终验收数据预处理模块第一次提交

.vscode/launch.json

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README.md

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+# 数据预处理系统 (Data Preprocessing System)
+
+## 1. 项目概述
+本项目是一个用于处理车辆数据的预处理系统，目前主要包括：
+- 路测内置数据处理 (merge_data_process_LST.py)
+- 仿真内置数据处理 (后续扩展)
+- 插件式自定义数据处理
+
+## 功能特点
+
+- 支持多种数据源处理: CAN、GNSS、雷达等
+- 插件系统支持扩展自定义数据处理
+- 并行处理优化
+- 灵活的配置管理
+- 完整的错误处理机制
+
+## 安装要求
+
+- Python 3.6+
+- 依赖包:
+  - pandas
+  - numpy
+  - cantools (CAN数据处理)
+  - bagpy (ROS包处理)
+  - pyproj (坐标投影)
+
+## 安装步骤
+
+1. 克隆或下载代码库
+2. 安装依赖:
+```bash
+pip install -r requirements.txt
+```
+
+## 使用方法
+
+### 基本用法
+```bash
+python run.py --zip-path <数据ZIP文件路径> --output-dir <输出目录>
+```
+
+### 完整参数说明
+```bash
+python run.py \
+  --zip-path <数据ZIP文件路径> \
+  --trafficlight-json <交通灯JSON文件路径> \
+  --output-dir <输出目录> \
+  --utm-zone <UTM区域> \
+  --x-offset <X偏移量> \
+  --y-offset <Y偏移量> \
+  --plugins-dir <插件目录> \
+  --resources-dir <资源目录> \
+  --config <配置文件路径> \
+  --use-parallel \
+  --batch-size 10000
+```
+
+### 打包发布
+使用 PyInstaller 打包:
+```bash
+pyinstaller --onedir --name merge_data_process_LST --clean \
+  --add-data="$(python -c 'import bagpy; from pathlib import Path; print(f"{Path(bagpy.__file__).parent}:bagpy")')" \
+  --add-binary="/path/to/engine:." \
+  --add-data="./data_map:data_map" \
+  --add-data="VBox.dbc:." \
+  merge_data_process_LST.py
+```
+
+## 文件结构分析
+必要的核心文件:
+```
+/core/ - 核心模块目录
+  __init__.py
+  plugin_interface.py - 插件接口
+  plugin_manager.py - 插件管理器
+  resource_manager.py - 资源管理器
+  error_handler.py - 错误处理
+  config_manager.py - 配置管理
+/plugins/ - 插件目录
+  __init__.py
+  radar_data_processor.py - 雷达数据处理插件
+主要文件:
+  merge_data_process_LST.py - 主要数据处理逻辑
+  run.py - 命令行入口程序
+  config.json - 配置文件
+  README.md - 项目文档
+建议移除的文件:
+  merge_data_process.py - 功能已被整合到 merge_data_process_LST.py
+  data_pipeline.py - 未被实际使用
+```
+
+## 项目结构
+```
+dataPreProcess_LST_new_win/
+├── core/ # 核心模块
+│   ├── init.py
+│   ├── plugin_interface.py # 插件接口定义
+│   ├── plugin_manager.py # 插件管理器
+│   ├── resource_manager.py # 资源管理器
+│   ├── error_handler.py # 错误处理
+│   └── config_manager.py # 配置管理
+├── plugins/ # 插件目录
+│   ├── init.py
+│   └── radar_data_processor.py # 雷达数据处理插件示例
+├── resources/ # 资源文件目录
+│   ├── radar_config.xml # 雷达插件配置
+├── merge_data_process_LST.py # 主要数据处理逻辑
+├── run.py # 命令行入口程序
+├── config.json # 系统配置文件
+└── README.md
+```
+
+## 数据处理流程
+
+### 数据加载和预处理
+- ZIP文件解压
+- 配置加载
+- 插件发现和初始化
+
+### 主要处理阶段
+- CSV数据处理
+- ROS包数据提取
+- C++引擎处理
+- 数据质量检查
+- 数据合并和后处理
+
+### 插件数据处理
+- 数据合并
+- 结果验证和保存
+
+## 插件开发
+查看 plugins/radar_data_processor.py 作为示例插件。新插件需要:
+- 继承 CustomDataProcessorPlugin
+- 实现必要方法:
+  - can_handle()
+  - get_required_columns()
+  - _process_extracted_files()
+
+## 配置说明
+系统配置文件 (config.json) 包含:
+- 处理参数 (并行、批处理等)
+- 数据库配置
+- 插件设置
+- 日志配置
+- 坐标系参数
+- 文件路径配置
+
+## 系统架构
+
+1.编译代码
+```bash
+pyinstaller --onedir --name merge_data_process_LST --clean --add-data="$(python -c 'import bagpy; from pathlib import Path; print(f"{Path(bagpy.__file__).parent}:bagpy")')" --add-binary="/home/MapToCsv/build/engine:." --add-data="./data_map:data_map" --add-data="VBox.dbc:." --add-binary="/usr/lib/libstdc++.so.6:." --add-binary="/usr/lib/libgcc_s.so.1:." --add-binary="/lib/ld-musl-x86_64.so.1:." merge_data_process_LST.py
+```
+
+2.运行代码
+    打开python文件，修改路径
+    ```python
+    input_zip = '/home/cicv/zhaoyuan/data/dataprocess_python/LeftTurnAssit.zip'
+    output_dir = '/home/cicv/zhaoyuan/data/dataprocess_python/output'
+    shpdata_path = "./modules/map/data"
+    engine_dir = "./engine"
+    ```
+    请根据用户本地存放位置进行修改
+    
+```bash
+./your_packed_app --zip_path V2I_CSAE53-2020_LeftTurnAssist_PGVIL_01_VR.zip --output_path ./output
+```

