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2026-01-12 18:30:12 +08:00
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.gitignore vendored Normal file
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# 忽略所有 .md 文件
*.md
# 忽略 doc 和 docs 目录
doc/
docs/
# Python 相关
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
*.egg-info/
dist/
build/
# 虚拟环境
venv/
env/
ENV/
# IDE
.vscode/
.idea/
*.swp
*.swo
# 系统文件
.DS_Store
Thumbs.db

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{
"vmware": {
"vmx_path": "/Volumes/Castor/虚拟机/Jade_Win_11.vmwarevm/Windows 11 64 位 ARM 2.vmx",
"snapshot_name": "快照 3",
"vm_password": "lizhanyuan",
"guest_username": "lzy",
"guest_password": "LIZHANYUAN"
},
"network": {
"vm_ip": "192.168.116.149",
"agent_server_port": 5000
},
"paths": {
"guest_desktop": "C:\\Users\\lzy\\Desktop"
},
"notes": {
"vm_auto_login": "已启用（UI自动登录）",
"guest_auth": ""
}
}

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04/21/92 DIF Demo01: 37-1497 4-733 9-169 18-303 9-77
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#!/bin/bash
# JADE Benchmark 便捷运行脚本
PROJECT_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
cd "$PROJECT_ROOT"
# 激活虚拟环境（如果存在）
if [ -f "../Jade-Benchmark/.venv/bin/activate" ]; then
source "../Jade-Benchmark/.venv/bin/activate"
fi
# 主命令分发
case "$1" in
init)
python scripts/tools/init_task.py "${@:2}"
;;
collect)
python scripts/tools/collect_task.py "${@:2}"
;;
process)
python scripts/tools/process_trajectory.py "${@:2}"
;;
eval)
python scripts/tools/run_eval.py "${@:2}"
;;
test-vm)
python scripts/core/jade_env.py
;;
help|--help|-h)
cat << EOF
JADE Benchmark 工具集
用法: ./jade <命令> [参数]
命令:
init <task_id> 创建新任务
collect <task_id> [选项] 采集任务数据
process <task_id> [选项] 处理轨迹坐标
eval <task_id|--all> [选项] 运行评测
test-vm 测试VM连接
示例:
./jade init smoothing_001
./jade collect smoothing_001 --mode full
./jade process smoothing_001
./jade eval smoothing_001
./jade eval --all --output results.json
详细文档:
- QUICK_START.md
- MIGRATION_GUIDE.md
- scripts/README.md
EOF
;;
*)
echo "❌ 未知命令: $1"
echo "运行 './jade help' 查看帮助"
exit 1
;;
esac

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numpy
requests
pillow
pynput
flask
pyautogui

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"""
JADE Benchmark Scripts Package
"""

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"""
JADE Benchmark 核心模块
包含VM控制、轨迹录制、评测等核心功能
"""
from .jade_env import JadeEnv
from .recorder import Recorder, record_interactive
from .evaluator import evaluate, load_xrd_data
__all__ = [
'JadeEnv',
'Recorder',
'record_interactive',
'evaluate',
'load_xrd_data'
]

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import json
import numpy as np
import os
import sys
import re
def load_xrd_data(file_path):
"""
读取 XRD 导出的 txt 文件（如 background_result.txt），跳过头部的 Metadata。
"""
if not os.path.exists(file_path):
raise FileNotFoundError(f"文件不存在: {file_path}")
data = []
with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
for line in f:
line = line.strip()
if not line:
continue
parts = line.split()
try:
values = [float(x) for x in parts]
if len(values) >= 2:
data.append(values[:2])
except ValueError:
continue
if not data:
raise ValueError(f"在文件中未找到有效的数值数据: {file_path}")
return np.array(data)
def load_peak_report(file_path):
"""
专门解析 JADE Peak Search Report (.pid)
提取表格部分的数值数据
"""
if not os.path.exists(file_path):
raise FileNotFoundError(f"文件不存在: {file_path}")
peaks = []
metadata = {}
with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
lines = f.readlines()
# 查找表格开始的位置（在含有 "2-Theta" 的行之后）
table_started = False
for line in lines:
line = line.strip()
if not line:
continue
# 提取元数据示例: JADE: Peak Search Report (72 Peaks, Max P/N = 19.3)
if "Peak Search Report" in line:
match = re.search(r"\((\d+) Peaks", line)
if match:
metadata['reported_peak_count'] = int(match.group(1))
# 识别表头
if "2-Theta" in line and "d(" in line:
table_started = True
continue
if table_started:
parts = line.split()
# Peak 报告通常每行有 8-9 个字段
if len(parts) >= 6:
try:
# 尝试将前几个字段转为 float
# 0: 2-Theta, 1: d, 2: BG, 3: Height, 4: I%, 5: Area, 6: I%, 7: FWHM
peak_data = [float(p) for p in parts[:8]]
peaks.append(peak_data)
except ValueError:
# 如果转换失败，可能是说明文字或空行，跳过
continue
if not peaks:
raise ValueError(f"未能从 Peak 报告中解析出有效数据: {file_path}")
return np.array(peaks), metadata
def evaluate(gt_path, agent_path, tolerance=1e-4, mode="xrd_data"):
"""
对比 Ground Truth 和 Agent Output。
支持两种模式:
- xrd_data: 对比 (2-Theta, Intensity) 原始数据点
- peak_report: 对比 Peak 搜索结果列表
"""
try:
if mode == "peak_report":
gt_data, gt_meta = load_peak_report(gt_path)
agent_data, agent_meta = load_peak_report(agent_path)
# 对于 Peak 报告，我们主要关注 2-Theta 位置和 Height
# 这里对比全表，但放宽容差，因为 Peak Search 的算法可能在不同环境下有极细微差异
if gt_data.shape != agent_data.shape:
# 如果数量不匹配，直接判定失败
return 0, f"失败: Peak 数量不匹配。GT {len(gt_data)}, Agent {len(agent_data)}"
else:
gt_data = load_xrd_data(gt_path)
agent_data = load_xrd_data(agent_path)
if gt_data.shape != agent_data.shape:
return 0, f"失败: 数据维度不匹配。GT 形状 {gt_data.shape}, Agent 形状 {agent_data.shape}"
diff = np.abs(gt_data - agent_data)
max_error = np.max(diff)
if max_error < tolerance:
return 1, f"成功: 最大绝对误差 {max_error:.2e} < 阈值 {tolerance}"
else:
return 0, f"失败: 最大绝对误差 {max_error:.2e} 超过阈值 {tolerance}"
except Exception as e:
return 0, f"错误: {str(e)}"
def evaluate_by_config(config_path):
"""
根据任务配置文件进行评测。
"""
# 兼容性处理：如果传入的是任务 ID 路径
if not os.path.isabs(config_path) and not config_path.startswith('.'):
# 尝试补全路径，例如 instructions/smoothing_001.json
if not config_path.endswith('.json'):
config_path = config_path + ".json"
if not os.path.exists(config_path):
# 尝试在任务目录下找
pass
base_dir = os.path.dirname(os.path.dirname(os.path.abspath(config_path)))
with open(config_path, 'r', encoding='utf-8') as f:
config = json.load(f)
eval_cfg = config.get('evaluation', {})
# 处理相对路径
gt_path = os.path.join(base_dir, eval_cfg['ground_truth'])
agent_path = os.path.join(base_dir, eval_cfg['target_output'])
tolerance = eval_cfg.get('tolerance', 1e-4)
# 自动识别模式
mode = "xrd_data"
if gt_path.lower().endswith('.pid') or eval_cfg.get('type') == 'peak_report':
mode = "peak_report"
# Peak 报告的默认容差放宽一些，因为算法可能受环境微小影响
if 'tolerance' not in eval_cfg:
tolerance = 1e-2
print(f"--- 正在执行评测: {config.get('id', 'unknown')} ---")
print(f"指令: {config.get('instruction')}")
print(f"模式: {mode}")
score, message = evaluate(gt_path, agent_path, tolerance, mode=mode)
return score, message
if __name__ == "__main__":
if len(sys.argv) == 2 and sys.argv[1].endswith('.json'):
# 模式 A: Config 模式
try:
score, message = evaluate_by_config(sys.argv[1])
except Exception as e:
print(f"配置文件解析失败: {e}")
sys.exit(1)
elif len(sys.argv) >= 3:
# 模式 B: 直接对比模式
score, message = evaluate(sys.argv[1], sys.argv[2])
else:
print("用法:")
print(" python scripts/evaluator.py instructions/smoothing_001.json")
print(" python scripts/evaluator.py <gt_file_path> <agent_file_path>")
sys.exit(1)
print("-" * 30)
print(f"Score: {score}")
print(f"Reason: {message}")
print("-" * 30)
sys.exit(0 if score == 1 else 1)

