== LOAD MODEL AND FILE LIST ====================
+
+s = SyncNetInstance();
+
+s.loadParameters(opt.initial_model);
+print("Model %s loaded."%opt.initial_model);
+
+flist = glob.glob(os.path.join(opt.crop_dir,opt.reference,'0*.avi'))
+flist.sort()
+
+# ==================== GET OFFSETS ====================
+
+dists = []
+offsets_list = []  # 新增：存储每个裁剪视频的偏移值
+confidences_list = []  # 新增：存储每个裁剪视频的置信度
+
+for idx, fname in enumerate(flist):
+    print(f"\nProcessing crop video {idx}: {fname}")
+    offset, conf, dist = s.evaluate(opt,videofile=fname)
+    dists.append(dist)
+    offsets_list.append(offset)  # 保存偏移值
+    confidences_list.append(conf)  # 保存置信度
+
+# ==================== SAVE ACTIVESD.PCKL ====================
+
+with open(os.path.join(opt.work_dir,opt.reference,'activesd.pckl'), 'wb') as fil:
+    pickle.dump(dists, fil)
+print(f"\nSaved raw distance matrix to: {os.path.join(opt.work_dir,opt.reference,'activesd.pckl')}")
+
+# ==================== 新增：解析并生成 offsets.txt ====================
+def generate_offsets_txt(opt, offsets, confidences):
+    """从activesd.pckl的原始数据/直接结果生成offsets.txt"""
+    txt_path = os.path.join(opt.work_dir, opt.reference, 'offsets.txt')
+    frame_rate = 25  # 固定帧率（与run_pipeline.py一致）
+    
+    with open(txt_path, 'w', encoding='utf-8') as f:
+        # 写入表头
+        f.write("track_id\toffset_frames\toffset_seconds\tconfidence\n")
+        # 写入每个裁剪视频的结果
+        for track_id, (offset, conf) in enumerate(zip(offsets, confidences)):
+            offset_sec = offset / frame_rate  # 转换为秒
+            f.write(f"{track_id}\t{offset}\t{offset_sec:.4f}\t{conf:.4f}\n")
+    
+    print(f"Saved offset results to: {txt_path}")
+    print("\n=== Final Offset Summary ===")
+    for track_id, (offset, conf) in enumerate(zip(offsets, confidences)):
+        print(f"Track {track_id}: Offset = {offset} frames ({offset/25:.4f} sec), Confidence = {conf:.4f}")
+
+# 调用生成函数
+generate_offsets_txt(opt, offsets_list, confidences_list)
diff --git a/run_syncnet_update_0116.py b/run_syncnet_update_0116.py
new file mode 100644
index 0000000..b6f42ce
--- /dev/null
+++ b/run_syncnet_update_0116.py
@@ -0,0 +1,86 @@
+#!/usr/bin/python
+#-*- coding: utf-8 -*-
+
+import time, pdb, argparse, subprocess, pickle, os, gzip, glob
+import numpy as np  # 新增：导入numpy
+
+from SyncNetInstance import *
+
+# ==================== PARSE ARGUMENT ====================
+
+parser = argparse.ArgumentParser(description = "SyncNet");
+parser.add_argument('--initial_model', type=str, default="data/syncnet_v2.model", help='');
+parser.add_argument('--batch_size', type=int, default='20', help='');
+parser.add_argument('--vshift', type=int, default='15', help='');
+parser.add_argument('--data_dir', type=str, default='data/work', help='');
+parser.add_argument('--videofile', type=str, default='', help='');
+parser.add_argument('--reference', type=str, default='', help='');
+opt = parser.parse_args();
+
+setattr(opt,'avi_dir',os.path.join(opt.data_dir,'pyavi'))
+setattr(opt,'tmp_dir',os.path.join(opt.data_dir,'pytmp'))
+setattr(opt,'work_dir',os.path.join(opt.data_dir,'pywork'))
+setattr(opt,'crop_dir',os.path.join(opt.data_dir,'pycrop'))
+
+
+# ==================== LOAD MODEL AND FILE LIST ====================
+
+s = SyncNetInstance();
+
+s.loadParameters(opt.initial_model);
+print("Model %s loaded."%opt.initial_model);
+
+flist = glob.glob(os.path.join(opt.crop_dir,opt.reference,'0*.avi'))
+flist.sort()
+
+# ==================== GET OFFSETS ====================
+
+dists = []
+offsets_list = []  # 存储每个裁剪视频的偏移值
+confidences_list = []  # 存储每个裁剪视频的置信度
+avg_min_dist_list = []  # 新增：存储每个裁剪视频的「最优偏移平均同步差」
+
+for idx, fname in enumerate(flist):
+    print(f"\nProcessing crop video {idx}: {fname}")
+    offset, conf, dist = s.evaluate(opt,videofile=fname)
+    dists.append(dist)
+    offsets_list.append(offset)
+    confidences_list.append(conf)
+    
+    # 新增：计算当前track的「最优偏移平均同步差（最小距离）」
+    # dist.shape = [帧数, 2*vshift+1] → 按帧取最小距离 → 求均值
+    min_vals_per_frame = np.min(dist, axis=1)  # 每帧的最小距离（最优偏移对应的距离）
+    avg_min_dist = np.mean(min_vals_per_frame) # 所有帧的最小距离均值（平均同步差）