README_new.md

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+# 数据预处理系统（Data Preprocessing System）
+
+## 项目概述
+
+本项目是一个用于处理车辆数据的预处理系统，支持多种数据源和格式，包括CAN数据、GNSS数据、雷达数据等。系统采用插件架构，允许用户扩展自定义数据处理功能。
+
+## 系统架构
+
+### 核心组件
+
+```
++------------------+      +------------------+      +------------------+
+|                  |      |                  |      |                  |
+|  数据源（ZIP）    +----->+  数据处理管道     +----->+  输出（CSV）     |
+|                  |      |                  |      |                  |
++------------------+      +------------------+      +------------------+
+                                   |
+                                   v
+                          +------------------+
+                          |                  |
+                          |   插件系统       |
+                          |                  |
+                          +------------------+
+```
+
+系统由以下主要组件构成：
+
+1. **数据处理器（DataProcessor）**：负责协调整个处理流程，包括ZIP文件解析、数据提取、格式转换等。
+
+2. **插件系统**：
+   - **插件接口（PluginInterface）**：定义插件必须实现的方法。
+   - **插件管理器（PluginManager）**：负责发现、加载和管理插件。
+   - **资源管理器（ResourceManager）**：管理插件资源和验证数据格式。
+
+3. **数据处理管道**：
+   - **ZIP/CSV处理器**：处理ZIP中的数据库文件并生成CSV。
+   - **Rosbag处理器**：处理ROS包数据。
+   - **C++引擎**：执行高性能数据处理任务。
+   - **最终数据处理器**：合并、清洗和验证数据。
+
+## 插件系统
+
+### 插件接口
+
+所有插件必须实现以下接口：
+
+```python
+def can_handle(self, zip_path: Path, folder_name: str) -> bool:
+    """判断插件是否可以处理指定文件夹中的数据"""
+    pass
+
+def process_data(self, zip_path: Path, folder_name: str, output_dir: Path) -> Optional[Path]:
+    """处理数据并返回处理后的CSV文件路径"""
+    pass
+
+def get_required_columns(self) -> Dict[str, Any]:
+    """返回插件将提供的列及其数据类型"""
+    pass
+```
+
+### 现有插件
+
+系统已包含以下插件：
+
+1. **雷达数据处理器（RadarDataProcessor）**：处理雷达数据，使用XML配置文件定义数据格式和处理参数。
+2. **文本数据处理器（TextDataProcessor）**：处理简单文本格式的传感器数据。
+
+## 数据处理流程
+
+1. **初始化配置**：创建配置对象，设置输入/输出路径、UTM区域等参数。
+2. **处理ZIP中的数据库文件**：提取并处理数据库文件，生成初始CSV文件。
+3. **处理Rosbag文件**：提取并处理ROS包数据。
+4. **运行C++预处理引擎**：执行高性能数据处理任务。
+5. **数据质量预检**：验证数据质量，检查帧丢失等问题。
+6. **最终数据处理**：合并数据、计算曲率、处理交通灯信息等。
+7. **插件数据处理**：使用插件处理自定义数据文件夹，并将结果合并到主数据文件中。
+8. **扩展后处理**：应用额外的处理/验证步骤。
+
+## 使用方法
+
+### 基本用法
+
+```bash
+python run.py --zip-path <数据ZIP文件路径> --output-dir <输出目录>
+```
+
+### 完整参数
+
+```bash
+python run.py \
+  --zip-path <数据ZIP文件路径> \
+  --trafficlight-json <交通灯JSON文件路径> \
+  --output-dir <输出目录> \
+  --utm-zone <UTM区域> \
+  --x-offset <X偏移量> \
+  --y-offset <Y偏移量> \
+  --plugins-dir <插件目录> \
+  --resources-dir <资源目录>
+```
+
+### 列出可用插件
+
+```bash
+python run.py --list-plugins
+```
+
+## 开发自定义插件
+
+1. 在 `plugins` 目录下创建新的Python文件。
+2. 导入并继承 `CustomDataProcessorPlugin` 基类。
+3. 实现所有必需的方法（`can_handle`、`process_data`、`get_required_columns`）。
+4. 如果需要配置文件，可以将其放在 `resources` 目录下。
+
+示例插件模板可参考 `plugins/data_processor_template.py`。
+
+## 系统要求
+
+- Python 3.6+
+- pandas
+- numpy
+- cantools（用于处理CAN数据）
+- bagpy（用于处理ROS包）
+- pyproj（用于坐标投影）
+
+## 编译打包
+
+使用PyInstaller打包为可执行文件：
+
+```bash
+pyinstaller --onedir --name merge_data_process_LST --clean \
+  --add-data="$(python -c 'import bagpy; from pathlib import Path; print(f"{Path(bagpy.__file__).parent}:bagpy")')" \
+  --add-binary="/path/to/engine:." \
+  --add-data="./data_map:data_map" \
+  --add-data="VBox.dbc:." \
+  merge_data_process_LST.py
+```
+
+## 代码优化建议
+
+1. **错误处理**：增强错误处理机制，提供更详细的错误信息和恢复策略。
+2. **日志系统**：引入结构化日志系统，便于调试和问题追踪。
+3. **配置管理**：使用统一的配置管理系统，支持配置文件、环境变量和命令行参数。
+4. **单元测试**：增加单元测试覆盖率，确保代码质量和稳定性。
+5. **文档完善**：为每个模块和函数添加详细的文档字符串。
+6. **性能优化**：识别性能瓶颈并进行优化，特别是大数据处理部分。
+7. **代码重构**：减少重复代码，提高模块化和可维护性。

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+
+
+
+CM_ BO_ 3221225472 "This is a message for not used signals, created by Vector CANdb++ DBC OLE DB Provider.";
+

_internal/data_map/hd_arrow.csv

@@ -0,0 +1,256 @@
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@@ -0,0 +1,14 @@
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_internal/data_map/hd_sign.csv

@@ -0,0 +1,58 @@
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_internal/data_map/hd_stopline.csv

@@ -0,0 +1,73 @@
+stopline_id,stopline_ref_link,stopline_ref_lane,stopline_color,stopline_type,stopline_coords
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_internal/data_map/hd_trafficlight.csv

@@ -0,0 +1,30 @@
+trafficlight_id,trafficlight_ref_link,trafficlight_ref_lane,trafficlight_type,trafficlight_head,trafficlight_coords
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+48100037,20163236,"",1,"2","[[258270.376261255,4150068.000521734],[258270.419235147,4150067.971284435],[258270.634465118,4150067.627163054],[258270.609544266,4150067.653099366],[258270.376261255,4150068.000521734]]"
+48100038,20164040,"",1,"1","[[258355.955595618,4149974.877379770],[258355.971259196,4149974.916798688],[258357.332454086,4149975.682753458],[258357.327809323,4149975.647790618],[258355.955595618,4149974.877379770]]"
+48100039,20163089,"",1,"2","[[258392.881659658,4149965.715729477],[258392.935914920,4149965.718264258],[258393.200030651,4149965.194462779],[258393.138282733,4149965.205251557],[258392.881659658,4149965.715729477]]"
+48100040,20163092,"",1,"2","[[258195.032290965,4150151.958291780],[258195.082421916,4150151.526332816],[258194.948569375,4150151.535663661],[258194.898435207,4150151.967511652],[258195.032290965,4150151.958291780]]"
+48100041,20163170,"",1,"2","[[258314.849734685,4149930.418224048],[258314.525187826,4149930.812055442],[258314.591923577,4149930.805341886],[258314.901055309,4149930.426396978],[258314.849734685,4149930.418224048]]"
+48100042,20163159,"",1,"1","[[258178.233785310,4150157.682985005],[258177.879244164,4150157.631975339],[258177.783046757,4150157.691082007],[258178.137587899,4150157.742091670],[258178.233785310,4150157.682985005]]"
+48100043,20164039,"",1,"2","[[258349.626111732,4149971.334772261],[258350.770028476,4149971.996298286],[258350.788933284,4149971.973535760],[258349.664223528,4149971.321004048],[258349.626111732,4149971.334772261]]"