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"""
JADE Benchmark 环境控制器
负责VM的重置、文件注入/收集、截图获取等操作
"""
import subprocess
import time
import os
import requests
from PIL import Image
import io
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class JadeEnv:
"""轻量级JADE虚拟机环境控制器"""
def __init__(self, vmx_path, snapshot_name="Jade_Ready", vm_ip="192.168.116.129",
vm_password=None, guest_username=None, guest_password=None):
"""
初始化JADE环境
Args:
vmx_path: 虚拟机.vmx文件路径
snapshot_name: 快照名称
vm_ip: 虚拟机IP地址（用于HTTP通信）
vm_password: 虚拟机文件加密密码（-vp参数）
guest_username: 虚拟机内操作系统用户名（-gu参数）
guest_password: 虚拟机内操作系统密码（-gp参数）
"""
self.vmx_path = vmx_path
self.snapshot_name = snapshot_name
self.vm_ip = vm_ip
self.vm_url = f"http://{vm_ip}:5000"
# VMware认证参数
self.vm_password = vm_password
self.guest_username = guest_username
self.guest_password = guest_password
# VMware Fusion路径（macOS）
self.vmrun = "/Applications/VMware Fusion.app/Contents/Library/vmrun"
# 虚拟机内路径
self.guest_desktop = r"C:\Users\lzy\Desktop"
logger.info(f"JadeEnv初始化: VM={os.path.basename(vmx_path)}, Snapshot={snapshot_name}")
logger.info(f" 认证配置: vm_password={'已设置' if vm_password else '未设置'}, "
f"guest_user={'已设置' if guest_username else '未设置'}, "
f"guest_pass={'已设置' if guest_password else '未设置'}")
def _build_vmrun_cmd(self, *args):
"""构建vmrun命令"""
cmd = [self.vmrun, "-T", "fusion"]
# 添加认证参数
if self.vm_password:
cmd.extend(["-vp", self.vm_password])
if self.guest_username:
cmd.extend(["-gu", self.guest_username])
if self.guest_password:
cmd.extend(["-gp", self.guest_password])
cmd.extend(args)
return cmd
def _run_vmrun(self, *args, check=True, timeout=30):
"""执行vmrun命令"""
cmd = self._build_vmrun_cmd(*args)
# 打印完整命令（隐藏密码）
cmd_display = []
skip_next = False
for i, part in enumerate(cmd):
if skip_next:
cmd_display.append("***")
skip_next = False
elif part in ["-vp", "-gp"]:
cmd_display.append(part)
skip_next = True
else:
cmd_display.append(part)
logger.info(f"执行vmrun命令: {' '.join(cmd_display)}")
try:
result = subprocess.run(
cmd,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
text=True,
timeout=timeout
)
if check and result.returncode != 0:
error_msg = result.stderr or result.stdout
raise RuntimeError(f"vmrun命令执行失败: {error_msg}")
return result
except subprocess.TimeoutExpired:
logger.error(f"❌ vmrun命令超时（{timeout}秒）")
raise RuntimeError(f"vmrun命令执行超时（{timeout}秒）")
def _detect_and_update_ip(self):
"""
检测VM的IP地址，如果变化则自动更新
Returns:
bool: IP是否发生变化
"""
logger.info("🔍 检测VM IP地址...")
try:
# 使用vmrun获取VM IP
cmd = self._build_vmrun_cmd("getGuestIPAddress", self.vmx_path)
result = subprocess.run(
cmd,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
text=True,
timeout=10
)
if result.returncode == 0:
new_ip = result.stdout.strip()
if new_ip and new_ip != "":
if new_ip != self.vm_ip:
logger.info(f"⚠️ IP地址已变化: {self.vm_ip} → {new_ip}")
logger.info(f" 自动更新IP地址...")
# 更新实例变量
self.vm_ip = new_ip
self.vm_url = f"http://{new_ip}:5000"
# 更新配置文件
try:
import json
from pathlib import Path
# 获取项目根目录（jade_env.py在scripts/core/，向上3级到项目根目录）
project_root = Path(__file__).parent.parent.parent
config_path = project_root / "config.json"
if config_path.exists():
with open(config_path, 'r', encoding='utf-8') as f:
config = json.load(f)
config["network"]["vm_ip"] = new_ip
with open(config_path, 'w', encoding='utf-8') as f:
json.dump(config, f, indent=2, ensure_ascii=False)
logger.info(f"✅ 配置文件已更新: {config_path}")
except Exception as e:
logger.warning(f"⚠️ 更新配置文件失败: {e}（不影响使用）")
return True
else:
logger.info(f"✅ IP地址未变化: {self.vm_ip}")
return False
else:
logger.warning(f"⚠️ vmrun返回空IP地址")
return False
else:
error_msg = result.stderr or result.stdout
logger.warning(f"⚠️ 获取IP失败: {error_msg}（将使用配置中的IP）")
return False
except subprocess.TimeoutExpired:
logger.warning(f"⚠️ 获取IP超时（将使用配置中的IP）")
return False
except Exception as e:
logger.warning(f"⚠️ 检测IP异常: {e}（将使用配置中的IP）")
return False
def reset(self, wait_time=5):
"""
重置环境：恢复快照并启动虚拟机
Args:
wait_time: 启动后等待时间（秒）
"""
logger.info(f"正在恢复快照: {self.snapshot_name}...")
try:
# 1. 恢复快照
self._run_vmrun("revertToSnapshot", self.vmx_path, self.snapshot_name)
logger.info("✅ 快照恢复成功")
time.sleep(3)
# 2. 启动虚拟机（如果未运行）
logger.info("正在启动虚拟机...")
result = self._run_vmrun("start", self.vmx_path, check=False)
if result.returncode == 0:
logger.info("✅ 虚拟机启动成功")
else:
# 可能已经在运行
if "is already running" in result.stderr.lower():
logger.info("✅ 虚拟机已在运行")
else:
logger.warning(f"启动虚拟机警告: {result.stderr}")
# 3. 等待系统稳定（快照恢复后agent_server已在运行）
logger.info(f"等待系统稳定 ({wait_time}秒)...")
time.sleep(wait_time)
# 4. 检测并更新IP地址（恢复快照后IP可能变化）
self._detect_and_update_ip()
# 5. 验证HTTP服务可用
self._wait_for_http_service()
logger.info("✅ 环境重置完成")
except Exception as e:
logger.error(f"❌ 环境重置失败: {e}")
raise
def _wait_for_http_service(self, max_retries=10, retry_interval=3):
"""等待agent_server.py HTTP服务可用"""
logger.info(f"等待虚拟机HTTP服务... (URL: {self.vm_url})")
# 绕过代理（避免Clash等代理工具干扰局域网访问）
proxies = {
'http': None,
'https': None
}
for i in range(max_retries):
try:
logger.debug(f"尝试连接: {self.vm_url}/screen_info (timeout=5秒, 不使用代理)")
response = requests.get(f"{self.vm_url}/screen_info", timeout=5, proxies=proxies)
logger.debug(f"收到响应: status_code={response.status_code}")
if response.status_code == 200:
logger.info("✅ HTTP服务已就绪")
return True
else:
logger.warning(f"HTTP状态码异常: {response.status_code}")
except requests.exceptions.Timeout as e:
logger.info(f"HTTP服务未就绪（超时），重试 {i+1}/{max_retries}...")
if i < max_retries - 1:
time.sleep(retry_interval)
except requests.exceptions.ConnectionError as e:
logger.info(f"HTTP服务未就绪（连接失败: {str(e)[:50]}），重试 {i+1}/{max_retries}...")
if i < max_retries - 1:
time.sleep(retry_interval)
except requests.exceptions.RequestException as e:
logger.warning(f"HTTP请求异常: {type(e).__name__}: {str(e)[:100]}")
if i < max_retries - 1:
logger.info(f"重试 {i+1}/{max_retries}...")
time.sleep(retry_interval)
else:
logger.error("❌ HTTP服务超时！")
logger.error(f" 最后错误: {e}")
return False
logger.error("❌ HTTP服务超时！请检查agent_server.py是否在VM中运行")
logger.info(" 在VM中运行: python agent_server.py")
return False
def inject_file(self, host_path, guest_filename=None):
"""
将文件从主机注入到虚拟机桌面
Args:
host_path: 主机文件路径
guest_filename: 虚拟机中的文件名（默认使用原文件名）
"""
if not os.path.exists(host_path):
raise FileNotFoundError(f"源文件不存在: {host_path}")
if guest_filename is None:
guest_filename = os.path.basename(host_path)
guest_path = f"{self.guest_desktop}\\{guest_filename}"
# 获取文件大小
file_size = os.path.getsize(host_path)
file_size_kb = file_size / 1024
logger.info(f"注入文件: {os.path.basename(host_path)} ({file_size_kb:.1f}KB) → 虚拟机桌面")
logger.info(f" 源路径: {host_path}")
logger.info(f" 目标路径: {guest_path}")
try:
start_time = time.time()
# 使用vmrun传输（30秒超时）
self._run_vmrun(
"copyFileFromHostToGuest",
self.vmx_path,
host_path,
guest_path,
timeout=30
)
elapsed = time.time() - start_time
logger.info(f"✅ 文件注入成功: {guest_filename} (耗时 {elapsed:.1f}秒)")
except Exception as e:
logger.error(f"❌ 文件注入失败: {e}")
raise
def collect_file(self, guest_filename, host_path):
"""
从虚拟机桌面收集文件到主机
Args:
guest_filename: 虚拟机桌面上的文件名
host_path: 主机保存路径
"""
guest_path = f"{self.guest_desktop}\\{guest_filename}"
logger.info(f"收集文件: {guest_filename} → {os.path.basename(host_path)}")
try:
# 确保目标目录存在
os.makedirs(os.path.dirname(host_path), exist_ok=True)
# 方法1: 尝试使用vmrun
try:
self._run_vmrun(
"copyFileFromGuestToHost",
self.vmx_path,
guest_path,
host_path
)
logger.info(f"✅ 文件收集成功（vmrun）: {guest_filename}")
return
except RuntimeError as e:
logger.warning(f"vmrun收集失败，尝试HTTP方式: {e}")
# 方法2: 尝试通过HTTP下载（备用）
try:
response = requests.get(
f"{self.vm_url}/download/{guest_filename}",
timeout=10
)
if response.status_code == 200:
with open(host_path, 'wb') as f:
f.write(response.content)
logger.info(f"✅ 文件收集成功（HTTP）: {guest_filename}")
else:
raise RuntimeError(f"HTTP下载失败: {response.status_code}")
except Exception as http_error:
logger.error(f"❌ HTTP收集也失败: {http_error}")
raise RuntimeError(f"文件收集失败（两种方法都失败）")
except Exception as e:
logger.error(f"❌ 文件收集失败: {e}")
raise
def get_screenshot(self, retry_with_ip_detect=True):
"""
获取虚拟机截图
Args:
retry_with_ip_detect: 如果连接失败，是否尝试检测IP并重试
Returns:
PIL.Image对象
"""
try:
# 绕过代理
proxies = {'http': None, 'https': None}
response = requests.get(f"{self.vm_url}/screenshot", timeout=5, proxies=proxies)
if response.status_code == 200:
return Image.open(io.BytesIO(response.content))
else:
raise RuntimeError(f"截图失败: HTTP {response.status_code}")
except (requests.exceptions.ConnectionError, requests.exceptions.Timeout) as e:
if retry_with_ip_detect:
logger.warning(f"⚠️ 截图连接失败，尝试检测并更新IP...")
if self._detect_and_update_ip():
# IP已更新，重试一次
logger.info(f"🔄 使用新IP重试截图...")
return self.get_screenshot(retry_with_ip_detect=False)
logger.error(f"❌ 获取截图失败: {e}")
raise
except Exception as e:
logger.error(f"❌ 获取截图失败: {e}")
raise
def get_screen_info(self, retry_with_ip_detect=True):
"""
获取虚拟机屏幕信息（分辨率、DPI等）
Args:
retry_with_ip_detect: 如果连接失败，是否尝试检测IP并重试
Returns:
dict: 包含screen_width, screen_height, dpi_scale等信息
"""
try:
# 绕过代理
proxies = {'http': None, 'https': None}
response = requests.get(f"{self.vm_url}/screen_info", timeout=5, proxies=proxies)
if response.status_code == 200:
return response.json()
else:
raise RuntimeError(f"获取屏幕信息失败: HTTP {response.status_code}")
except (requests.exceptions.ConnectionError, requests.exceptions.Timeout) as e:
if retry_with_ip_detect:
logger.warning(f"⚠️ 屏幕信息连接失败，尝试检测并更新IP...")
if self._detect_and_update_ip():
# IP已更新，重试一次
logger.info(f"🔄 使用新IP重试获取屏幕信息...")
return self.get_screen_info(retry_with_ip_detect=False)
logger.error(f"❌ 获取屏幕信息失败: {e}")
raise
except Exception as e:
logger.error(f"❌ 获取屏幕信息失败: {e}")
raise
def list_desktop_files(self):
"""
列出虚拟机桌面文件（用于调试）
Returns:
list: 文件名列表
"""
try:
# 绕过代理
proxies = {'http': None, 'https': None}
response = requests.get(f"{self.vm_url}/list_desktop", timeout=5, proxies=proxies)
if response.status_code == 200:
return response.json().get('files', [])
else:
raise RuntimeError(f"列出文件失败: HTTP {response.status_code}")
except Exception as e:
logger.warning(f"⚠️ 列出桌面文件失败: {e}")
return []
def send_action(self, action_type, **params):
"""
发送动作到虚拟机（用于未来的Agent自动执行）
Args:
action_type: 动作类型 (click/type/hotkey)
**params: 动作参数
"""
try:
# 绕过代理
proxies = {'http': None, 'https': None}
payload = {"type": action_type, **params}
response = requests.post(
f"{self.vm_url}/action",
json=payload,
timeout=5,
proxies=proxies
)
if response.status_code == 200:
logger.debug(f"动作执行成功: {action_type}")
else:
raise RuntimeError(f"动作执行失败: HTTP {response.status_code}")
except Exception as e:
logger.error(f"❌ 发送动作失败: {e}")
raise
def get_mouse_pos(self):
"""
从虚拟机获取当前鼠标物理坐标
Returns:
tuple: (x, y) 物理坐标，失败返回 (None, None)
"""
try:
# 绕过代理
proxies = {'http': None, 'https': None}
response = requests.get(f"{self.vm_url}/mouse_pos", timeout=2, proxies=proxies)
if response.status_code == 200:
data = response.json()
return data['x'], data['y']
return None, None
except Exception as e:
logger.debug(f"获取VM鼠标位置失败: {e}")
return None, None
if __name__ == "__main__":
# 测试代码
print("JadeEnv 测试")
print("=" * 60)
# 配置（需要根据实际情况修改）
VMX_PATH = "/Volumes/Castor/虚拟机/Jade_Win_11.vmwarevm/Windows 11 64 位 ARM 2.vmx"
SNAPSHOT = "Jade_Ready"
VM_PASSWORD = "lizhanyuan"
try:
env = JadeEnv(
vmx_path=VMX_PATH,
snapshot_name=SNAPSHOT,
vm_password=VM_PASSWORD,
guest_username="lzy",
guest_password="LIZHANYUAN"
)
# 测试重置
print("\n测试1: 重置环境")
env.reset()
# 测试获取屏幕信息
print("\n测试2: 获取屏幕信息")
info = env.get_screen_info()
print(f" 分辨率: {info['screen_width']}x{info['screen_height']}")
print(f" DPI缩放: {info['dpi_scale']}")
# 测试列出桌面文件
print("\n测试3: 列出桌面文件")
files = env.list_desktop_files()
print(f" 桌面文件: {files[:5]}..." if len(files) > 5 else f" 桌面文件: {files}")
print("\n✅ 所有测试通过！")
except Exception as e:
print(f"\n❌ 测试失败: {e}")
import traceback
traceback.print_exc()