+    avg_min_dist_list.append(avg_min_dist)
+    print(f"Track {idx} - 最优偏移平均同步差（最小距离）: {avg_min_dist:.4f}")
+
+# ==================== SAVE ACTIVESD.PCKL ====================
+
+with open(os.path.join(opt.work_dir,opt.reference,'activesd.pckl'), 'wb') as fil:
+    pickle.dump(dists, fil)
+print(f"\nSaved raw distance matrix to: {os.path.join(opt.work_dir,opt.reference,'activesd.pckl')}")
+
+# ==================== 生成 offsets.txt（含平均同步差） ====================
+def generate_offsets_txt(opt, offsets, confidences, avg_min_dists):
+    """生成包含偏移、置信度、平均同步差的offsets.txt"""
+    txt_path = os.path.join(opt.work_dir, opt.reference, 'offsets.txt')
+    frame_rate = 25  # 固定帧率（与run_pipeline.py一致）
+    
+    with open(txt_path, 'w', encoding='utf-8') as f:
+        # 新增表头：avg_min_dist（最优偏移平均同步差）
+        f.write("track_id\toffset_frames\toffset_seconds\tconfidence\tavg_min_dist\n")
+        # 写入每个track的结果
+        for track_id, (offset, conf, avg_min) in enumerate(zip(offsets, confidences, avg_min_dists)):
+            offset_sec = offset / frame_rate  # 偏移转换为秒
+            f.write(f"{track_id}\t{offset}\t{offset_sec:.4f}\t{conf:.4f}\t{avg_min:.4f}\n")
+    
+    print(f"Saved offset results to: {txt_path}")
+    print("\n=== Final Offset Summary ===")
+    for track_id, (offset, conf, avg_min) in enumerate(zip(offsets, confidences, avg_min_dists)):
+        print(f"Track {track_id}: "
+              f"Offset = {offset} frames ({offset/25:.4f} sec), "
+              f"Confidence = {conf:.4f}, "
+              f"Avg Min Dist (平均同步差) = {avg_min:.4f}")
+
+# 调用生成函数（传入新增的avg_min_dist_list）
+generate_offsets_txt(opt, offsets_list, confidences_list, avg_min_dist_list)
diff --git a/utils/syncnet_summary_mean_by_linecount.py b/utils/syncnet_summary_mean_by_linecount.py
new file mode 100644
index 0000000..9cbfeea
--- /dev/null
+++ b/utils/syncnet_summary_mean_by_linecount.py
@@ -0,0 +1,171 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+import os
+import argparse
+import numpy as np
+from pathlib import Path
+from collections import Counter
+
+def parse_offsets_txt(txt_path):
+    """
+    读取单个offsets.txt文件，返回：
+    - 有效数据行数量（不含表头）
+    - 按confidence降序排序后的列数据 (sorted_offset_frames, sorted_offset_seconds, sorted_confidence)
+    """
+    offset_frames = []
+    offset_seconds = []
+    confidence = []
+    
+    try:
+        with open(txt_path, 'r', encoding='utf-8') as f:
+            # 跳过表头
+            header = f.readline().strip()
+            if not header.startswith("track_id"):
+                print(f"⚠️  {txt_path} 表头格式异常，跳过")
+                return (0, None)
+            
+            # 读取数据行
+            for line in f:
+                line = line.strip()
+                if not line:
+                    continue
+                # 按制表符/空格分割（兼容不同分隔符）
+                parts = line.split()
+                if len(parts) < 4:
+                    print(f"⚠️  {txt_path} 行数据异常：{line}，跳过")
+                    continue
+                # 提取数值（忽略track_id）
+                try:
+                    of = int(parts[1])
+                    os_val = float(parts[2])
+                    conf = float(parts[3])
+                    offset_frames.append(of)
+                    offset_seconds.append(os_val)
+                    confidence.append(conf)
+                except ValueError:
+                    print(f"⚠️  {txt_path} 数值转换失败：{line}，跳过")
+                    continue
+        
+        # 统计有效数据行数量
+        data_line_count = len(confidence)
+        if data_line_count == 0:
+            print(f"⚠️  {txt_path} 无有效数据行，跳过")
+            return (0, None)
+        
+        # 按confidence降序排序（所有列同步排序）
+        sorted_indices = np.argsort(confidence)[::-1]
+        sorted_of = [offset_frames[i] for i in sorted_indices]
+        sorted_os = [offset_seconds[i] for i in sorted_indices]
+        sorted_conf = [confidence[i] for i in sorted_indices]
+        
+        return (data_line_count, (sorted_of, sorted_os, sorted_conf))
+    
+    except Exception as e:
+        print(f"❌ 读取 {txt_path} 失败：{str(e)}")