config/config.json

@@ -0,0 +1,35 @@
+{
+  "processing": {
+    "use_parallel": true,
+    "max_workers": null,
+    "batch_size": 10000,
+    "use_vectorized": true
+  },
+  "database": {
+    "batch_query_size": 10000,
+    "use_parallel_query": true,
+    "max_db_workers": null
+  },
+  "plugins": {
+    "enabled": true,
+    "auto_discover": true
+  },
+  "logging": {
+    "level": "INFO",
+    "log_to_file": true,
+    "log_dir": "logs"
+  },
+  "coordinates": {
+    "utm_zone": 51,
+    "x_offset": 0.0,
+    "y_offset": 0.0
+  },
+  "paths": {
+    "resources_dir": "resources",
+    "plugins_dir": "plugins",
+    "output_dir": "output",
+    "engine_path": "_internal/engine",
+    "map_path": "_internal/data_map",
+    "dbc_path": "_internal/VBox.dbc"
+  }
+}

core/__init__.py

@@ -0,0 +1,9 @@
+"""
+核心组件包，包含插件系统的基础设施
+"""
+
+from .plugin_interface import CustomDataProcessorPlugin
+from .plugin_manager import PluginManager
+from .resource_manager import ResourceManager
+
+__all__ = ['CustomDataProcessorPlugin', 'PluginManager', 'ResourceManager']

core/config_manager.py

@@ -0,0 +1,257 @@
+import json
+import os
+from pathlib import Path
+from typing import Dict, Any, Optional, Union, List
+import yaml
+
+# 全局配置字典
+_CONFIG: Dict[str, Any] = {}
+
+# 默认配置
+DEFAULT_CONFIG = {
+    # 数据处理配置
+    "processing": {
+        "use_parallel": True,
+        "max_workers": None,  # None表示使用CPU核心数
+        "batch_size": 10000,
+        "use_vectorized": True
+    },
+    # 数据库配置
+    "database": {
+        "batch_query_size": 10000,
+        "use_parallel_query": True,
+        "max_db_workers": None
+    },
+    # 插件配置
+    "plugins": {
+        "enabled": True,
+        "auto_discover": True
+    },
+    # 日志配置
+    "logging": {
+        "level": "INFO",
+        "log_to_file": True,
+        "log_dir": "logs"
+    },
+    # 坐标系配置
+    "coordinates": {
+        "utm_zone": 51,
+        "x_offset": 0.0,
+        "y_offset": 0.0
+    },
+    # 文件路径配置
+    "paths": {
+        "resources_dir": "resources",
+        "plugins_dir": "plugins",
+        "output_dir": "output",
+        "engine_path": "_internal/engine",
+        "map_path": "_internal/data_map",
+        "dbc_path": "_internal/VBox.dbc"
+    }
+}
+
+
+def load_config(config_path: Optional[Union[str, Path]] = None) -> Dict[str, Any]:
+    """加载配置文件
+    
+    Args:
+        config_path: 配置文件路径，支持JSON和YAML格式
+        
+    Returns:
+        配置字典
+    """
+    global _CONFIG
+    
+    # 首先使用默认配置
+    _CONFIG = DEFAULT_CONFIG.copy()
+    
+    if config_path is None:
+        # 尝试查找默认配置文件
+        default_paths = [
+            Path("config.json"),
+            Path("config.yaml"),
+            Path("config.yml"),
+            Path("config/config.json"),
+            Path("config/config.yaml"),
+            Path("config/config.yml")
+        ]
+        
+        for path in default_paths:
+            if path.exists():
+                config_path = path
+                break
+    
+    if config_path is None:
+        print("未找到配置文件，使用默认配置")
+        return _CONFIG
+    
+    # 确保config_path是Path对象
+    if isinstance(config_path, str):
+        config_path = Path(config_path)
+    
+    if not config_path.exists():
+        print(f"配置文件不存在: {config_path}，使用默认配置")
+        return _CONFIG
+    
+    try:
+        # 根据文件扩展名选择解析方法
+        suffix = config_path.suffix.lower()
+        
+        if suffix in [".yaml", ".yml"]:
+            with open(config_path, "r", encoding="utf-8") as f:
+                user_config = yaml.safe_load(f)
+        elif suffix == ".json":
+            with open(config_path, "r", encoding="utf-8") as f:
+                user_config = json.load(f)
+        else:
+            print(f"不支持的配置文件格式: {suffix}，使用默认配置")
+            return _CONFIG
+        
+        # 递归合并配置
+        _CONFIG = _merge_configs(_CONFIG, user_config)
+        print(f"成功加载配置文件: {config_path}")
+        
+    except Exception as e:
+        print(f"加载配置文件失败: {e}")
+    
+    return _CONFIG
+
+
+def _merge_configs(base: Dict[str, Any], override: Dict[str, Any]) -> Dict[str, Any]:
+    """递归合并配置字典
+    
+    Args:
+        base: 基础配置
+        override: 覆盖配置
+        
+    Returns:
+        合并后的配置
+    """
+    result = base.copy()
+    
+    for key, value in override.items():
+        # 如果两个都是字典，递归合并
+        if key in result and isinstance(result[key], dict) and isinstance(value, dict):
+            result[key] = _merge_configs(result[key], value)
+        else:
+            # 否则直接覆盖
+            result[key] = value
+    
+    return result
+
+
+def get_config(section: Optional[str] = None) -> Any:
+    """获取配置
+    
+    Args:
+        section: 配置节名称，如果为None则返回整个配置字典
+        
+    Returns:
+        配置值或配置字典
+    """
+    global _CONFIG
+    
+    # 如果配置为空，加载默认配置
+    if not _CONFIG:
+        _CONFIG = DEFAULT_CONFIG.copy()
+    
+    if section is None:
+        return _CONFIG
+    
+    # 支持点号分隔的多级配置访问，如 "database.batch_query_size"
+    if "." in section:
+        parts = section.split(".")
+        current = _CONFIG
+        for part in parts:
+            if part in current:
+                current = current[part]
+            else:
+                print(f"配置节不存在: {section}")
+                return None
+        return current
+    
+    # 单级配置访问
+    return _CONFIG.get(section)
+
+
+def set_config(section: str, value: Any) -> None:
+    """设置配置
+    
+    Args:
+        section: 配置节名称，支持点号分隔的多级配置
+        value: 配置值
+    """
+    global _CONFIG
+    
+    # 如果配置为空，加载默认配置
+    if not _CONFIG:
+        _CONFIG = DEFAULT_CONFIG.copy()
+    
+    # 支持点号分隔的多级配置设置
+    if "." in section:
+        parts = section.split(".")
+        current = _CONFIG
+        
+        # 遍历到倒数第二级
+        for part in parts[:-1]:
+            # 如果中间节点不存在或不是字典，创建新字典
+            if part not in current or not isinstance(current[part], dict):
+                current[part] = {}
+            current = current[part]
+        
+        # 设置最后一级的值
+        current[parts[-1]] = value
+    else:
+        # 单级配置设置
+        _CONFIG[section] = value
+
+
+def save_config(config_path: Union[str, Path], format: str = "json") -> bool:
+    """保存配置到文件
+    
+    Args:
+        config_path: 配置文件路径
+        format: 文件格式，支持 "json" 和 "yaml"
+        
+    Returns:
+        是否保存成功
+    """
+    global _CONFIG
+    
+    # 确保config_path是Path对象
+    if isinstance(config_path, str):
+        config_path = Path(config_path)
+    
+    # 确保目录存在
+    config_path.parent.mkdir(parents=True, exist_ok=True)
+    
+    try:
+        # 根据格式选择保存方法
+        if format.lower() == "yaml":
+            with open(config_path, "w", encoding="utf-8") as f:
+                yaml.dump(_CONFIG, f, default_flow_style=False, sort_keys=False)
+        else:  # 默认使用JSON
+            with open(config_path, "w", encoding="utf-8") as f:
+                json.dump(_CONFIG, f, indent=2, ensure_ascii=False)
+        
+        print(f"配置已保存到: {config_path}")
+        return True
+        
+    except Exception as e:
+        print(f"保存配置失败: {e}")
+        return False
+
+
+def update_config(new_config: Dict[str, Any]) -> None:
+    """更新全局配置
+    
+    Args:
+        new_config: 新的配置字典，将与现有配置合并
+    """
+    global _CONFIG
+    _CONFIG = _merge_configs(_CONFIG, new_config)
+    print("配置已更新")
+
+
+# 初始化时加载配置
+load_config()