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"""
轨迹录制器
监听鼠标键盘事件，记录操作轨迹和截图
"""
import time
import json
import os
from datetime import datetime
from pynput import mouse, keyboard
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class Recorder:
"""轨迹录制器 - Host端事件驱动录制"""
def __init__(self, jade_env, task_id, output_dir):
"""
初始化录制器
Args:
jade_env: JadeEnv实例
task_id: 任务ID
output_dir: 输出目录（human_demo/）
"""
self.env = jade_env
self.task_id = task_id
self.output_dir = output_dir
# 创建输出目录
self.screens_dir = os.path.join(output_dir, "screens")
os.makedirs(self.screens_dir, exist_ok=True)
# 数据结构
self.actions = []
self.metadata = {}
self.start_time = None
self.screenshot_counter = 0
# 监听器
self.mouse_listener = None
self.keyboard_listener = None
# 状态
self.is_recording = False
logger.info(f"录制器初始化: 任务={task_id}")
def start(self):
"""开始录制"""
if self.is_recording:
logger.warning("录制已在进行中")
return
self.is_recording = True
self.start_time = time.time()
# 获取虚拟机屏幕信息
try:
screen_info = self.env.get_screen_info()
self.metadata = {
"task_id": self.task_id,
"vm_resolution": [screen_info['screen_width'], screen_info['screen_height']],
"vm_screenshot_resolution": [screen_info['screenshot_width'], screen_info['screenshot_height']],
"vm_dpi_scale": screen_info['dpi_scale'],
"recording_start": datetime.now().isoformat(),
"recording_end": None
}
logger.info(f"虚拟机分辨率: {screen_info['screen_width']}x{screen_info['screen_height']}")
logger.info(f"截图分辨率: {screen_info['screenshot_width']}x{screen_info['screenshot_height']}")
except Exception as e:
logger.warning(f"获取屏幕信息失败: {e}")
self.metadata = {
"task_id": self.task_id,
"recording_start": datetime.now().isoformat(),
"recording_end": None
}
# 记录初始截图
self._capture_screenshot("initial")
# 启动监听器
self.mouse_listener = mouse.Listener(
on_click=self._on_mouse_click,
on_scroll=self._on_mouse_scroll
)
self.keyboard_listener = keyboard.Listener(
on_press=self._on_key_press
)
self.mouse_listener.start()
self.keyboard_listener.start()
logger.info("✅ 录制已启动")
print("\n" + "=" * 60)
print("🎥 录制进行中...")
print("💡 提示：")
print(" - 请在VMware窗口中操作JADE")
print(" - 每次点击都会自动截图")
print(" - 按 Ctrl+C 停止录制")
print("=" * 60 + "\n")
def _on_mouse_click(self, x, y, button, pressed):
"""鼠标点击事件处理"""
if not self.is_recording or not pressed:
return
# 核心修改：立刻从虚拟机获取真实物理坐标
vm_x, vm_y = self.env.get_mouse_pos()
elapsed = time.time() - self.start_time
# 记录动作
action = {
"t": round(elapsed, 3),
"type": "click",
"button": str(button).replace("Button.", ""),
"pos_host": [x, y], # Mac 逻辑坐标（留作参考）
"pos_vm": [vm_x, vm_y] if vm_x is not None else None # 真实VM物理坐标
}
# 截图
screenshot_filename = self._capture_screenshot("click")
action["screenshot"] = screenshot_filename
self.actions.append(action)
if vm_x is not None:
logger.info(f"[{elapsed:.1f}s] 点击: VM({vm_x}, {vm_y}) [Host: {int(x)}, {int(y)}] {action['button']}")
else:
logger.info(f"[{elapsed:.1f}s] 点击: Host({int(x)}, {int(y)}) [VM获取失败] {action['button']}")
def _on_mouse_scroll(self, x, y, dx, dy):
"""鼠标滚轮事件处理"""
if not self.is_recording:
return
elapsed = time.time() - self.start_time
action = {
"t": round(elapsed, 3),
"type": "scroll",
"pos_host": [x, y],
"delta": [dx, dy],
"pos_vm": None
}
self.actions.append(action)
logger.debug(f"[{elapsed:.1f}s] 滚轮: ({x}, {y}) delta=({dx}, {dy})")
def _on_key_press(self, key):
"""键盘按键事件处理"""
if not self.is_recording:
return
elapsed = time.time() - self.start_time
# 转换按键名称
try:
if hasattr(key, 'char') and key.char:
key_name = key.char
else:
key_name = str(key).replace("Key.", "")
except:
key_name = str(key)
action = {
"t": round(elapsed, 3),
"type": "key",
"key": key_name
}
self.actions.append(action)
logger.debug(f"[{elapsed:.1f}s] 按键: {key_name}")
def _capture_screenshot(self, tag=""):
"""
捕获截图
Args:
tag: 标签（用于文件名）
Returns:
str: 截图相对路径
"""
try:
screenshot = self.env.get_screenshot()
# 生成文件名
self.screenshot_counter += 1
if tag:
filename = f"{self.screenshot_counter:04d}_{tag}.png"
else:
filename = f"{self.screenshot_counter:04d}.png"
filepath = os.path.join(self.screens_dir, filename)
screenshot.save(filepath)
logger.debug(f"截图保存: {filename}")
return f"screens/{filename}"
except Exception as e:
logger.error(f"截图失败: {e}")
return None
def stop(self):
"""停止录制"""
if not self.is_recording:
logger.warning("录制未在进行中")
return
self.is_recording = False
# 停止监听器
if self.mouse_listener:
self.mouse_listener.stop()
if self.keyboard_listener:
self.keyboard_listener.stop()
# 记录结束截图
self._capture_screenshot("final")
# 更新元数据
self.metadata["recording_end"] = datetime.now().isoformat()
self.metadata["total_duration"] = round(time.time() - self.start_time, 2)
self.metadata["total_actions"] = len(self.actions)
self.metadata["total_screenshots"] = self.screenshot_counter
logger.info("✅ 录制已停止")
def save(self):
"""保存轨迹数据"""
if self.is_recording:
logger.warning("录制仍在进行，先停止录制")
self.stop()
# 保存原始数据（未处理坐标）
output_data = {
"metadata": self.metadata,
"actions": self.actions
}
raw_path = os.path.join(self.output_dir, "actions_raw.json")
with open(raw_path, 'w', encoding='utf-8') as f:
json.dump(output_data, f, indent=2, ensure_ascii=False)
logger.info(f"✅ 轨迹数据已保存: {raw_path}")
logger.info(f" - 总动作数: {len(self.actions)}")
logger.info(f" - 截图数: {self.screenshot_counter}")
logger.info(f" - 总时长: {self.metadata.get('total_duration', 0):.1f}秒")
print("\n" + "=" * 60)
print("📊 录制统计:")
print(f" 动作数: {len(self.actions)}")
print(f" 截图数: {self.screenshot_counter}")
print(f" 时长: {self.metadata.get('total_duration', 0):.1f}秒")
print(f" 保存位置: {raw_path}")
print("=" * 60)
print("\n💡 下一步：运行坐标转换")
print(f" python scripts/tools/process_trajectory.py {self.task_id}")
print("=" * 60 + "\n")
def record_interactive(jade_env, task_id, output_dir):
"""
交互式录制（带Ctrl+C停止）
Args:
jade_env: JadeEnv实例
task_id: 任务ID
output_dir: 输出目录
"""
recorder = Recorder(jade_env, task_id, output_dir)
recorder.start()
try:
# 保持录制状态，直到Ctrl+C
while recorder.is_recording:
time.sleep(0.1)
except KeyboardInterrupt:
print("\n\n⏹ 收到停止信号...")
finally:
recorder.stop()
recorder.save()
return recorder
if __name__ == "__main__":
print("Recorder 独立测试模式")
print("提示: 通常应该通过 collect_task.py 调用")

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# 运行在 Windows 虚拟机内部
from flask import Flask, request, send_file
import pyautogui
import io
import os
import subprocess
import ctypes
import time
app = Flask(__name__)
# 获取Windows DPI缩放比例
def get_dpi_scale():
"""获取Windows的DPI缩放比例"""
try:
# 获取主显示器的DPI缩放比例
scale_factor = ctypes.windll.shcore.GetScaleFactorForDevice(0) / 100.0
return scale_factor
except:
# 如果获取失败，默认返回1.0（无缩放）
return 1.0
# 获取实际屏幕分辨率
def get_screen_size():
"""获取实际屏幕分辨率（物理像素）"""
try:
user32 = ctypes.windll.user32
width = user32.GetSystemMetrics(0) # SM_CXSCREEN
height = user32.GetSystemMetrics(1) # SM_CYSCREEN
return width, height
except:
# 如果获取失败，使用 pyautogui 的方法
return pyautogui.size()
DPI_SCALE = get_dpi_scale()
SCREEN_WIDTH, SCREEN_HEIGHT = get_screen_size()
print(f"检测到DPI缩放比例: {DPI_SCALE}")
print(f"实际屏幕分辨率: {SCREEN_WIDTH} x {SCREEN_HEIGHT}")
# 获取截图分辨率（用于坐标转换）
def get_screenshot_size():
"""获取截图的实际分辨率"""
img = pyautogui.screenshot()
return img.size[0], img.size[1]
SCREENSHOT_WIDTH, SCREENSHOT_HEIGHT = get_screenshot_size()
print(f"截图分辨率: {SCREENSHOT_WIDTH} x {SCREENSHOT_HEIGHT}")
# 1. 获取屏幕截图
@app.route('/screenshot', methods=['GET'])
def screenshot():
img = pyautogui.screenshot()
img_io = io.BytesIO()
img.save(img_io, 'PNG')
img_io.seek(0)
return send_file(img_io, mimetype='image/png')
# 获取分辨率信息（用于调试）
@app.route('/screen_info', methods=['GET'])
def screen_info():
"""返回屏幕和截图的分辨率信息，用于调试坐标转换"""
screenshot_w, screenshot_h = get_screenshot_size()
return {
"screen_width": SCREEN_WIDTH,
"screen_height": SCREEN_HEIGHT,
"screenshot_width": screenshot_w,
"screenshot_height": screenshot_h,
"dpi_scale": DPI_SCALE,
"scale_ratio_x": SCREEN_WIDTH / screenshot_w if screenshot_w > 0 else 1.0,
"scale_ratio_y": SCREEN_HEIGHT / screenshot_h if screenshot_h > 0 else 1.0
}
# 2. 执行动作
@app.route('/action', methods=['POST'])
def action():
data = request.json
try:
if data['type'] == 'click':
# 获取当前截图分辨率（可能每次不同）
screenshot_w, screenshot_h = get_screenshot_size()
# 从截图坐标转换为实际屏幕坐标
# 如果截图分辨率和屏幕分辨率不同，需要按比例缩放
x = data['x']
y = data['y']
# 计算缩放比例
scale_x = SCREEN_WIDTH / screenshot_w if screenshot_w > 0 else 1.0
scale_y = SCREEN_HEIGHT / screenshot_h if screenshot_h > 0 else 1.0
# 应用缩放
actual_x = int(x * scale_x)
actual_y = int(y * scale_y)
print(f"收到坐标: ({x}, {y}) -> 转换后: ({actual_x}, {actual_y}) [缩放比例: {scale_x:.2f}, {scale_y:.2f}]")
pyautogui.click(x=actual_x, y=actual_y)
elif data['type'] == 'type':
pyautogui.write(data['text'])
elif data['type'] == 'hotkey':
pyautogui.hotkey(*data['keys']) # 例如 ['ctrl', 's']
return {"status": "success"}
except Exception as e:
return {"status": "error", "msg": str(e)}
# 获取当前鼠标位置 (用于Host录制辅助)
@app.route('/mouse_pos', methods=['GET'])
def mouse_pos():
"""获取虚拟机当前鼠标位置"""
try:
x, y = pyautogui.position()
return {
"status": "success",
"x": int(x),
"y": int(y),
"timestamp": time.time()
}
except Exception as e:
return {"status": "error", "msg": str(e)}, 500
# 3. [关键！] 初始化环境
@app.route('/reset', methods=['POST'])
def reset():
# 这里可以写简单的逻辑：
# 1. 杀死 Jade 进程
os.system("taskkill /f /im jade.exe")
# 2. 这里的"重置"比快照弱，但对于 M1 调试更方便
# 如果必须用快照，需要在 Step 3 的 Mac 端调用 vmrun
return {"status": "reset_done"}
# 4. 列出桌面文件（用于调试）
@app.route('/list_desktop', methods=['GET'])
def list_desktop():
"""列出桌面上的文件"""
try:
desktop = os.path.expanduser(r"~\Desktop")
if os.path.exists(desktop):
files = os.listdir(desktop)
return {"status": "success", "files": files, "desktop_path": desktop}
else:
return {"status": "error", "msg": "Desktop path not found"}
except Exception as e:
return {"status": "error", "msg": str(e)}
# 5. 下载桌面文件（备用文件收集方式）
@app.route('/download/<filename>', methods=['GET'])
def download_file(filename):
"""
从桌面下载文件
用作vmrun文件传输的备用方案
"""
try:
desktop = os.path.expanduser(r"~\Desktop")
filepath = os.path.join(desktop, filename)
if not os.path.exists(filepath):
return {"status": "error", "msg": f"File not found: {filename}"}, 404
return send_file(filepath, as_attachment=True, download_name=filename)
except Exception as e:
return {"status": "error", "msg": str(e)}, 500
if __name__ == '__main__':
# 监听 0.0.0.0 允许外部访问
print("\n" + "=" * 60)
print("JADE Agent Server 启动")
print("=" * 60)
print(f"监听地址: 0.0.0.0:5000")
print(f"屏幕分辨率: {SCREEN_WIDTH}x{SCREEN_HEIGHT}")
print(f"截图分辨率: {SCREENSHOT_WIDTH}x{SCREENSHOT_HEIGHT}")
print(f"DPI缩放: {DPI_SCALE}")
print("=" * 60)
print("\n可用接口:")
print(" GET /screenshot - 获取屏幕截图")
print(" GET /screen_info - 获取屏幕信息")
print(" POST /action - 执行动作")
print(" POST /reset - 重置环境")
print(" GET /list_desktop - 列出桌面文件")
print(" GET /download/<file> - 下载桌面文件")
print("=" * 60 + "\n")
app.run(host='0.0.0.0', port=5000)

4
scripts/guest_scripts/start_agent.bat Normal file
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@echo off
cd C:\Users\lzy\workplace\OSWorld\desktop_env\server\
..venv\bin\activate
python agent_server.py