+        return (0, None)
+
+def main():
+    # 解析命令行参数
+    parser = argparse.ArgumentParser(description="汇总SyncNet批量处理结果的均值（按数据行数量分组计算）")
+    parser.add_argument("--output_dir", type=str, required=True,
+                        help="SyncNet的输出根目录（如/path/to/output/）")
+    args = parser.parse_args()
+    
+    # 校验输出目录
+    output_root = Path(args.output_dir).resolve()
+    pywork_dir = output_root / "pywork"
+    if not pywork_dir.exists():
+        print(f"❌ 未找到pywork目录：{pywork_dir}")
+        return
+    
+    # 第一步：遍历所有视频子目录，收集数据行数量和排序后的数据
+    all_data = {}  # key: 视频名称, value: (data_line_count, sorted_data)
+    video_names = []  # 记录所有找到的视频名称
+    
+    for video_subdir in pywork_dir.iterdir():
+        if not video_subdir.is_dir():
+            continue
+        offsets_txt = video_subdir / "offsets.txt"
+        if not offsets_txt.exists():
+            print(f"⚠️  {video_subdir.name} 目录下无offsets.txt，跳过")
+            continue
+        
+        # 读取该视频的offsets.txt
+        data_line_count, sorted_data = parse_offsets_txt(str(offsets_txt))
+        if data_line_count == 0 or sorted_data is None:
+            continue
+        
+        all_data[video_subdir.name] = (data_line_count, sorted_data)
+        video_names.append(video_subdir.name)
+    
+    if not all_data:
+        print(f"❌ 未找到任何有效offsets.txt文件")
+        return
+    
+    # 第二步：按数据行数量分组
+    grouped_data = {}  # key: 数据行数量, value: {"videos": [视频名列表], "data": [排序后数据列表]}
+    for video_name, (line_count, sorted_data) in all_data.items():
+        if line_count not in grouped_data:
+            grouped_data[line_count] = {"videos": [], "data": []}
+        grouped_data[line_count]["videos"].append(video_name)
+        grouped_data[line_count]["data"].append(sorted_data)
+    
+    # 打印分组统计
+    print(f"\n===== 数据行数量分组统计 =====")
+    for line_count in sorted(grouped_data.keys()):
+        video_list = grouped_data[line_count]["videos"]
+        print(f"数据行数量 {line_count}：共 {len(video_list)} 个文件 → {', '.join(video_list)}")
+    
+    # 第三步：逐组计算均值
+    output_txt = output_root / "syncnet_summary_mean_by_linecount.txt"
+    with open(output_txt, 'w', encoding='utf-8') as f:
+        # 写入总统计信息
+        f.write("===== SyncNet批量结果均值汇总（按数据行数量分组）=====\n")
+        f.write(f"汇总时间：{os.popen('date').read().strip()}\n")
+        f.write(f"输出根目录：{output_root}\n\n")
+        
+        # 逐组写入结果
+        for line_count in sorted(grouped_data.keys()):
+            group_info = grouped_data[line_count]
+            video_list = group_info["videos"]
+            data_list = group_info["data"]
+            group_size = len(video_list)
+            
+            # 写入分组表头
+            f.write(f"{'='*60}\n")
+            f.write(f"分组：数据行数量 = {line_count} 行\n")
+            f.write(f"参与计算的视频数：{group_size}\n")
+            f.write(f"参与计算的视频名称：{', '.join(video_list)}\n")
+            f.write(f"{'='*60}\n")
+            
+            # 写入该组均值表头
+            f.write("排序索引\t均值_offset_frames\t均值_offset_seconds\t均值_confidence\t参与计算的视频数\n")
+            
+            # 计算该组均值（数据行数量一致，直接按索引遍历）
+            for idx in range(line_count):
+                # 收集该索引下所有文件的数值
+                of_vals = [d[0][idx] for d in data_list]
+                os_vals = [d[1][idx] for d in data_list]
+                conf_vals = [d[2][idx] for d in data_list]
+                
+                # 计算均值
+                mean_of = np.mean(of_vals)
+                mean_os = np.mean(os_vals)
+                mean_conf = np.mean(conf_vals)
+                
+                # 写入该行均值
+                f.write(f"{idx+1}\t{mean_of:.4f}\t{mean_os:.4f}\t{mean_conf:.4f}\t{group_size}\n")
+            
+            f.write("\n")  # 组间空行分隔
+    
+    # 控制台输出完成信息
+    print(f"\n✅ 汇总完成！结果已保存到：{output_txt}")
+    print(f"\n===== 最终汇总结果预览 =====")
+    with open(output_txt, 'r', encoding='utf-8') as f:
+        preview = f.read().splitlines()[:20]  # 预览前20行
+        print("\n".join(preview))
+        if len(f.read().splitlines()) > 20:
+            print("...（更多内容请查看完整文件）")
+
+if __name__ == "__main__":
+    main()