core/data_pipeline.py

@@ -0,0 +1,295 @@
+from pathlib import Path
+from typing import Dict, Any, List, Callable, Optional, Union, Tuple
+import pandas as pd
+import numpy as np
+import concurrent.futures
+import time
+import os
+import traceback
+
+from core.error_handler import ProcessingError, ErrorHandler
+
+class DataPipeline:
+    """数据处理管道，提供高效的数据处理流程"""
+    
+    def __init__(self, name: str = "default_pipeline"):
+        self.name = name
+        self.steps = []
+        print(f"初始化流水线 '{name}'")
+        
+    def add_step(self, name: str, func: Callable) -> 'DataPipeline':
+        """添加处理步骤
+        
+        Args:
+            name: 步骤名称
+            func: 处理函数，接收一个DataFrame和可选参数，返回处理后的DataFrame
+            
+        Returns:
+            当前管道实例，支持链式调用
+        """
+        self.steps.append((name, func))
+        print(f"添加处理步骤: {name}")
+        return self
+    
+    @ErrorHandler.measure_performance
+    def process(self, data: pd.DataFrame, **kwargs) -> pd.DataFrame:
+        """执行所有处理步骤
+        
+        Args:
+            data: 输入数据
+            **kwargs: 传递给处理函数的额外参数
+            
+        Returns:
+            处理后的数据
+        """
+        result = data.copy()
+        
+        for name, func in self.steps:
+            start_time = time.time()
+            print(f"开始执行步骤: {name}")
+            
+            try:
+                # 执行处理步骤
+                result = func(result, **kwargs)
+                
+                # 记录处理时间和结果信息
+                elapsed = time.time() - start_time
+                print(f"步骤 {name} 在 {elapsed:.2f} 秒内完成，结果行数: {len(result)}")
+                
+            except Exception as e:
+                print(f"步骤 {name} 失败: {e}")
+                traceback.print_exc()
+                raise ProcessingError(f"处理步骤 {name} 失败: {e}")
+        
+        return result
+    
+    @ErrorHandler.measure_performance
+    def process_batch(self, data: pd.DataFrame, batch_size: int = 10000, **kwargs) -> pd.DataFrame:
+        """分批处理数据
+        
+        Args:
+            data: 输入数据
+            batch_size: 批次大小
+            **kwargs: 传递给处理函数的额外参数
+            
+        Returns:
+            处理后的数据
+        """
+        if len(data) <= batch_size:
+            return self.process(data, **kwargs)
+        
+        print(f"开始分批处理数据，总行数: {len(data)}，批次大小: {batch_size}")
+        
+        # 计算批次数
+        num_batches = (len(data) + batch_size - 1) // batch_size
+        results = []
+        
+        for i in range(num_batches):
+            start_idx = i * batch_size
+            end_idx = min((i + 1) * batch_size, len(data))
+            batch = data.iloc[start_idx:end_idx].copy()
+            
+            print(f"处理批次 {i+1}/{num_batches}，行数: {len(batch)}")
+            batch_result = self.process(batch, **kwargs)
+            results.append(batch_result)
+        
+        # 合并结果
+        result = pd.concat(results, ignore_index=True)
+        print(f"分批处理完成，总行数: {len(result)}")
+        
+        return result
+    
+    @ErrorHandler.measure_performance
+    def process_files_parallel(self, file_paths: List[Path], read_func: Callable[[Path], pd.DataFrame], 
+                             write_func: Callable[[pd.DataFrame, Path], None], output_dir: Path, 
+                             max_workers: Optional[int] = None, **kwargs) -> List[Path]:
+        """并行处理多个文件
+        
+        Args:
+            file_paths: 输入文件路径列表
+            read_func: 读取文件的函数，接收文件路径，返回DataFrame
+            write_func: 写入文件的函数，接收DataFrame和输出路径
+            output_dir: 输出目录
+            max_workers: 最大工作线程数
+            **kwargs: 传递给处理函数的额外参数
+            
+        Returns:
+            处理后的文件路径列表
+        """
+        if max_workers is None or max_workers <= 1 or len(file_paths) <= 1:
+            return self.process_files_sequential(file_paths, read_func, write_func, output_dir, **kwargs)
+        
+        print(f"开始并行处理{len(file_paths)}个文件")
+        output_paths = []
+        
+        # 确保输出目录存在
+        output_dir.mkdir(parents=True, exist_ok=True)
+        
+        # 定义处理单个文件的函数
+        def process_file(file_path):
+            try:
+                # 读取文件
+                data = read_func(file_path)
+                
+                # 处理数据
+                processed_data = self.process(data, **kwargs)
+                
+                # 确定输出路径
+                output_path = output_dir / f"processed_{file_path.name}"
+                
+                # 写入结果
+                write_func(processed_data, output_path)
+                
+                print(f"文件处理完成: {file_path.name} -> {output_path.name}")
+                return output_path
+                
+            except Exception as e:
+                print(f"处理文件失败: {file_path}: {e}")
+                traceback.print_exc()
+                return None
+        
+        # 使用线程池并行处理文件
+        with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
+            futures = {executor.submit(process_file, file_path): file_path for file_path in file_paths}
+            
+            for future in concurrent.futures.as_completed(futures):
+                file_path = futures[future]
+                try:
+                    output_path = future.result()
+                    if output_path:
+                        output_paths.append(output_path)
+                except Exception as e:
+                    print(f"获取处理结果失败: {file_path}: {e}")
+                    traceback.print_exc()
+        
+        print(f"并行处理完成，成功处理: {len(output_paths)}/{len(file_paths)}个文件")
+        return output_paths
+    
+    def process_files_sequential(self, file_paths: List[Path], read_func: Callable[[Path], pd.DataFrame], 
+                               write_func: Callable[[pd.DataFrame, Path], None], output_dir: Path, 
+                               **kwargs) -> List[Path]:
+        """顺序处理多个文件
+        
+        Args:
+            file_paths: 输入文件路径列表
+            read_func: 读取文件的函数，接收文件路径，返回DataFrame
+            write_func: 写入文件的函数，接收DataFrame和输出路径
+            output_dir: 输出目录
+            **kwargs: 传递给处理函数的额外参数
+            
+        Returns:
+            处理后的文件路径列表
+        """
+        print(f"开始顺序处理{len(file_paths)}个文件")
+        output_paths = []
+        
+        # 确保输出目录存在
+        output_dir.mkdir(parents=True, exist_ok=True)
+        
+        for file_path in file_paths:
+            try:
+                # 读取文件
+                data = read_func(file_path)
+                
+                # 处理数据
+                processed_data = self.process(data, **kwargs)
+                
+                # 确定输出路径
+                output_path = output_dir / f"processed_{file_path.name}"
+                
+                # 写入结果
+                write_func(processed_data, output_path)
+                
+                print(f"文件处理完成: {file_path.name} -> {output_path.name}")
+                output_paths.append(output_path)
+                
+            except Exception as e:
+                print(f"处理文件失败: {file_path}: {e}")
+                traceback.print_exc()
+        
+        print(f"顺序处理完成，成功处理: {len(output_paths)}/{len(file_paths)}个文件")
+        return output_paths
+    
+    @staticmethod
+    def create_csv_reader(usecols: Optional[List[str]] = None, dtype: Optional[Dict[str, Any]] = None, 
+                         **pandas_kwargs) -> Callable[[Path], pd.DataFrame]:
+        """创建CSV文件读取函数
+        
+        Args:
+            usecols: 需要读取的列
+            dtype: 列的数据类型
+            **pandas_kwargs: 传递给pd.read_csv的额外参数
+            
+        Returns:
+            读取函数
+        """
+        def read_csv(file_path: Path) -> pd.DataFrame:
+            return pd.read_csv(file_path, usecols=usecols, dtype=dtype, **pandas_kwargs)
+        return read_csv
+    
+    @staticmethod
+    def create_csv_writer(index: bool = False, **pandas_kwargs) -> Callable[[pd.DataFrame, Path], None]:
+        """创建CSV文件写入函数
+        
+        Args:
+            index: 是否写入索引
+            **pandas_kwargs: 传递给pd.to_csv的额外参数
+            
+        Returns:
+            写入函数
+        """
+        def write_csv(data: pd.DataFrame, file_path: Path) -> None:
+            data.to_csv(file_path, index=index, **pandas_kwargs)
+        return write_csv
+    
+    @staticmethod
+    def create_filter_step(filter_func: Callable[[pd.DataFrame], pd.Series], name: str = "filter_data") -> Tuple[str, Callable]:
+        """创建数据过滤步骤
+        
+        Args:
+            filter_func: 过滤函数，接收DataFrame返回布尔Series
+            name: 步骤名称
+            
+        Returns:
+            处理步骤元组(名称, 函数)
+        """
+        def filter_step(data: pd.DataFrame, **kwargs) -> pd.DataFrame:
+            mask = filter_func(data)
+            filtered_data = data[mask].copy()
+            print(f"过滤前行数: {len(data)}, 过滤后行数: {len(filtered_data)}, 过滤掉: {len(data) - len(filtered_data)}行")
+            return filtered_data
+        return (name, filter_step)
+    
+    @staticmethod
+    def create_transform_step(transform_func: Callable[[pd.DataFrame], pd.DataFrame], name: str = "transform_data") -> Tuple[str, Callable]:
+        """创建数据转换步骤
+        
+        Args:
+            transform_func: 转换函数，接收DataFrame返回转换后的DataFrame
+            name: 步骤名称
+            
+        Returns:
+            处理步骤元组(名称, 函数)
+        """
+        def transform_step(data: pd.DataFrame, **kwargs) -> pd.DataFrame:
+            result = transform_func(data)
+            print(f"转换完成: {name}, 输入行数: {len(data)}, 输出行数: {len(result)}")
+            return result
+        return (name, transform_step)
+    
+    @staticmethod
+    def create_column_mapping_step(mapping: Dict[str, str], name: str = "map_columns") -> Tuple[str, Callable]:
+        """创建列映射步骤
+        
+        Args:
+            mapping: 列名映射字典，键为原列名，值为新列名
+            name: 步骤名称
+            
+        Returns:
+            处理步骤元组(名称, 函数)
+        """
+        def map_columns(data: pd.DataFrame, **kwargs) -> pd.DataFrame:
+            result = data.rename(columns=mapping)
+            print(f"列映射完成，映射了 {len(mapping)} 列")
+            return result
+        return (name, map_columns)