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
批量创建任务
从JSON定义文件批量创建任务
用法:
python scripts/tools/batch_create_tasks.py tasks/batch_definitions/basic_processing_tasks.json
"""
import json
import os
import sys
from pathlib import Path
# 添加项目根目录到路径
project_root = Path(__file__).parent.parent.parent
sys.path.insert(0, str(project_root))
from scripts.tools.init_task import init_task
def batch_create_tasks(definition_file, project_root=".", force=False, skip_existing=True):
"""
批量创建任务
Args:
definition_file: 任务定义JSON文件路径
project_root: 项目根目录
force: 是否强制覆盖已存在的任务
skip_existing: 是否跳过已存在的任务（与force互斥）
"""
# 读取任务定义
with open(definition_file, 'r', encoding='utf-8') as f:
data = json.load(f)
tasks = data.get("tasks", [])
category = data.get("category", "unknown")
tutorial_source = data.get("tutorial_source", "")
print("=" * 60)
print(f"📚 批量创建任务: {category}")
if tutorial_source:
print(f" 教程来源: {tutorial_source}")
print(f" 任务数量: {len(tasks)}")
print("=" * 60)
results = {
"success": [],
"skipped": [],
"failed": []
}
for i, task_def in enumerate(tasks, 1):
task_id = task_def["id"]
print(f"\n[{i}/{len(tasks)}] 处理任务: {task_id}")
print("-" * 60)
# 检查是否已存在
task_dir = os.path.join(project_root, "tasks", task_id)
if os.path.exists(task_dir) and not force:
if skip_existing:
print(f"⏭️ 跳过（已存在）: {task_id}")
results["skipped"].append(task_id)
continue
else:
print(f"⚠️ 任务已存在: {task_id}")
print(" 使用 --force 强制覆盖，或设置 skip_existing=True")
results["failed"].append((task_id, "已存在"))
continue
try:
# 调用 init_task 创建任务结构
success = init_task(
task_id=task_id,
project_root=project_root,
force=force,
category=task_def.get("category", category),
difficulty=task_def.get("difficulty", "easy"),
instruction=task_def.get("instruction", "")
)
if not success:
results["failed"].append((task_id, "初始化失败"))
continue
# 更新 task.json
task_json_path = os.path.join(task_dir, "task.json")
if os.path.exists(task_json_path):
with open(task_json_path, 'r', encoding='utf-8') as f:
task_config = json.load(f)
# 更新输入输出配置
source_file = task_def.get("source_file", "DEMO01.MDI")
if not os.path.isabs(source_file) and not source_file.startswith("../"):
source_file = f"../../data/source/{source_file}"
filename = os.path.basename(source_file)
inject_to = f"C:\\Users\\lzy\\Desktop\\{filename}"
output_filename = task_def.get("output_filename", "result.txt")
collect_from = f"C:\\Users\\lzy\\Desktop\\{output_filename}"
task_config["input"] = {
"source_file": source_file,
"inject_to": inject_to
}
task_config["output"] = {
"expected_file": output_filename,
"collect_from": collect_from
}
# 更新评测方法
eval_method = task_def.get("evaluation_method", "xrd_data_compare")
task_config["evaluation"]["method"] = eval_method
# 添加教程来源
if task_def.get("tutorial_source"):
task_config["tutorial_source"] = task_def["tutorial_source"]
# 添加备注
if task_def.get("notes"):
task_config["notes"] = task_def["notes"]
# 保存
with open(task_json_path, 'w', encoding='utf-8') as f:
json.dump(task_config, f, ensure_ascii=False, indent=2)
print(f"✅ 任务创建成功: {task_id}")
results["success"].append(task_id)
else:
print(f"❌ task.json 未创建: {task_json_path}")
results["failed"].append((task_id, "配置文件未创建"))
except Exception as e:
print(f"❌ 创建任务失败: {task_id}")
print(f" 错误: {e}")
import traceback
traceback.print_exc()
results["failed"].append((task_id, str(e)))
# 打印总结
print("\n" + "=" * 60)
print("📊 批量创建总结")
print("=" * 60)
print(f"✅ 成功: {len(results['success'])}")
if results["success"]:
for task_id in results["success"]:
print(f" - {task_id}")
if results["skipped"]:
print(f"\n⏭️ 跳过: {len(results['skipped'])}")
for task_id in results["skipped"]:
print(f" - {task_id}")
if results["failed"]:
print(f"\n❌ 失败: {len(results['failed'])}")
for task_id, reason in results["failed"]:
print(f" - {task_id}: {reason}")
print("=" * 60)
return results
def main():
import argparse
parser = argparse.ArgumentParser(
description="批量创建任务",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
使用示例:
# 批量创建任务（跳过已存在的）
python scripts/tools/batch_create_tasks.py tasks/batch_definitions/basic_processing_tasks.json
# 强制覆盖已存在的任务
python scripts/tools/batch_create_tasks.py tasks/batch_definitions/basic_processing_tasks.json --force
# 不跳过已存在的任务（遇到已存在就失败）
python scripts/tools/batch_create_tasks.py tasks/batch_definitions/basic_processing_tasks.json --no-skip-existing
"""
)
parser.add_argument("definition_file", help="任务定义JSON文件路径")
parser.add_argument("--project-root", default=".", help="项目根目录")
parser.add_argument("--force", action="store_true", help="强制覆盖已存在的任务")
parser.add_argument("--no-skip-existing", action="store_true", help="不跳过已存在的任务（遇到就失败）")
args = parser.parse_args()
if not os.path.exists(args.definition_file):
print(f"❌ 定义文件不存在: {args.definition_file}")
sys.exit(1)
skip_existing = not args.no_skip_existing
results = batch_create_tasks(
args.definition_file,
args.project_root,
force=args.force,
skip_existing=skip_existing
)
# 如果有失败的任务，返回非0退出码
if results["failed"]:
sys.exit(1)
else:
sys.exit(0)
if __name__ == "__main__":
main()

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"""
任务数据采集入口
整合环境控制、轨迹录制、文件收集的完整流程
"""
import os
import sys
import argparse
import json
import logging
# 添加父目录到路径
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from core.jade_env import JadeEnv
from core.recorder import record_interactive
from utils.config_loader import load_config, get_vm_config, get_network_config
logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
logger = logging.getLogger(__name__)
def load_default_config():
"""加载默认配置"""
try:
config = load_config()
vm_config = get_vm_config(config)
network_config = get_network_config(config)
return {
"vmx_path": vm_config.get('vmx_path'),
"snapshot_name": vm_config.get('snapshot_name', 'Jade_Ready'),
"vm_ip": network_config.get('vm_ip'),
"vm_password": vm_config.get('vm_password'),
"guest_username": vm_config.get('guest_username'),
"guest_password": vm_config.get('guest_password')
}
except Exception as e:
logger.warning(f"⚠️ 无法加载config.json: {e}")
logger.info(" 使用硬编码配置")
return {
"vmx_path": "/Volumes/Castor/虚拟机/Jade_Win_11.vmwarevm/Windows 11 64 位 ARM 2.vmx",
"snapshot_name": "Jade_Ready",
"vm_ip": "192.168.116.129",
"vm_password": "lizhanyuan",
"guest_username": "lzy",
"guest_password": "LIZHANYUAN"
}
def load_task_config(task_id, project_root="."):
"""加载任务配置文件"""
task_json_path = os.path.join(project_root, "tasks", task_id, "task.json")
if not os.path.exists(task_json_path):
logger.error(f"❌ 任务配置文件不存在: {task_json_path}")
logger.info(" 请先创建任务目录和task.json")
return None
with open(task_json_path, 'r', encoding='utf-8') as f:
return json.load(f)
def mode_reset(env, task_config, project_root="."):
"""
模式1: 重置环境并注入输入文件
"""
print("\n" + "=" * 60)
print("🔄 模式: 重置环境")
print("=" * 60)
# 1. 重置虚拟机
env.reset()
# 2. 注入输入文件
if 'input' in task_config:
input_config = task_config['input']
source_file = input_config.get('source_file')
if source_file:
# 处理相对路径（相对于任务目录）
if not os.path.isabs(source_file):
task_dir = os.path.join(project_root, "tasks", task_config['id'])
source_file = os.path.normpath(os.path.join(task_dir, source_file))
# 确保使用绝对路径
source_file = os.path.abspath(source_file)
if os.path.exists(source_file):
# 从Windows路径中提取文件名（处理反斜杠）
inject_to = input_config.get('inject_to', '')
if inject_to:
# 使用Windows路径分隔符分割
guest_filename = inject_to.split('\\')[-1]
else:
guest_filename = os.path.basename(source_file)
env.inject_file(source_file, guest_filename)
else:
logger.warning(f"⚠️ 输入文件不存在: {source_file}")
print("\n✅ 环境准备完成！")
print("=" * 60)
print("💡 下一步：开始录制操作")
print(f" python scripts/tools/collect_task.py {task_config['id']} --mode record")
print("=" * 60 + "\n")
def mode_record(env, task_config, project_root="."):
"""
模式2: 录制人类操作轨迹
"""
task_id = task_config['id']
output_dir = os.path.join(project_root, "tasks", task_id, "human_demo")
print("\n" + "=" * 60)
print("🎥 模式: 录制轨迹")
print("=" * 60)
print(f"任务: {task_config.get('instruction', 'N/A')}")
print("=" * 60)
# 创建录制器并开始录制
record_interactive(env, task_id, output_dir)
print("\n💡 下一步：处理坐标转换")
print(f" python scripts/tools/process_trajectory.py {task_id}")
print("=" * 60 + "\n")
def mode_collect(env, task_config, project_root="."):
"""
模式3: 收集输出文件到ground_truth
"""
print("\n" + "=" * 60)
print("📦 模式: 收集结果文件")
print("=" * 60)
task_id = task_config['id']
if 'output' in task_config:
output_config = task_config['output']
expected_file = output_config.get('expected_file')
if expected_file:
# 目标路径
gt_dir = os.path.join(project_root, "tasks", task_id, "ground_truth")
os.makedirs(gt_dir, exist_ok=True)
host_path = os.path.join(gt_dir, expected_file)
# 收集文件
env.collect_file(expected_file, host_path)
print(f"\n✅ 文件已保存到: {host_path}")
else:
logger.warning("⚠️ 任务配置中未指定expected_file")
else:
logger.warning("⚠️ 任务配置中未指定output")
print("\n" + "=" * 60)
print("💡 下一步：验证评测")
print(f" python scripts/tools/run_eval.py {task_id}")
print("=" * 60 + "\n")
def mode_full(env, task_config, project_root="."):
"""
模式4: 完整流程（reset + record + collect）
"""
print("\n" + "=" * 60)
print("🔄 模式: 完整采集流程")
print("=" * 60)
# Step 1: Reset
mode_reset(env, task_config, project_root)
# Step 2: Record
input("\n按Enter键开始录制...")
mode_record(env, task_config, project_root)
# Step 3: Collect
input("\n按Enter键收集结果...")
mode_collect(env, task_config, project_root)
print("\n✅ 完整采集流程完成！")
def main():
parser = argparse.ArgumentParser(
description="JADE Benchmark 任务数据采集工具",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
使用示例:
# 完整流程（推荐）
python scripts/collect_task.py smoothing_001 --mode full
# 分步执行
python scripts/collect_task.py smoothing_001 --mode reset # 1. 重置并注入文件
python scripts/collect_task.py smoothing_001 --mode record # 2. 录制操作
python scripts/collect_task.py smoothing_001 --mode collect # 3. 收集结果
"""
)
parser.add_argument("task_id", help="任务ID（对应tasks/目录下的子目录名）")
parser.add_argument(
"--mode",
choices=["reset", "record", "collect", "full"],
default="full",
help="采集模式（默认：full）"
)
parser.add_argument("--project-root", default=".", help="项目根目录")
parser.add_argument("--vmx", help="虚拟机.vmx文件路径（覆盖默认配置）")
parser.add_argument("--snapshot", help="快照名称（覆盖默认配置）")
parser.add_argument("--vm-ip", help="虚拟机IP地址（覆盖默认配置）")
args = parser.parse_args()
# 加载任务配置
task_config = load_task_config(args.task_id, args.project_root)
if not task_config:
sys.exit(1)
# 加载并合并配置
config = load_default_config()
if args.vmx:
config['vmx_path'] = args.vmx
if args.snapshot:
config['snapshot_name'] = args.snapshot
if args.vm_ip:
config['vm_ip'] = args.vm_ip
# 初始化环境
try:
logger.info("初始化JADE环境...")
env = JadeEnv(
vmx_path=config['vmx_path'],
snapshot_name=config['snapshot_name'],
vm_ip=config['vm_ip'],
vm_password=config.get('vm_password'),
guest_username=config.get('guest_username'),
guest_password=config.get('guest_password')
)
# 执行对应模式
if args.mode == "reset":
mode_reset(env, task_config, args.project_root)
elif args.mode == "record":
mode_record(env, task_config, args.project_root)
elif args.mode == "collect":
mode_collect(env, task_config, args.project_root)
elif args.mode == "full":
mode_full(env, task_config, args.project_root)
except KeyboardInterrupt:
print("\n\n⏹ 操作已取消")
sys.exit(1)
except Exception as e:
logger.error(f"❌ 错误: {e}")
import traceback
traceback.print_exc()
sys.exit(1)
if __name__ == "__main__":
main()

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
自动检测VM的IP地址
使用vmrun getGuestIPAddress命令获取VM的当前IP
"""
import subprocess
import sys
import json
import os
from pathlib import Path
try:
import requests
except ImportError:
requests = None
# 添加项目根目录到路径
project_root = Path(__file__).parent.parent.parent
sys.path.insert(0, str(project_root))
from scripts.utils.config_loader import load_config
def get_vm_ip(vmx_path, vm_password=None):
"""
使用vmrun获取VM的IP地址
Args:
vmx_path: 虚拟机.vmx文件路径
vm_password: 虚拟机文件加密密码（可选）
Returns:
str: VM的IP地址，如果失败返回None
"""
vmrun = "/Applications/VMware Fusion.app/Contents/Library/vmrun"
# 构建命令
cmd = [vmrun, "-T", "fusion"]
if vm_password:
cmd.extend(["-vp", vm_password])
cmd.extend(["getGuestIPAddress", vmx_path])
try:
result = subprocess.run(
cmd,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
text=True,
timeout=10
)
if result.returncode == 0:
ip = result.stdout.strip()
if ip and ip != "":
return ip
else:
print(f"⚠️ vmrun返回空IP地址")
return None
else:
error_msg = result.stderr or result.stdout
print(f"❌ 获取IP失败: {error_msg}")
return None
except subprocess.TimeoutExpired:
print(f"❌ 获取IP超时")
return None
except Exception as e:
print(f"❌ 获取IP异常: {e}")
return None
def update_config_ip(new_ip, config_path="config.json"):
"""
更新config.json中的IP地址
Args:
new_ip: 新的IP地址
config_path: 配置文件路径
"""
config_path = os.path.join(project_root, config_path)
if not os.path.exists(config_path):
print(f"❌ 配置文件不存在: {config_path}")
return False
try:
with open(config_path, 'r', encoding='utf-8') as f:
config = json.load(f)
old_ip = config.get("network", {}).get("vm_ip", "未知")
if old_ip == new_ip:
print(f"✅ IP地址未变化: {new_ip}")
return True
config["network"]["vm_ip"] = new_ip
with open(config_path, 'w', encoding='utf-8') as f:
json.dump(config, f, indent=2, ensure_ascii=False)
print(f"✅ 已更新IP地址: {old_ip} → {new_ip}")
return True
except Exception as e:
print(f"❌ 更新配置文件失败: {e}")
return False
def test_http_connection(ip, port=5000, timeout=5):
"""
测试HTTP连接是否可用
Args:
ip: VM的IP地址
port: 端口号
timeout: 超时时间（秒）
Returns:
bool: 连接是否成功
"""
try:
import requests
except ImportError:
print(f"⚠️ requests模块未安装，跳过HTTP连接测试")
return False
url = f"http://{ip}:{port}/screen_info"
proxies = {'http': None, 'https': None} # 绕过代理
try:
response = requests.get(url, timeout=timeout, proxies=proxies)
if response.status_code == 200:
print(f"✅ HTTP服务连接成功: {url}")
return True
else:
print(f"⚠️ HTTP服务响应异常: 状态码 {response.status_code}")
return False
except requests.exceptions.Timeout:
print(f"⚠️ HTTP服务连接超时: {url}")
return False
except requests.exceptions.ConnectionError as e:
print(f"⚠️ HTTP服务连接失败: {url}")
print(f" 错误: {str(e)[:100]}")
return False
except Exception as e:
print(f"⚠️ HTTP连接异常: {e}")
return False
def main():
"""主函数"""
print("=" * 60)
print("🔍 检测VM IP地址")
print("=" * 60)
# 加载配置
try:
config = load_config()
vmx_path = config["vmware"]["vmx_path"]
vm_password = config["vmware"].get("vm_password")
current_ip = config["network"].get("vm_ip", "未知")
except Exception as e:
print(f"❌ 加载配置失败: {e}")
sys.exit(1)
print(f"\n📋 当前配置:")
print(f" VM路径: {os.path.basename(vmx_path)}")
print(f" 当前IP: {current_ip}")
print(f" 端口: {config['network'].get('agent_server_port', 5000)}")
# 获取VM IP
print(f"\n🔍 正在获取VM IP地址...")
vm_ip = get_vm_ip(vmx_path, vm_password)
if not vm_ip:
print("\n❌ 无法获取VM IP地址")
print(" 可能原因:")
print(" 1. VM未运行")
print(" 2. VM网络未配置")
print(" 3. vmrun命令执行失败")
sys.exit(1)
print(f"✅ 检测到VM IP: {vm_ip}")
# 测试HTTP连接
port = config["network"].get("agent_server_port", 5000)
print(f"\n🔗 测试HTTP连接 (端口 {port})...")
http_ok = test_http_connection(vm_ip, port)
# 询问是否更新配置
if vm_ip != current_ip:
print(f"\n⚠️ IP地址已变化: {current_ip} → {vm_ip}")
if http_ok:
print(f"\n❓ 是否更新配置文件? (y/n): ", end="")
try:
choice = input().strip().lower()
if choice == 'y':
if update_config_ip(vm_ip):
print(f"\n✅ 配置已更新！")
else:
print(f"\n❌ 配置更新失败")
else:
print(f"\n⏭️ 跳过更新")
except KeyboardInterrupt:
print(f"\n\n⚠️ 用户取消")
else:
print(f"\n⚠️ HTTP服务不可用，请检查:")
print(f" 1. VM中是否运行了 agent_server.py?")
print(f" 2. 端口 {port} 是否被占用?")
print(f" 3. 防火墙是否阻止了连接?")
else:
print(f"\n✅ IP地址未变化")
if not http_ok:
print(f"\n⚠️ 但HTTP服务不可用，请检查VM中的agent_server.py")
print("=" * 60)
if __name__ == "__main__":
main()

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
从教程信息快速生成任务定义
用法:
python scripts/tools/extract_task_from_tutorial.py
"""
import json
import os
import sys
from pathlib import Path
# 添加项目根目录到路径
project_root = Path(__file__).parent.parent.parent
sys.path.insert(0, str(project_root))
from scripts.tools.init_task import init_task
# 任务类别和难度映射
CATEGORY_MAP = {
"1": "basic_processing",
"2": "peak_analysis",
"3": "phase_identification",
"4": "crystal_parameters",
"5": "calibration",
"6": "advanced_analysis",
}
DIFFICULTY_MAP = {
"1": "easy",
"2": "medium",
"3": "hard",
}
# 常见任务模板
TASK_TEMPLATES = {
"basic_processing": {
"open_file": "请打开桌面上的 {filename} 文件。",
"smooth": "请打开桌面上的 {filename} 文件，进行平滑处理 (Smoothing)，然后将处理后的曲线导出为 ASCII (.txt) 文件并命名为 {output}。",
"background": "请打开桌面上的 {filename} 文件，进行背景扣除 (Background Removal)，然后将处理后的曲线导出为 ASCII (.txt) 文件并命名为 {output}。",
"export": "请打开桌面上的 {filename} 文件，将当前曲线导出为 ASCII (.txt) 文件并命名为 {output}。",
},
"peak_analysis": {
"peak_search": "请打开桌面上的 {filename} 文件，进行寻峰操作 (Peak Search)，并导出寻峰结果文件 {output}。",
"peak_separation": "请打开桌面上的 {filename} 文件，进行多峰分离操作 (Peak Separation)，并导出结果文件 {output}。",
"peak_fitting": "请打开桌面上的 {filename} 文件，进行峰形拟合 (Peak Fitting)，并导出结果文件 {output}。",
},
"phase_identification": {
"phase_search": "请打开桌面上的 {filename} 文件，进行物相检索 (Phase Search)，并导出检索结果文件 {output}。",
"quantitative": "请打开桌面上的 {filename} 文件，进行物相定量分析 (Quantitative Analysis)，并导出结果文件 {output}。",
},
"crystal_parameters": {
"lattice_constant": "请打开桌面上的 {filename} 文件，精确测定晶格常数 (Lattice Constant)，并导出结果文件 {output}。",
"crystal_size": "请打开桌面上的 {filename} 文件，使用Scherrer公式计算晶粒大小 (Crystal Size)，并导出结果文件 {output}。",
"stress": "请打开桌面上的 {filename} 文件，进行残余应力分析 (Stress Analysis)，并导出结果文件 {output}。",
"crystallinity": "请打开桌面上的 {filename} 文件，计算结晶化度 (Crystallinity)，并导出结果文件 {output}。",
},
}
def print_category_menu():
"""打印类别菜单"""
print("\n📚 任务类别:")
print(" 1. basic_processing (基础处理)")
print(" 2. peak_analysis (峰分析)")
print(" 3. phase_identification (物相检索)")
print(" 4. crystal_parameters (晶体参数)")
print(" 5. calibration (校正)")
print(" 6. advanced_analysis (高级分析)")
def print_difficulty_menu():
"""打印难度菜单"""
print("\n📊 难度等级:")
print(" 1. easy (简单，3-5步操作)")
print(" 2. medium (中等，5-10步操作)")
print(" 3. hard (困难，10+步操作)")
def get_user_input():
"""交互式获取用户输入"""
print("=" * 60)
print("🎯 从教程提取任务 - 快速生成工具")
print("=" * 60)
# 任务ID
task_id = input("\n📝 任务ID (例如: peak_search_001): ").strip()
if not task_id:
print("❌ 任务ID不能为空")
return None
# 类别
print_category_menu()
category_choice = input("\n选择类别 (1-6): ").strip()
category = CATEGORY_MAP.get(category_choice)
if not category:
print("❌ 无效的类别选择")
return None
# 难度
print_difficulty_menu()
difficulty_choice = input("\n选择难度 (1-3): ").strip()
difficulty = DIFFICULTY_MAP.get(difficulty_choice)
if not difficulty:
print("❌ 无效的难度选择")
return None
# 输入文件
print("\n📁 输入文件配置:")
source_file = input(" 源文件路径 (相对于data/source/, 例如: DEMO01.MDI): ").strip()
if not source_file:
source_file = "DEMO01.MDI"
# 输出文件
print("\n📤 输出文件配置:")
output_filename = input(" 输出文件名 (例如: result.txt): ").strip()
if not output_filename:
output_filename = "result.txt"
# 任务类型（如果类别有模板）
task_type = None
if category in TASK_TEMPLATES:
templates = TASK_TEMPLATES[category]
print(f"\n📋 可用任务模板 ({category}):")
for i, (key, template) in enumerate(templates.items(), 1):
print(f" {i}. {key}")
use_template = input("\n使用模板? (y/n, 默认n): ").strip().lower()
if use_template == 'y':
template_choice = input(f"选择模板 (1-{len(templates)}): ").strip()
try:
template_key = list(templates.keys())[int(template_choice) - 1]
task_type = template_key
except (ValueError, IndexError):
print("⚠️ 无效的模板选择，将使用自定义指令")
# 指令
if task_type and category in TASK_TEMPLATES:
# 使用模板
template = TASK_TEMPLATES[category][task_type]
instruction = template.format(
filename=os.path.basename(source_file),
output=output_filename
)
print(f"\n✅ 生成的指令 (模板): {instruction}")
confirm = input("使用此指令? (y/n, 默认y): ").strip().lower()
if confirm == 'n':
instruction = input("\n📝 自定义指令: ").strip()
else:
# 自定义指令
instruction = input("\n📝 任务指令 (中文描述): ").strip()
if not instruction:
print("❌ 指令不能为空")
return None
# 教程来源（可选）
tutorial_source = input("\n📚 教程来源 (可选，例如: 教程(1)): ").strip()
return {
"task_id": task_id,
"category": category,
"difficulty": difficulty,
"instruction": instruction,
"source_file": source_file,
"output_filename": output_filename,
"tutorial_source": tutorial_source,
}
def create_task_from_info(info):
"""根据信息创建任务"""
task_id = info["task_id"]
category = info["category"]
difficulty = info["difficulty"]
instruction = info["instruction"]
# 构建源文件路径
source_file = info["source_file"]
if not os.path.isabs(source_file):
# 相对路径，假设在 data/source/ 下
source_file = f"../../data/source/{source_file}"
# 构建VM路径
filename = os.path.basename(source_file)
inject_to = f"C:\\Users\\lzy\\Desktop\\{filename}"
# 输出文件路径
output_filename = info["output_filename"]
collect_from = f"C:\\Users\\lzy\\Desktop\\{output_filename}"
print(f"\n🚀 正在创建任务: {task_id}")
print(f" 类别: {category}")
print(f" 难度: {difficulty}")
print(f" 源文件: {source_file}")
print(f" 输出文件: {output_filename}")
# 调用 init_task
try:
init_task(
task_id=task_id,
category=category,
difficulty=difficulty,
instruction=instruction,
project_root=str(project_root)
)
# 更新 task.json
task_json_path = project_root / "tasks" / task_id / "task.json"
if task_json_path.exists():
with open(task_json_path, 'r', encoding='utf-8') as f:
task_config = json.load(f)
# 更新输入输出配置
task_config["input"] = {
"source_file": source_file,
"inject_to": inject_to
}
task_config["output"] = {
"expected_file": output_filename,
"collect_from": collect_from
}
# 添加教程来源（如果有）
if info.get("tutorial_source"):
task_config["tutorial_source"] = info["tutorial_source"]
# 保存
with open(task_json_path, 'w', encoding='utf-8') as f:
json.dump(task_config, f, ensure_ascii=False, indent=2)
print(f"\n✅ 任务创建成功!")
print(f" 任务目录: tasks/{task_id}/")
print(f" 配置文件: tasks/{task_id}/task.json")
print(f"\n📝 下一步:")
print(f" 1. 检查并完善 task.json")
print(f" 2. 运行: python scripts/tools/collect_task.py {task_id} --mode full")
return True
else:
print(f"❌ 任务目录创建失败: {task_json_path}")
return False
except Exception as e:
print(f"❌ 创建任务时出错: {e}")
import traceback
traceback.print_exc()
return False
def main():
"""主函数"""
try:
info = get_user_input()
if info:
create_task_from_info(info)
else:
print("\n❌ 任务创建取消")
except KeyboardInterrupt:
print("\n\n⚠️ 用户取消操作")
except Exception as e:
print(f"\n❌ 发生错误: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
main()

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"""
任务初始化工具
快速创建新任务的目录结构和配置文件模板
"""
import os
import json
import sys
import argparse
TASK_JSON_TEMPLATE = {
"id": "",
"category": "basic_processing", # basic_processing, peak_analysis, phase_identification, compound_tasks
"difficulty": "easy", # easy, medium, hard
"instruction": "请填写任务指令",
"input": {
"source_file": "../../data/source/DEMO01.MDI",
"inject_to": "C:\\Users\\lzy\\Desktop\\DEMO01.MDI"
},
"output": {
"expected_file": "result.txt",
"collect_from": "C:\\Users\\lzy\\Desktop\\result.txt"
},
"evaluation": {
"method": "xrd_data_compare",
"ground_truth": "ground_truth/result.txt",
"target_output": "agent_output/result.txt",
"tolerance": 1e-4
}
}
def init_task(task_id, project_root=".", force=False, category=None, difficulty=None, instruction=None):
"""
初始化新任务
Args:
task_id: 任务ID
project_root: 项目根目录
force: 是否覆盖已存在的任务
category: 任务类别（可选）
difficulty: 任务难度（可选）
instruction: 任务指令（可选）
"""
task_dir = os.path.join(project_root, "tasks", task_id)
# 检查是否已存在
if os.path.exists(task_dir) and not force:
print(f"❌ 任务目录已存在: {task_dir}")
print(" 使用 --force 参数强制覆盖")
return False
print(f"创建任务: {task_id}")
print("=" * 60)
# 创建目录结构
directories = [
task_dir,
os.path.join(task_dir, "ground_truth"),
os.path.join(task_dir, "human_demo"),
os.path.join(task_dir, "human_demo", "screens"),
os.path.join(task_dir, "agent_output")
]
for directory in directories:
os.makedirs(directory, exist_ok=True)
print(f"✅ 创建目录: {os.path.relpath(directory, project_root)}")
# 创建task.json
task_config = TASK_JSON_TEMPLATE.copy()
task_config["id"] = task_id
# 更新可选参数
if category:
task_config["category"] = category
if difficulty:
task_config["difficulty"] = difficulty
if instruction:
task_config["instruction"] = instruction
task_json_path = os.path.join(task_dir, "task.json")
with open(task_json_path, 'w', encoding='utf-8') as f:
json.dump(task_config, f, indent=2, ensure_ascii=False)
print(f"✅ 创建配置: {os.path.relpath(task_json_path, project_root)}")
# 创建README
readme_content = f"""# 任务: {task_id}
## 任务信息
- **ID**: {task_id}
- **类别**: {task_config['category']}
- **难度**: {task_config['difficulty']}
## 指令
{task_config['instruction']}
## 数据采集状态
- [ ] 环境重置与文件注入
- [ ] 操作轨迹录制
- [ ] 结果文件收集
- [ ] 坐标转换处理
- [ ] 评测验证
## 采集命令
```bash
# 完整流程
python scripts/collect_task.py {task_id} --mode full
# 分步执行
python scripts/collect_task.py {task_id} --mode reset
python scripts/collect_task.py {task_id} --mode record
python scripts/collect_task.py {task_id} --mode collect
python scripts/process_trajectory.py {task_id}
python scripts/run_eval.py {task_id}
```
## 文件结构
```
{task_id}/
├── task.json # 任务配置
├── ground_truth/ # 标准答案输出
├── human_demo/ # 人类操作轨迹
│ ├── actions_raw.json # 原始轨迹（未转换坐标）
│ ├── actions.json # 处理后轨迹（已转换坐标）
│ └── screens/ # 截图序列
└── agent_output/ # Agent输出（评测时使用）
```
"""
readme_path = os.path.join(task_dir, "README.md")
with open(readme_path, 'w', encoding='utf-8') as f:
f.write(readme_content)
print(f"✅ 创建说明: {os.path.relpath(readme_path, project_root)}")
print("=" * 60)
print("✅ 任务初始化完成！")
print("\n📝 下一步:")
print(f" 1. 编辑任务配置: {task_json_path}")
print(f" 2. 确保输入文件存在:例如 {task_config['input']['source_file']}")
print(f" 3. 开始数据采集: python scripts/tools/collect_task.py {task_id}")
print("=" * 60 + "\n")
return True
def main():
parser = argparse.ArgumentParser(
description="初始化新任务",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
使用示例:
# 创建新任务
python scripts/init_task.py smoothing_001
# 强制覆盖已存在的任务
python scripts/init_task.py smoothing_001 --force
"""
)
parser.add_argument("task_id", help="任务ID（建议格式: category_序号）")
parser.add_argument("--project-root", default=".", help="项目根目录")
parser.add_argument("--force", action="store_true", help="强制覆盖已存在的任务")
args = parser.parse_args()
success = init_task(args.task_id, args.project_root, args.force)
sys.exit(0 if success else 1)
if __name__ == "__main__":
main()