core/error_handler.py

@@ -0,0 +1,152 @@
+import sys
+import traceback
+from typing import Callable, Any, Optional, Dict, List, Type, Union
+from functools import wraps
+import time
+
+class ProcessingError(Exception):
+    """数据处理错误的基类"""
+    def __init__(self, message: str, details: Optional[Dict[str, Any]] = None):
+        self.message = message
+        self.details = details or {}
+        super().__init__(message)
+
+class DataValidationError(ProcessingError):
+    """数据验证错误"""
+    pass
+
+class ResourceNotFoundError(ProcessingError):
+    """资源未找到错误"""
+    pass
+
+class PluginError(ProcessingError):
+    """插件相关错误"""
+    pass
+
+class ConfigurationError(ProcessingError):
+    """配置错误"""
+    pass
+
+class EngineExecutionError(ProcessingError):
+    """引擎执行错误"""
+    pass
+
+class ErrorHandler:
+    """错误处理器，提供统一的异常处理机制"""
+    
+    @staticmethod
+    def handle_exceptions(func: Callable) -> Callable:
+        """装饰器：用于处理函数执行期间的异常"""
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            try:
+                return func(*args, **kwargs)
+            except Exception as e:
+                ErrorHandler.handle_exception(e, {"function": func.__name__})
+                raise
+        return wrapper
+
+    @staticmethod
+    def handle_exception(exc: Exception, context: Optional[Dict[str, Any]] = None) -> None:
+        """处理异常，记录日志并提供上下文信息"""
+        context = context or {}
+        
+        # 获取异常的堆栈跟踪
+        exc_type, exc_value, exc_traceback = sys.exc_info()
+        stack_trace = traceback.format_exception(exc_type, exc_value, exc_traceback)
+        
+        # 打印异常信息
+        error_message = f"异常: {type(exc).__name__}: {str(exc)}"
+        print(error_message)
+        
+        # 打印上下文信息
+        if context:
+            print(f"上下文信息: {context}")
+        
+        # 打印堆栈跟踪
+        print("堆栈跟踪:\n" + "".join(stack_trace))
+    
+    @staticmethod
+    def retry(max_attempts: int = 3, delay: float = 1.0, backoff: float = 2.0, 
+              exceptions: Union[Type[Exception], List[Type[Exception]]] = Exception):
+        """重试装饰器，用于自动重试可能失败的操作
+        
+        Args:
+            max_attempts: 最大尝试次数
+            delay: 初始延迟时间（秒）
+            backoff: 延迟时间的增长因子
+            exceptions: 需要捕获的异常类型
+        """
+        def decorator(func):
+            @wraps(func)
+            def wrapper(*args, **kwargs):
+                attempt = 1
+                current_delay = delay
+                
+                while attempt <= max_attempts:
+                    try:
+                        return func(*args, **kwargs)
+                    except exceptions as e:
+                        if attempt == max_attempts:
+                            print(f"重试{max_attempts}次后失败: {func.__name__}")
+                            raise
+                        
+                        print(f"尝试 {attempt}/{max_attempts} 失败: {func.__name__}, 错误: {e}")
+                        print(f"将在 {current_delay:.2f} 秒后重试...")
+                        
+                        time.sleep(current_delay)
+                        attempt += 1
+                        current_delay *= backoff
+            
+            return wrapper
+        return decorator
+    
+    @staticmethod
+    def validate_input(validator: Callable[[Any], bool], error_message: str = "输入验证失败"):
+        """输入验证装饰器，用于验证函数输入参数
+        
+        Args:
+            validator: 验证函数，接收函数的所有参数并返回布尔值
+            error_message: 验证失败时的错误消息
+        """
+        def decorator(func):
+            @wraps(func)
+            def wrapper(*args, **kwargs):
+                if not validator(*args, **kwargs):
+                    raise DataValidationError(error_message)
+                return func(*args, **kwargs)
+            return wrapper
+        return decorator
+    
+    @staticmethod
+    def measure_performance(func):
+        """性能测量装饰器，用于记录函数执行时间"""
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            start_time = time.time()
+            result = func(*args, **kwargs)
+            end_time = time.time()
+            execution_time = end_time - start_time
+            print(f"函数 {func.__name__} 执行时间: {execution_time:.4f} 秒")
+            return result
+        return wrapper
+    
+    @staticmethod
+    def safe_execute(fallback_value: Any = None, log_exception: bool = True):
+        """安全执行装饰器，捕获异常并返回默认值
+        
+        Args:
+            fallback_value: 发生异常时返回的默认值
+            log_exception: 是否记录异常信息
+        """
+        def decorator(func):
+            @wraps(func)
+            def wrapper(*args, **kwargs):
+                try:
+                    return func(*args, **kwargs)
+                except Exception as e:
+                    if log_exception:
+                        print(f"函数 {func.__name__} 执行失败: {e}")
+                    return fallback_value
+            return wrapper
+        return decorator

core/optimized_processor.py

@@ -0,0 +1,175 @@
+from pathlib import Path
+from typing import Optional
+import traceback
+
+from .processors.built_in.lst import ZipCSVProcessor, RosbagProcessor, Config
+# from .processors.final_processor import FinalDataProcessor
+from core.processors.built_in.lst import data_precheck, run_cpp_engine, FinalDataProcessor
+
+
+
+def process_lst_data(
+    zip_data_path: Path,
+    output_base_dir: Path,
+    trafficlight_json_path: Optional[Path] = None,
+    utm_zone: int = 51,
+    x_offset: float = 0.0,