165
scripts/tools/process_trajectory.py Normal file
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"""
轨迹数据后处理
将录制的原始Host坐标转换为VM内坐标
"""
import json
import os
import sys
import argparse
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def process_trajectory(task_id, project_root=".", force=False,
scale_x_adjust=1.0, scale_y_adjust=1.0,
offset_x=0, offset_y=0):
"""
处理轨迹数据：将Host坐标转换为VM坐标
Args:
task_id: 任务ID
project_root: 项目根目录
force: 是否强制覆盖已有的处理结果
scale_x_adjust: X轴缩放调整系数
scale_y_adjust: Y轴缩放调整系数
offset_x: X轴偏移调整
offset_y: Y轴偏移调整
"""
# 路径
task_dir = os.path.join(project_root, "tasks", task_id)
human_demo_dir = os.path.join(task_dir, "human_demo")
raw_path = os.path.join(human_demo_dir, "actions_raw.json")
processed_path = os.path.join(human_demo_dir, "actions.json")
# 检查文件
if not os.path.exists(raw_path):
logger.error(f"❌ 原始轨迹文件不存在: {raw_path}")
logger.info(" 请先运行: python scripts/collect_task.py <task_id> --mode record")
return False
if os.path.exists(processed_path) and not force:
logger.warning(f"⚠️ 处理后的文件已存在: {processed_path}")
logger.info(" 使用 --force 参数强制覆盖")
return False
# 读取原始数据
logger.info(f"读取原始轨迹: {raw_path}")
with open(raw_path, 'r', encoding='utf-8') as f:
data = json.load(f)
metadata = data['metadata']
actions = data['actions']
logger.info(f"任务ID: {metadata['task_id']}")
logger.info(f"动作数: {len(actions)}")
# 获取分辨率信息
if 'vm_resolution' in metadata and 'vm_screenshot_resolution' in metadata:
vm_w, vm_h = metadata['vm_resolution']
screenshot_w, screenshot_h = metadata['vm_screenshot_resolution']
# 计算缩放比例
# 注意：Host端的点击坐标对应截图坐标，需要转换为VM内实际坐标
scale_x = (vm_w / screenshot_w) * scale_x_adjust
scale_y = (vm_h / screenshot_h) * scale_y_adjust
logger.info(f"VM分辨率: {vm_w}x{vm_h}")
logger.info(f"截图分辨率: {screenshot_w}x{screenshot_h}")
logger.info(f"转换比例: X={scale_x:.3f}, Y={scale_y:.3f}")
if scale_x_adjust != 1.0 or scale_y_adjust != 1.0:
logger.info(f"应用调整系数: X={scale_x_adjust}, Y={scale_y_adjust}")
if offset_x != 0 or offset_y != 0:
logger.info(f"应用偏移调整: X={offset_x}, Y={offset_y}")
else:
logger.warning("⚠️ 元数据缺少分辨率信息，使用默认比例1.0")
scale_x = 1.0 * scale_x_adjust
scale_y = 1.0 * scale_y_adjust
# 转换坐标
converted_count = 0
for action in actions:
if 'pos_host' in action and action['pos_host']:
host_x, host_y = action['pos_host']
# 应用转换
vm_x = int(host_x * scale_x + offset_x)
vm_y = int(host_y * scale_y + offset_y)
action['pos_vm'] = [vm_x, vm_y]
converted_count += 1
logger.info(f"✅ 坐标转换完成: {converted_count}/{len(actions)} 个动作")
# 添加处理信息到元数据
metadata['processed'] = {
"processed_at": __import__('datetime').datetime.now().isoformat(),
"scale_x": scale_x,
"scale_y": scale_y,
"offset_x": offset_x,
"offset_y": offset_y,
"converted_actions": converted_count
}
# 保存处理后的数据
logger.info(f"保存处理后的轨迹: {processed_path}")
with open(processed_path, 'w', encoding='utf-8') as f:
json.dump(data, f, indent=2, ensure_ascii=False)
logger.info("✅ 处理完成！")
# 输出示例
print("\n" + "=" * 60)
print("📊 坐标转换示例（前5个点击）:")
print("-" * 60)
click_count = 0
for action in actions:
if action['type'] == 'click' and 'pos_host' in action:
host_x, host_y = action['pos_host']
vm_x, vm_y = action['pos_vm'] if action['pos_vm'] else (0, 0)
# 转换为整数显示
print(f" Host({int(host_x):4d}, {int(host_y):4d}) → VM({int(vm_x):4d}, {int(vm_y):4d})")
click_count += 1
if click_count >= 5:
break
print("=" * 60)
print("\n💡 下一步：可视化验证（可选）")
print(f" python scripts/visualize_trajectory.py {task_id}")
print("=" * 60 + "\n")
return True
def main():
parser = argparse.ArgumentParser(description="处理轨迹数据，转换坐标")
parser.add_argument("task_id", help="任务ID")
parser.add_argument("--project-root", default=".", help="项目根目录")
parser.add_argument("--force", action="store_true", help="强制覆盖已有文件")
parser.add_argument("--scale-x", type=float, default=1.0, help="X轴缩放调整系数")
parser.add_argument("--scale-y", type=float, default=1.0, help="Y轴缩放调整系数")
parser.add_argument("--offset-x", type=int, default=0, help="X轴偏移调整")
parser.add_argument("--offset-y", type=int, default=0, help="Y轴偏移调整")
args = parser.parse_args()
success = process_trajectory(
task_id=args.task_id,
project_root=args.project_root,
force=args.force,
scale_x_adjust=args.scale_x,
scale_y_adjust=args.scale_y,
offset_x=args.offset_x,
offset_y=args.offset_y
)
sys.exit(0 if success else 1)
if __name__ == "__main__":
main()

261
scripts/tools/run_eval.py Normal file
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"""
评测入口脚本
支持单任务或批量评测
"""
import os
import sys
import json
import argparse
import logging
from datetime import datetime
# 添加父目录到路径
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from core.evaluator import evaluate
logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
logger = logging.getLogger(__name__)
def evaluate_task(task_id, project_root=".", verbose=True):
"""
评测单个任务
Args:
task_id: 任务ID
project_root: 项目根目录
verbose: 是否详细输出
Returns:
tuple: (score, message, details)
"""
task_dir = os.path.join(project_root, "tasks", task_id)
task_json_path = os.path.join(task_dir, "task.json")
# 检查任务配置
if not os.path.exists(task_json_path):
logger.error(f"❌ 任务配置不存在: {task_json_path}")
return 0, "任务配置不存在", {}
# 加载任务配置
with open(task_json_path, 'r', encoding='utf-8') as f:
task_config = json.load(f)
if verbose:
print("\n" + "=" * 60)
print(f"📝 评测任务: {task_id}")
print("=" * 60)
print(f"类别: {task_config.get('category', 'N/A')}")
print(f"难度: {task_config.get('difficulty', 'N/A')}")
print(f"指令: {task_config.get('instruction', 'N/A')}")
print("=" * 60)
# 获取评测配置
eval_config = task_config.get('evaluation', {})
method = eval_config.get('method', 'xrd_data_compare')
# 构建文件路径
gt_path = os.path.join(task_dir, eval_config.get('ground_truth', ''))
agent_path = os.path.join(task_dir, eval_config.get('target_output', ''))
tolerance = eval_config.get('tolerance', 1e-4)
# 检查文件
if not os.path.exists(gt_path):
logger.error(f"❌ Ground truth文件不存在: {gt_path}")
return 0, "Ground truth文件不存在", {}
if not os.path.exists(agent_path):
logger.error(f"❌ Agent输出文件不存在: {agent_path}")
return 0, "Agent输出文件不存在", {}
# 执行评测
try:
if method == 'xrd_data_compare':
score, message = evaluate(gt_path, agent_path, tolerance, mode="xrd_data")
elif method == 'peak_report_compare':
score, message = evaluate(gt_path, agent_path, tolerance, mode="peak_report")
else:
logger.warning(f"⚠️ 未知的评测方法: {method}")
score, message = 0, f"未知的评测方法: {method}"
details = {
"task_id": task_id,
"method": method,
"ground_truth": gt_path,
"agent_output": agent_path,
"tolerance": tolerance,
"timestamp": datetime.now().isoformat()
}
if verbose:
print(f"\n📊 评测结果:")
print(f" Score: {score}")
print(f" {message}")
print("=" * 60 + "\n")
return score, message, details
except Exception as e:
logger.error(f"❌ 评测失败: {e}")
import traceback
traceback.print_exc()
return 0, f"评测失败: {str(e)}", {}
def evaluate_batch(task_ids, project_root=".", output_file=None):
"""
批量评测多个任务
Args:
task_ids: 任务ID列表
project_root: 项目根目录
output_file: 结果输出文件（JSON格式）
"""
print("\n" + "=" * 60)
print("📊 批量评测")
print("=" * 60)
print(f"任务数: {len(task_ids)}")
print("=" * 60 + "\n")
results = []
total_score = 0
for i, task_id in enumerate(task_ids, 1):
print(f"\n[{i}/{len(task_ids)}] 评测: {task_id}")
score, message, details = evaluate_task(task_id, project_root, verbose=False)
result = {
"task_id": task_id,
"score": score,
"message": message,
**details
}
results.append(result)
total_score += score
status = "✅ 通过" if score == 1 else "❌ 失败"
print(f" {status}: {message}")
# 统计
pass_count = sum(1 for r in results if r['score'] == 1)
pass_rate = pass_count / len(task_ids) * 100 if task_ids else 0
print("\n" + "=" * 60)
print("📈 评测统计")
print("=" * 60)
print(f"总任务数: {len(task_ids)}")
print(f"通过数: {pass_count}")
print(f"失败数: {len(task_ids) - pass_count}")
print(f"通过率: {pass_rate:.1f}%")
print(f"平均分: {total_score / len(task_ids):.2f}")
print("=" * 60 + "\n")
# 保存结果
if output_file:
output_data = {
"timestamp": datetime.now().isoformat(),
"total_tasks": len(task_ids),
"pass_count": pass_count,
"pass_rate": pass_rate,
"results": results
}
with open(output_file, 'w', encoding='utf-8') as f:
json.dump(output_data, f, indent=2, ensure_ascii=False)
print(f"📄 详细结果已保存到: {output_file}\n")
return results
def discover_tasks(project_root="."):
"""
自动发现所有任务
Returns:
list: 任务ID列表
"""
tasks_dir = os.path.join(project_root, "tasks")
if not os.path.exists(tasks_dir):
return []
task_ids = []
for item in os.listdir(tasks_dir):
task_dir = os.path.join(tasks_dir, item)
task_json = os.path.join(task_dir, "task.json")
if os.path.isdir(task_dir) and os.path.exists(task_json):
task_ids.append(item)
return sorted(task_ids)
def main():
parser = argparse.ArgumentParser(
description="JADE Benchmark 评测工具",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
使用示例:
# 评测单个任务
python scripts/run_eval.py smoothing_001
# 评测多个任务
python scripts/run_eval.py smoothing_001 peak_search_001
# 评测所有任务
python scripts/run_eval.py --all
# 保存结果到文件
python scripts/run_eval.py --all --output results.json
"""
)
parser.add_argument("task_ids", nargs="*", help="任务ID列表")
parser.add_argument("--all", action="store_true", help="评测所有任务")
parser.add_argument("--project-root", default=".", help="项目根目录")
parser.add_argument("--output", help="结果输出文件（JSON格式）")
args = parser.parse_args()
# 确定要评测的任务
if args.all:
task_ids = discover_tasks(args.project_root)
if not task_ids:
logger.error("❌ 未找到任何任务")
sys.exit(1)
logger.info(f"发现 {len(task_ids)} 个任务")
elif args.task_ids:
task_ids = args.task_ids
else:
parser.print_help()
sys.exit(1)
# 执行评测
try:
if len(task_ids) == 1:
# 单任务评测
score, message, _ = evaluate_task(task_ids[0], args.project_root)
sys.exit(0 if score == 1 else 1)
else:
# 批量评测
results = evaluate_batch(task_ids, args.project_root, args.output)
# 返回码：全部通过返回0，否则返回1
all_pass = all(r['score'] == 1 for r in results)
sys.exit(0 if all_pass else 1)
except KeyboardInterrupt:
print("\n\n⏹ 评测已取消")
sys.exit(1)
except Exception as e:
logger.error(f"❌ 错误: {e}")
import traceback
traceback.print_exc()
sys.exit(1)
if __name__ == "__main__":
main()

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scripts/utils/__init__.py Normal file
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"""
JADE Benchmark 辅助工具
配置加载、文件处理等辅助功能
"""
from .config_loader import load_config, get_vm_config, get_network_config
__all__ = [
'load_config',
'get_vm_config',
'get_network_config'
]

51
scripts/utils/config_loader.py Normal file
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"""
配置文件加载器
"""
import json
import os
def load_config(config_path=None):
"""
加载配置文件
Args:
config_path: 配置文件路径（默认在项目根目录的config.json）
Returns:
dict: 配置字典
"""
if config_path is None:
# 查找项目根目录的config.json
current_dir = os.path.dirname(os.path.abspath(__file__))
scripts_dir = os.path.dirname(current_dir) # scripts/
project_root = os.path.dirname(scripts_dir) # Jade-BenchMark-MVP/
config_path = os.path.join(project_root, "config.json")
if not os.path.exists(config_path):
raise FileNotFoundError(f"配置文件不存在: {config_path}")
with open(config_path, 'r', encoding='utf-8') as f:
return json.load(f)
def get_vm_config(config=None):
"""获取VM配置"""
if config is None:
config = load_config()
return config.get('vmware', {})
def get_network_config(config=None):
"""获取网络配置"""
if config is None:
config = load_config()
return config.get('network', {})
if __name__ == "__main__":
# 测试配置加载
config = load_config()
print("配置加载成功:")
print(json.dumps(config, indent=2, ensure_ascii=False))

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295
tasks/background_001/human_demo/actions_raw.json Normal file
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{
"metadata": {
"task_id": "background_001",
"vm_resolution": [
3840,
2160
],
"vm_screenshot_resolution": [
3840,
2160
],
"vm_dpi_scale": 2.0,
"recording_start": "2026-01-09T19:01:55.177741",
"recording_end": "2026-01-09T19:03:23.015038",
"total_duration": 88.34,
"total_actions": 35,
"total_screenshots": 13
},
"actions": [
{
"t": 5.872,
"type": "key",
"key": "right"
},
{
"t": 7.817,
"type": "click",
"button": "left",
"pos_host": [
40.93359375,
333.15234375
],
"pos_vm": [
80,
666
],
"screenshot": "screens/0002_click.png"
},
{
"t": 33.337,
"type": "click",
"button": "right",
"pos_host": [
554.56640625,
219.70703125
],
"pos_vm": [
1108,
438
],
"screenshot": "screens/0003_click.png"
},
{
"t": 37.47,
"type": "click",
"button": "left",
"pos_host": [
624.0234375,
485.74609375
],
"pos_vm": [
1248,
970
],
"screenshot": "screens/0004_click.png"
},
{
"t": 40.943,
"type": "click",
"button": "left",
"pos_host": [
666.17578125,
484.203125
],
"pos_vm": [
1332,
968
],
"screenshot": "screens/0005_click.png"
},
{
"t": 46.95,
"type": "click",
"button": "left",
"pos_host": [
550.890625,
222.9921875
],
"pos_vm": [
1100,
444
],
"screenshot": "screens/0006_click.png"
},
{
"t": 50.701,
"type": "click",
"button": "left",
"pos_host": [
245.34375,
196.83203125
],
"pos_vm": [
490,
392
],
"screenshot": "screens/0007_click.png"
},
{
"t": 57.946,
"type": "click",
"button": "left",
"pos_host": [
672.25390625,
506.546875
],
"pos_vm": [
1344,
1012
],
"screenshot": "screens/0008_click.png"
},
{
"t": 61.764,
"type": "click",
"button": "left",
"pos_host": [
237.87890625,
196.30859375
],
"pos_vm": [
474,
392
],
"screenshot": "screens/0009_click.png"
},
{
"t": 66.595,
"type": "click",
"button": "left",
"pos_host": [
675.453125,
508.65625
],
"pos_vm": [
1350,
1016
],
"screenshot": "screens/0010_click.png"
},
{
"t": 71.596,
"type": "key",
"key": "b"
},
{
"t": 71.69,
"type": "key",
"key": "a"
},
{
"t": 72.934,
"type": "key",
"key": "shift"
},
{
"t": 73.417,
"type": "key",
"key": "c"
},
{
"t": 73.93,
"type": "key",
"key": "k"
},
{
"t": 74.232,
"type": "key",
"key": "g"
},
{
"t": 74.596,
"type": "key",
"key": "r"
},
{
"t": 74.957,
"type": "key",
"key": "o"
},
{
"t": 75.051,
"type": "key",
"key": "u"
},
{
"t": 75.259,
"type": "key",
"key": "n"
},
{
"t": 75.393,
"type": "key",
"key": "d"
},
{
"t": 75.618,
"type": "key",
"key": "shift"
},
{
"t": 75.768,
"type": "key",
"key": "_"
},
{
"t": 76.924,
"type": "key",
"key": "r"
},
{
"t": 77.011,
"type": "key",
"key": "e"
},
{
"t": 77.257,
"type": "key",
"key": "s"
},
{
"t": 77.358,
"type": "key",
"key": "u"
},
{
"t": 77.611,
"type": "key",
"key": "l"
},
{
"t": 77.71,
"type": "key",
"key": "t"
},
{
"t": 81.94,
"type": "key",
"key": "ctrl"
},
{
"t": 79.723,
"type": "click",
"button": "left",
"pos_host": [
1214.86328125,
751.3515625
],
"pos_vm": [
2428,
1502
],
"screenshot": "screens/0011_click.png"
},
{
"t": 82.972,
"type": "key",
"key": "left"
},
{
"t": 84.977,
"type": "key",
"key": "ctrl"
},
{
"t": 86.418,
"type": "key",
"key": "c"
},
{
"t": 84.163,
"type": "click",
"button": "left",
"pos_host": [
1093.87890625,
904.1015625
],
"pos_vm": [
2428,
1502
],
"screenshot": "screens/0012_click.png"
}
]
}

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{
"id": "background_001",
"category": "basic_processing",
"difficulty": "easy",
"instruction": "请打开桌面上的 DEMO01.MDI 文件，进行背景扣除 (Background Removal)，使用自动背景扣除方法，然后将处理后的曲线导出为 ASCII (.txt) 文件并命名为 background_result.txt。",
"input": {
"source_file": "../../data/source/DEMO01.MDI",
"inject_to": "C:\\Users\\lzy\\Desktop\\DEMO01.MDI"
},
"output": {
"expected_file": "background_result.txt",
"collect_from": "C:\\Users\\lzy\\Desktop\\background_result.txt"
},
"evaluation": {
"method": "xrd_data_compare",
"ground_truth": "ground_truth/background_result.txt",
"target_output": "agent_output/background_result.txt",
"tolerance": 0.0001
},
"tutorial_source": "教程(1) + 基础处理知识",
"notes": "使用自动背景扣除方法（固定参数）"
}

70
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{
"description": "基础处理类别任务定义 - 根据教程(1)提取，使用固定参数",
"category": "basic_processing",
"tutorial_source": "Jade使用教程（1）打开文件 寻峰.pdf",
"parameter_strategy": "fixed_parameters",
"tasks": [
{
"id": "open_file_001",
"category": "basic_processing",
"difficulty": "easy",
"instruction": "请打开桌面上的 DEMO01.MDI 文件。",
"source_file": "DEMO01.MDI",
"output_filename": "open_file_result.txt",
"tutorial_source": "教程(1) - 打开文件部分",
"notes": "打开文件后导出为ASCII文件用于评测",
"evaluation_method": "xrd_data_compare",
"skip_if_exists": false,
"parameters": {
"note": "无参数，仅打开文件并导出"
}
},
{
"id": "smooth_001",
"category": "basic_processing",
"difficulty": "easy",
"instruction": "请打开桌面上的 DEMO01.MDI 文件，进行平滑处理 (Smoothing)，平滑点数设置为 5，然后将处理后的曲线导出为 ASCII (.txt) 文件并命名为 result.txt。",
"source_file": "DEMO01.MDI",
"output_filename": "result.txt",
"tutorial_source": "教程(1) + 基础处理",
"notes": "重新采集，固定平滑点数=5",
"evaluation_method": "xrd_data_compare",
"skip_if_exists": false,
"parameters": {
"smooth_points": 5,
"note": "固定参数：平滑点数=5"
}
},
{
"id": "background_001",
"category": "basic_processing",
"difficulty": "easy",
"instruction": "请打开桌面上的 DEMO01.MDI 文件，进行背景扣除 (Background Removal)，使用自动背景扣除方法，然后将处理后的曲线导出为 ASCII (.txt) 文件并命名为 background_result.txt。",
"source_file": "DEMO01.MDI",
"output_filename": "background_result.txt",
"tutorial_source": "教程(1) + 基础处理知识",
"notes": "使用自动背景扣除方法（固定参数）",
"evaluation_method": "xrd_data_compare",
"skip_if_exists": false,
"parameters": {
"background_method": "auto",
"note": "固定参数：自动背景扣除"
}
},
{
"id": "export_001",
"category": "basic_processing",
"difficulty": "easy",
"instruction": "请打开桌面上的 DEMO01.MDI 文件，将当前曲线导出为 ASCII (.txt) 文件并命名为 export_result.txt。",
"source_file": "DEMO01.MDI",
"output_filename": "export_result.txt",
"tutorial_source": "教程(1) - 导出数据部分",
"notes": "基础导出操作，不进行任何处理",
"evaluation_method": "xrd_data_compare",
"skip_if_exists": false,
"parameters": {
"note": "无参数，仅导出原始数据"
}
}
]
}

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{
"metadata": {
"task_id": "open_file_001",
"vm_resolution": [
3840,
2160
],
"vm_screenshot_resolution": [
3840,
2160
],
"vm_dpi_scale": 2.0,
"recording_start": "2026-01-09T18:51:58.410186",
"recording_end": "2026-01-09T18:52:57.169082",
"total_duration": 59.33,
"total_actions": 31,
"total_screenshots": 9
},
"actions": [
{
"t": 4.219,
"type": "key",
"key": "right"
},
{
"t": 7.338,
"type": "click",
"button": "left",
"pos_host": [
136.80078125,
282.97265625
],
"pos_vm": [
1068,
0
],
"screenshot": "screens/0002_click.png"
},
{
"t": 17.248,
"type": "click",
"button": "left",
"pos_host": [
238.72265625,
191.63671875
],
"pos_vm": [
476,
382
],
"screenshot": "screens/0003_click.png"
},
{
"t": 21.72,
"type": "click",
"button": "left",
"pos_host": [
631.64453125,
511.1171875
],
"pos_vm": [
1262,
1022
],
"screenshot": "screens/0004_click.png"
},
{
"t": 24.441,
"type": "click",
"button": "left",
"pos_host": [
240.703125,
191.7890625
],
"pos_vm": [
480,
382
],
"screenshot": "screens/0005_click.png"
},
{
"t": 27.768,
"type": "click",
"button": "left",
"pos_host": [
628.42578125,
512.640625
],
"pos_vm": [
1256,
1024
],
"screenshot": "screens/0006_click.png"
},
{
"t": 34.342,
"type": "key",
"key": "o"
},
{
"t": 34.398,
"type": "key",
"key": "p"
},
{
"t": 34.624,
"type": "key",
"key": "e"
},
{
"t": 34.78,
"type": "key",
"key": "n"
},
{
"t": 35.097,
"type": "key",
"key": "shift"
},
{
"t": 37.072,
"type": "key",
"key": "shift"
},
{
"t": 38.647,
"type": "key",
"key": "_"
},
{
"t": 39.276,
"type": "key",
"key": "f"
},
{
"t": 39.382,
"type": "key",
"key": "i"
},
{
"t": 39.666,
"type": "key",
"key": "l"
},
{
"t": 41.112,
"type": "key",
"key": "e"
},
{
"t": 41.554,
"type": "key",
"key": "shift"
},
{
"t": 41.689,
"type": "key",
"key": "_"
},
{
"t": 42.03,
"type": "key",
"key": "r"
},
{
"t": 42.103,
"type": "key",
"key": "e"
},
{
"t": 42.374,
"type": "key",
"key": "s"
},
{
"t": 42.476,
"type": "key",
"key": "u"
},
{
"t": 42.757,
"type": "key",
"key": "l"
},
{
"t": 42.899,
"type": "key",
"key": "t"
},
{
"t": 45.435,
"type": "click",
"button": "left",
"pos_host": [
1223.6953125,
752.48828125
],
"pos_vm": [
2446,
1504
],
"screenshot": "screens/0007_click.png"
},
{
"t": 49.703,
"type": "key",
"key": "ctrl"
},
{
"t": 51.684,
"type": "key",
"key": "left"
},
{
"t": 57.073,
"type": "key",
"key": "ctrl"
},
{
"t": 57.344,
"type": "key",
"key": "c"
},
{
"t": 56.866,
"type": "click",
"button": "left",
"pos_host": [
1037.97265625,
990.03515625
],
"pos_vm": [
2446,
1504
],
"screenshot": "screens/0008_click.png"
}
]
}