+    y_offset: float = 0.0,
+    continue_to_iterate: bool = False,
+) -> Optional[Path]:
+    """
+    Processes LST data using an optimized pipeline.
+
+    Args:
+        zip_data_path: Path to the input ZIP file
+        output_base_dir: Base directory for output
+        trafficlight_json_path: Optional path to traffic light JSON file
+        utm_zone: UTM zone for coordinate projection
+        x_offset: X offset for C++ engine
+        y_offset: Y offset for C++ engine
+        continue_to_iterate: Flag to control iteration continuation
+
+    Returns:
+        Path to the final merged_ObjState.csv file if successful, None otherwise
+    """
+    print(f"Starting LST data processing for: {zip_data_path.name}")
+    
+    # Validate input paths
+    if not zip_data_path.exists():
+        print(f"Error: Input ZIP file not found: {zip_data_path}")
+        return None
+        
+    if not trafficlight_json_path:
+        print(f"Warning: Traffic light JSON file not found: {trafficlight_json_path}")
+        trafficlight_json_path = None
+
+    try:
+        # Initialize configuration
+        config = Config(
+            zip_path=zip_data_path.resolve(),
+            output_path=output_base_dir.resolve(),
+            json_path=trafficlight_json_path.resolve() if trafficlight_json_path else None,
+            dbc_path=Path("_internal/VBox.dbc").resolve(),
+            engine_path=Path("_internal/engine").resolve(),
+            map_path=Path("_internal/data_map").resolve(),
+            utm_zone=utm_zone,
+            x_offset=x_offset,
+            y_offset=y_offset
+        )
+
+        # Process built-in data types
+        print("Processing built-in data types...")
+        zip_processor = ZipCSVProcessor(config)
+        zip_processor.process_zip()
+
+        # Process rosbag data if available
+        rosbag_processor = RosbagProcessor(config)
+        rosbag_processor.process_zip_for_rosbags()
+
+        # Run C++ engine for additional processing
+        if not run_cpp_engine(config):
+            raise RuntimeError("C++ engine execution failed")
+
+        # Validate processed data
+        if not data_precheck(config.output_dir):
+            raise ValueError("Data quality pre-check failed")
+
+        # Final processing of built-in data
+        print("Processing and merging built-in data...")
+        final_processor = FinalDataProcessor(config)
+        
+        if not final_processor.process():
+            raise RuntimeError("Final data processing failed")
+
+        final_csv_path = config.output_dir / "merged_ObjState.csv"
+
+        
+        return final_csv_path
+
+    except Exception as e:
+        print(f"Error: Processing failed for {zip_data_path.name}: {str(e)}")
+        print(f"Debug: Stacktrace: {traceback.format_exc()}")
+        return None
+
+
+def process_pgvil_data(
+    zip_data_path: Path,
+    output_base_dir: Path,
+    utm_zone: int = 51,
+    x_offset: float = 0.0,
+    y_offset: float = 0.0
+) -> Optional[Path]:
+    """处理PGVIL数据
+    
+    Args:
+        zip_data_path: ZIP数据文件路径
+        output_base_dir: 输出基础目录
+        utm_zone: UTM坐标系区域
+        x_offset: X坐标偏移量
+        y_offset: Y坐标偏移量
+        
+    Returns:
+        Optional[Path]: 处理后的CSV文件路径，处理失败则返回None
+    """
+    try:
+        # 确保输出目录存在
+        output_base_dir.mkdir(parents=True, exist_ok=True)
+        
+        # 解压ZIP文件
+        if not extract_zip_file(zip_data_path, output_base_dir):
+            return None
+            
+        # 查找所有PGVIL数据文件
+        pgvil_files = []
+        for root, _, files in os.walk(output_base_dir):
+            for file in files:
+                if file.lower().endswith(('.csv', '.json')):
+                    pgvil_files.append(Path(root) / file)
+                    
+        if not pgvil_files:
+            print(f"在 {output_base_dir} 中未找到PGVIL数据文件")
+            return None
+            
+        print(f"找到 {len(pgvil_files)} 个PGVIL数据文件")
+        
+        # 处理所有PGVIL文件
+        all_data = []
+        for pgvil_file in pgvil_files:
+            try:
+                # 读取PGVIL文件
+                if pgvil_file.suffix.lower() == '.csv':
+                    df = pd.read_csv(pgvil_file)
+                elif pgvil_file.suffix.lower() == '.json':
+                    with open(pgvil_file, 'r') as f:
+                        data = json.load(f)
+                    df = pd.DataFrame(data)
+                    
+                # 确保必要的列存在
+                required_cols = ['simTime', 'simFrame', 'playerId']
+                for col in required_cols:
+                    if col not in df.columns:
+                        df[col] = 0  # 添加默认值
+                        
+                all_data.append(df)
+                print(f"成功处理文件: {pgvil_file}")
+            except Exception as e:
+                print(f"处理文件 {pgvil_file} 时出错: {e}")
+        
+        if not all_data:
+            print("没有成功处理任何PGVIL文件")
+            return None
+            
+        # 合并所有数据
+        combined_df = pd.concat(all_data, ignore_index=True)
+        
+        # 保存处理后的数据
+        output_path = output_base_dir / "processed_pgvil_data.csv"
+        combined_df.to_csv(output_path, index=False)
+        print(f"成功处理所有PGVIL数据，结果保存到: {output_path}")
+        return output_path
+        
+    except Exception as e:
+        print(f"处理PGVIL数据时出错: {e}")
+        import traceback
+        traceback.print_exc()
+        return None