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{
"id": "open_file_001",
"category": "basic_processing",
"difficulty": "easy",
"instruction": "请打开桌面上的 DEMO01.MDI 文件，然后将当前曲线导出为 ASCII (.txt) 文件并命名为 open_file_result.txt。",
"input": {
"source_file": "../../data/source/DEMO01.MDI",
"inject_to": "C:\\Users\\lzy\\Desktop\\DEMO01.MDI"
},
"output": {
"expected_file": "open_file_result.txt",
"collect_from": "C:\\Users\\lzy\\Desktop\\open_file_result.txt"
},
"evaluation": {
"method": "xrd_data_compare",
"ground_truth": "ground_truth/open_file_result.txt",
"target_output": "agent_output/open_file_result.txt",
"tolerance": 0.0001
},
"tutorial_source": "教程(1) - 打开文件部分",
"notes": "打开文件后导出为ASCII文件用于评测"
}

81
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USER: lzy
JADE: Peak Search Report (72 Peaks, Max P/N = 19.3)
DATE: Friday, Jan 09, 2026 06:13p
FILE: [DEMO01.MDI] Demo01: 37-1497 4-733 9-169 18-303 9-77
SCAN: 5.0/70.0/0.02/1(sec), Cu, I(max)=1542, 01/06/26 11:55
PEAK: 19-pts/Parabolic Filter, Threshold=3.0, Cutoff=0.1%, BG=3/1.0, Peak-Top=Summit
NOTE: Intensity = Counts, 2T(0)=0.0(deg), Wavelength to Compute d-Spacing = 1.54056? (Cu/K-alpha1)
2-Theta d(?) BG Height I% Area I% FWHM
10.818 8.1711 34 261 17.2 2666 12.8 0.174
11.680 7.5700 35 365 24.1 3860 18.5 0.180
13.658 6.4779 30 147 9.7 1450 7.0 0.168
14.225 6.2212 30 62 4.1 515 2.5 0.141
16.981 5.2170 23 176 11.6 3106 14.9 0.300
18.082 4.9017 21 864 57.0 9350 44.9 0.184
20.202 4.3919 22 76 5.0 563 2.7 0.126
20.941 4.2386 34 349 23.0 3214 15.4 0.157
21.783 4.0766 18 208 13.7 3141 15.1 0.257
22.980 3.8670 17 92 6.1 1066 5.1 0.197
25.881 3.4397 14 954 62.9 11045 53.0 0.197
26.502 3.3605 11 87 5.7 1571 7.5 0.307
27.442 3.2475 11 88 5.8 1094 5.3 0.211
27.781 3.2087 11 485 32.0 5123 24.6 0.180
28.239 3.1576 16 161 10.6 1759 8.4 0.186
28.646 3.1137 46 232 15.3 2572 12.3 0.188
28.945 3.0822 13 187 12.3 3765 18.1 0.342
29.279 3.0478 13 300 19.8 3643 17.5 0.206
29.680 3.0075 13 148 9.8 1245 6.0 0.143
30.503 2.9282 17 197 13.0 1945 9.3 0.168
31.058 2.8771 21 807 53.2 8420 40.4 0.177
31.959 2.7981 25 1517 100.0 16539 79.4 0.185
32.443 2.7574 26 160 10.5 2768 13.3 0.294
32.903 2.7199 27 532 35.1 5489 26.4 0.175
33.517 2.6714 42 58 3.8 326 1.6 0.096
34.119 2.6257 22 1353 89.2 20828 100.0 0.262
34.382 2.6062 21 642 42.3 13051 62.7 0.346
35.139 2.5518 16 100 6.6 1206 5.8 0.205
35.638 2.5172 13 233 15.4 2864 13.8 0.209
35.920 2.4980 7 62 4.1 860 4.1 0.236
37.360 2.4050 8 454 29.9 6894 33.1 0.258
37.897 2.3722 6 52 3.4 587 2.8 0.192
39.160 2.2985 11 86 5.7 668 3.2 0.132
39.805 2.2627 9 106 7.0 2291 11.0 0.367
40.044 2.2498 11 251 16.5 3307 15.9 0.224
41.101 2.1943 9 122 8.0 971 4.7 0.135
41.778 2.1603 10 217 14.3 3548 17.0 0.278
42.059 2.1465 10 71 4.7 1054 5.1 0.252
43.018 2.1009 14 48 3.2 324 1.6 0.115
43.939 2.0589 13 184 12.1 2794 13.4 0.258
44.596 2.0301 14 69 4.5 911 4.4 0.224
44.817 2.0207 11 65 4.3 1531 7.4 0.400
45.323 1.9993 12 101 6.7 1770 8.5 0.298
45.561 1.9894 14 156 10.3 2329 11.2 0.254
46.818 1.9388 8 666 43.9 10609 50.9 0.271
47.140 1.9263 7 525 34.6 9530 45.8 0.309
48.120 1.8894 7 187 12.3 3398 16.3 0.309
48.419 1.8784 7 145 9.6 1891 9.1 0.222
49.034 1.8563 14 21 1.4 135 0.6 0.109
49.520 1.8392 8 472 31.1 5989 28.8 0.216
50.379 1.8098 37 183 12.1 2056 9.9 0.191
50.819 1.7952 27 433 28.5 4381 21.0 0.172
51.283 1.7800 14 228 15.0 2666 12.8 0.199
51.903 1.7602 10 174 11.5 1818 8.7 0.178
52.999 1.7263 10 176 11.6 3315 15.9 0.320
53.204 1.7202 10 271 17.9 5366 25.8 0.337
53.862 1.7007 6 180 11.9 1897 9.1 0.179
54.379 1.6858 6 265 17.5 2776 13.3 0.178
55.104 1.6653 5 31 2.0 286 1.4 0.157
56.060 1.6391 5 183 12.1 2265 10.9 0.210
56.603 1.6247 6 53 3.5 400 1.9 0.128
57.198 1.6092 5 168 11.1 2365 11.4 0.239
57.541 1.6004 5 60 4.0 807 3.9 0.229
59.537 1.5514 6 96 6.3 1211 5.8 0.214
60.039 1.5397 6 121 8.0 1692 8.1 0.238
61.342 1.5100 6 147 9.7 1653 7.9 0.191
62.543 1.4839 5 138 9.1 1284 6.2 0.158
63.184 1.4704 4 128 8.4 1902 9.1 0.253
64.200 1.4495 6 417 27.5 4693 22.5 0.191
65.199 1.4297 8 171 11.3 1658 8.0 0.165
66.317 1.4083 3 51 3.4 753 3.6 0.251
67.481 1.3868 3 73 4.8 738 3.5 0.172

81
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USER: lzy
JADE: Peak Search Report (72 Peaks, Max P/N = 19.3)
DATE: Friday, Jan 09, 2026 06:13p
FILE: [DEMO01.MDI] Demo01: 37-1497 4-733 9-169 18-303 9-77
SCAN: 5.0/70.0/0.02/1(sec), Cu, I(max)=1542, 01/06/26 11:55
PEAK: 19-pts/Parabolic Filter, Threshold=3.0, Cutoff=0.1%, BG=3/1.0, Peak-Top=Summit
NOTE: Intensity = Counts, 2T(0)=0.0(deg), Wavelength to Compute d-Spacing = 1.54056? (Cu/K-alpha1)
2-Theta d(?) BG Height I% Area I% FWHM
10.818 8.1711 34 261 17.2 2666 12.8 0.174
11.680 7.5700 35 365 24.1 3860 18.5 0.180
13.658 6.4779 30 147 9.7 1450 7.0 0.168
14.225 6.2212 30 62 4.1 515 2.5 0.141
16.981 5.2170 23 176 11.6 3106 14.9 0.300
18.082 4.9017 21 864 57.0 9350 44.9 0.184
20.202 4.3919 22 76 5.0 563 2.7 0.126
20.941 4.2386 34 349 23.0 3214 15.4 0.157
21.783 4.0766 18 208 13.7 3141 15.1 0.257
22.980 3.8670 17 92 6.1 1066 5.1 0.197
25.881 3.4397 14 954 62.9 11045 53.0 0.197
26.502 3.3605 11 87 5.7 1571 7.5 0.307
27.442 3.2475 11 88 5.8 1094 5.3 0.211
27.781 3.2087 11 485 32.0 5123 24.6 0.180
28.239 3.1576 16 161 10.6 1759 8.4 0.186
28.646 3.1137 46 232 15.3 2572 12.3 0.188
28.945 3.0822 13 187 12.3 3765 18.1 0.342
29.279 3.0478 13 300 19.8 3643 17.5 0.206
29.680 3.0075 13 148 9.8 1245 6.0 0.143
30.503 2.9282 17 197 13.0 1945 9.3 0.168
31.058 2.8771 21 807 53.2 8420 40.4 0.177
31.959 2.7981 25 1517 100.0 16539 79.4 0.185
32.443 2.7574 26 160 10.5 2768 13.3 0.294
32.903 2.7199 27 532 35.1 5489 26.4 0.175
33.517 2.6714 42 58 3.8 326 1.6 0.096
34.119 2.6257 22 1353 89.2 20828 100.0 0.262
34.382 2.6062 21 642 42.3 13051 62.7 0.346
35.139 2.5518 16 100 6.6 1206 5.8 0.205
35.638 2.5172 13 233 15.4 2864 13.8 0.209
35.920 2.4980 7 62 4.1 860 4.1 0.236
37.360 2.4050 8 454 29.9 6894 33.1 0.258
37.897 2.3722 6 52 3.4 587 2.8 0.192
39.160 2.2985 11 86 5.7 668 3.2 0.132
39.805 2.2627 9 106 7.0 2291 11.0 0.367
40.044 2.2498 11 251 16.5 3307 15.9 0.224
41.101 2.1943 9 122 8.0 971 4.7 0.135
41.778 2.1603 10 217 14.3 3548 17.0 0.278
42.059 2.1465 10 71 4.7 1054 5.1 0.252
43.018 2.1009 14 48 3.2 324 1.6 0.115
43.939 2.0589 13 184 12.1 2794 13.4 0.258
44.596 2.0301 14 69 4.5 911 4.4 0.224
44.817 2.0207 11 65 4.3 1531 7.4 0.400
45.323 1.9993 12 101 6.7 1770 8.5 0.298
45.561 1.9894 14 156 10.3 2329 11.2 0.254
46.818 1.9388 8 666 43.9 10609 50.9 0.271
47.140 1.9263 7 525 34.6 9530 45.8 0.309
48.120 1.8894 7 187 12.3 3398 16.3 0.309
48.419 1.8784 7 145 9.6 1891 9.1 0.222
49.034 1.8563 14 21 1.4 135 0.6 0.109
49.520 1.8392 8 472 31.1 5989 28.8 0.216
50.379 1.8098 37 183 12.1 2056 9.9 0.191
50.819 1.7952 27 433 28.5 4381 21.0 0.172
51.283 1.7800 14 228 15.0 2666 12.8 0.199
51.903 1.7602 10 174 11.5 1818 8.7 0.178
52.999 1.7263 10 176 11.6 3315 15.9 0.320
53.204 1.7202 10 271 17.9 5366 25.8 0.337
53.862 1.7007 6 180 11.9 1897 9.1 0.179
54.379 1.6858 6 265 17.5 2776 13.3 0.178
55.104 1.6653 5 31 2.0 286 1.4 0.157
56.060 1.6391 5 183 12.1 2265 10.9 0.210
56.603 1.6247 6 53 3.5 400 1.9 0.128
57.198 1.6092 5 168 11.1 2365 11.4 0.239
57.541 1.6004 5 60 4.0 807 3.9 0.229
59.537 1.5514 6 96 6.3 1211 5.8 0.214
60.039 1.5397 6 121 8.0 1692 8.1 0.238
61.342 1.5100 6 147 9.7 1653 7.9 0.191
62.543 1.4839 5 138 9.1 1284 6.2 0.158
63.184 1.4704 4 128 8.4 1902 9.1 0.253
64.200 1.4495 6 417 27.5 4693 22.5 0.191
65.199 1.4297 8 171 11.3 1658 8.0 0.165
66.317 1.4083 3 51 3.4 753 3.6 0.251
67.481 1.3868 3 73 4.8 738 3.5 0.172

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{
"id": "peak_search_001",
"category": "basic_processing",
"difficulty": "medium",
"instruction": "请打开桌面上的 DEMO01.MDI 文件，进行自动寻峰操作 (Peak Search)，然后,导出峰报告文件并命名为 peak_result.pid。",
"input": {
"source_file": "../../data/source/DEMO01.MDI",
"inject_to": "C:\\Users\\lzy\\Desktop\\DEMO01.MDI"
},
"output": {
"expected_file": "peak_result.pid",
"collect_from": "C:\\Users\\lzy\\Desktop\\peak_result.pid"
},
"evaluation": {
"method": "peak_report_compare",
"ground_truth": "ground_truth/peak_result.pid",
"target_output": "agent_output/peak_result.pid",
"tolerance": 0.01
},
"tutorial_source": "教程(1) - 寻峰部分",
"notes": "自动寻峰并导出峰报告，使用默认寻峰参数"
}

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{
"id": "smooth_001",
"category": "basic_processing",
"difficulty": "easy",
"instruction": "请打开桌面上的 DEMO01.MDI 文件，进行平滑处理 (Smoothing)，平滑点数设置为 5，然后将处理后的曲线导出为 ASCII (.txt) 文件并命名为 smooth_result.txt。",
"input": {
"source_file": "../../data/source/DEMO01.MDI",
"inject_to": "C:\\Users\\lzy\\Desktop\\DEMO01.MDI"
},
"output": {
"expected_file": "smooth_result.txt",
"collect_from": "C:\\Users\\lzy\\Desktop\\smooth_result.txt"
},
"evaluation": {
"method": "xrd_data_compare",
"ground_truth": "ground_truth/smooth_result.txt",
"target_output": "agent_output/smooth_result.txt",
"tolerance": 0.0001
},
"tutorial_source": "教程(1) + 基础处理",
"notes": "重新采集，固定平滑点数=5"
}

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