core/plugin_interface.py

@@ -0,0 +1,106 @@
+from abc import ABC, abstractmethod
+from pathlib import Path
+import pandas as pd
+from typing import Dict, Any, Optional, Tuple
+import tempfile
+import zipfile
+
+class CustomDataProcessorPlugin(ABC):
+    """自定义数据处理插件的基类，提供通用方法"""
+    
+    @abstractmethod
+    def can_handle(self, zip_path: Path, folder_name: str) -> bool:
+        """检查是否可以处理指定的数据文件夹"""
+        pass
+    
+    @abstractmethod
+    def get_required_columns(self) -> Dict[str, Any]:
+        """返回必需的列和它们的类型"""
+        pass
+
+    def process_data(self, zip_path: Path, folder_name: str, output_dir: Path) -> Optional[pd.DataFrame]:
+        """处理数据的主方法，返回处理后的DataFrame"""
+        try:
+            with tempfile.TemporaryDirectory() as temp_dir:
+                temp_path = Path(temp_dir)
+                
+                # 提取数据文件
+                csv_files = self._extract_files(zip_path, folder_name, temp_path)
+                if not csv_files:
+                    return None
+                
+                # 处理数据
+                df = self._process_extracted_files(csv_files)
+                if df is None or df.empty:
+                    return None
+                
+                return df
+                
+        except Exception as e:
+            print(f"处理数据时发生错误: {e}")
+            import traceback
+            traceback.print_exc()
+            return None
+
+    def _extract_files(self, zip_path: Path, folder_name: str, temp_path: Path) -> list:
+        """从ZIP文件中提取数据文件，不限制文件格式"""
+        try:
+            with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+                # 获取指定文件夹下的所有文件，不限制格式
+                files = [name for name in zip_ref.namelist() 
+                        if name.startswith(f"{folder_name}/")]
+                
+                if not files:
+                    print(f"在{folder_name}中未找到数据文件")
+                    return []
+                    
+                # 解压所有文件
+                for file_name in files:
+                    zip_ref.extract(file_name, temp_path)
+                
+                # 返回解压后的所有文件路径
+                return list(temp_path.glob(f"{folder_name}/*"))
+                
+        except Exception as e:
+            print(f"提取文件时发生错误: {e}")
+            return []
+
+    @abstractmethod
+    def _process_extracted_files(self, file_paths: list) -> Optional[pd.DataFrame]:
+        """处理提取的文件"""
+        pass
+
+    def _validate_data(self, df: pd.DataFrame) -> bool:
+        """验证数据是否满足要求"""
+        required_cols = self.get_required_columns()
+        
+        # 检查必需列是否存在
+        missing_cols = [col for col in required_cols.keys() if col not in df.columns]
+        if missing_cols:
+            print(f"错误：缺少必需列: {missing_cols}")
+            return False
+            
+        # 检查数据类型
+        for col, dtype in required_cols.items():
+            try:
+                df[col] = df[col].astype(dtype)
+            except Exception as e:
+                print(f"错误：列 {col} 的数据类型转换失败: {e}")
+                return False
+                
+        return True
+
+    def _preprocess_data(self, df: pd.DataFrame) -> pd.DataFrame:
+        """数据预处理的通用方法"""
+        # 移除重复行
+        df = df.drop_duplicates()
+        
+        # 确保时间戳列的精度为3位
+        if 'simTime' in df.columns:
+            df['simTime'] = df['simTime'].round(3)
+        
+        # 按时间排序
+        if 'simTime' in df.columns:
+            df.sort_values('simTime', inplace=True)
+            
+        return df

core/plugin_manager.py

@@ -0,0 +1,83 @@
+import importlib
+import inspect
+from pathlib import Path
+from typing import Dict, List, Type, Optional
+import sys
+
+from .plugin_interface import CustomDataProcessorPlugin
+
+class PluginManager:
+    """Manages the discovery and loading of data processor plugins."""
+    
+    def __init__(self, plugin_dir: Path):
+        self.plugin_dir = plugin_dir
+        self.plugins: Dict[str, Type[CustomDataProcessorPlugin]] = {}
+        self._load_plugins()
+    
+    def _load_plugins(self) -> None:
+        """发现并加载插件目录中的所有插件"""
+        if not self.plugin_dir.exists():
+            print(f"未找到插件目录: {self.plugin_dir}")
+            return
+            
+        # 将插件目录添加到Python路径
+        sys.path.insert(0, str(self.plugin_dir.parent))
+        
+        try:
+            # 查找插件目录中的Python文件
+            for plugin_file in self.plugin_dir.glob("*.py"):
+                if plugin_file.name == "__init__.py":
+                    continue
+                    
+                try:
+                    # 修改模块导入方式，直接使用文件名
+                    module_name = f"plugins.{plugin_file.stem}"
+                    print(f"尝试加载插件模块: {module_name}")
+                        
+                    module = importlib.import_module(module_name)
+                    
+                    # 在模块中查找插件类
+                    for name, obj in inspect.getmembers(module):
+                        if (inspect.isclass(obj) 
+                            and issubclass(obj, CustomDataProcessorPlugin) 
+                            and obj != CustomDataProcessorPlugin):
+                            # 从类名中获取关键词（移除DataProcessor或Processor后缀）
+                            keyword = name.lower().replace('dataprocessor', '').replace('processor', '')
+                            self.plugins[keyword] = obj
+                            print(f"成功加载插件: {name}, 关键词: {keyword}")
+                            
+                except ImportError as e:
+                    print(f"导入插件 {plugin_file.name} 失败: {e}")
+                except Exception as e:
+                    print(f"加载插件 {plugin_file.name} 时出错: {e}")
+                    
+        finally:
+            # 从Python路径中移除插件目录
+            if str(self.plugin_dir.parent) in sys.path:
+                sys.path.remove(str(self.plugin_dir.parent))
+            
+    def get_plugins(self) -> List[Type[CustomDataProcessorPlugin]]:
+        """Returns a list of all loaded plugin classes."""
+        return list(self.plugins.values())
+    
+    def find_plugin_by_folder_name(self, folder_name: str) -> Optional[Type[CustomDataProcessorPlugin]]:
+        """根据文件夹名称查找匹配的插件"""
+        folder_name = folder_name.lower()
+        for keyword, plugin_class in self.plugins.items():
+            if keyword in folder_name:
+                return plugin_class
+        return None
+    
+    def get_plugin_for_data(self, zip_path: Path, folder_name: str) -> Type[CustomDataProcessorPlugin]:
+        """查找能处理给定数据的插件"""
+        # 首先通过文件夹名称匹配插件
+        plugin_class = self.find_plugin_by_folder_name(folder_name)
+        if plugin_class:
+            try:
+                plugin = plugin_class()
+                if plugin.can_handle(zip_path, folder_name):
+                    return plugin_class
+            except Exception as e:
+                print(f"检查插件 {plugin_class.__name__} 时出错: {e}")
+                
+        return None

core/processors/built_in/__init__.py

@@ -0,0 +1,24 @@
+
+from .lst import (
+    ZipCSVProcessor, 
+    RosbagProcessor, 
+    FinalDataProcessor,
+    Config,
+    run_cpp_engine,
+    data_precheck,
+    get_base_path
+)
+
+
+
+__all__ = [
+   
+    'ZipCSVProcessor',
+    'RosbagProcessor',
+    'FinalDataProcessor',
+
+    'Config',
+    'run_cpp_engine',
+    'data_precheck',
+    'get_base_path'
+]

core/processors/built_in/lst.py

@@ -0,0 +1,1449 @@
+import zipfile
+import sqlite3
+import csv
+import tempfile
+from pathlib import Path
+from typing import List, Dict, Tuple, Optional, Any, NamedTuple
+import cantools
+import os
+import subprocess
+import numpy as np
+import pandas as pd
+from collections import Counter
+from datetime import datetime
+import argparse
+import sys
+from pyproj import Proj
+from bagpy.bagreader import bagreader
+import shutil
+import json
+from dataclasses import dataclass, field
+
+# --- Constants ---
+PLAYER_ID_EGO = 1
+PLAYER_ID_OBJ = 2
+DEFAULT_TYPE = 1
+OUTPUT_CSV_OBJSTATE = "ObjState.csv"
+OUTPUT_CSV_TEMP_OBJSTATE = "ObjState_temp_intermediate.csv" # Should be eliminated
+OUTPUT_CSV_EGOSTATE = "EgoState.csv" # Not used in final merge? Check logic if needed.
+OUTPUT_CSV_MERGED = "merged_ObjState.csv"
+OUTPUT_CSV_OBU = "OBUdata.csv"
+OUTPUT_CSV_LANEMAP = "LaneMap.csv"
+OUTPUT_CSV_EGOMAP = "EgoMap.csv"
+OUTPUT_CSV_FUNCTION = "Function.csv"
+ROADMARK_CSV = "RoadMark.csv"
+
+# --- Configuration Class ---
+@dataclass
+class Config:
+    """Holds configuration paths and settings."""
+    zip_path: Path
+    output_path: Path
+    json_path: Optional[Path]  # Make json_path optional
+    dbc_path: Optional[Path] = None
+    engine_path: Optional[Path] = None
+    map_path: Optional[Path] = None
+    utm_zone: int = 51 # Example UTM zone
+    x_offset: float = 0.0
+    y_offset: float = 0.0
+
+    # Derived paths
+    output_dir: Path = field(init=False)
+
+    def __post_init__(self):
+        # Use output_path directly as output_dir to avoid nested directories
+        self.output_dir = self.output_path
+        self.output_dir.mkdir(parents=True, exist_ok=True)
+
+
+# --- Zip/CSV Processing ---
+class ZipCSVProcessor:
+    """Processes DB files within a ZIP archive to generate CSV data."""
+
+    # Define column mappings more clearly
+    EGO_COLS_NEW = [
+        "simTime", "simFrame", "playerId", "v", "speedX", "speedY",
+        "posH", "speedH", "posX", "posY", "accelX", "accelY",
+        "travelDist", "composite_v", "relative_dist", "type" # Added type
+    ]
+    OBJ_COLS_OLD_SUFFIXED = [
+        "v_obj", "speedX_obj", "speedY_obj", "posH_obj", "speedH_obj",
+        "posX_obj", "posY_obj", "accelX_obj", "accelY_obj", "travelDist_obj"
+    ]
+    OBJ_COLS_MAPPING = {old: new for old, new in zip(OBJ_COLS_OLD_SUFFIXED, EGO_COLS_NEW[3:13])} # Map suffixed cols to standard names
+
+    def __init__(self, config: Config):
+        self.config = config
+        self.dbc = self._load_dbc(config.dbc_path)
+        self.projection = Proj(proj='utm', zone=config.utm_zone, ellps='WGS84', preserve_units='m')
+        self._init_table_config()
+        self._init_keyword_mapping()
+
+    def _load_dbc(self, dbc_path: Optional[Path]) -> Optional[cantools.db.Database]:
+        if not dbc_path or not dbc_path.exists():
+            print("DBC path not provided or file not found.")
+            return None
+        try:
+            return cantools.db.load_file(dbc_path)
+        except Exception as e:
+            print(f"DBC loading failed: {e}")
+            return None
+
+    def _init_table_config(self):
+        """Initializes configurations for different table types."""
+        self.table_config = {
+            "gnss_table": self._get_gnss_config(),
+            "can_table": self._get_can_config()
+        }
+
+    def _get_gnss_config(self):
+        # Keep relevant columns, adjust mapping as needed
+        return {
+            "output_columns": self.EGO_COLS_NEW, # Use the standard ego columns + type
+            "mapping": { # Map output columns to source DB columns/signals
+                "simTime": ("second", "usecond"),
+                "simFrame": "ID",
+                "v": "speed",
+                "speedY": "y_speed",
+                "speedX": "x_speed",
+                "posH": "yaw",
+                "speedH": "yaw_rate",
+                "posX": "latitude_dd",  # Source before projection
+                "posY": "longitude_dd", # Source before projection
+                "accelX": "x_acceleration",
+                "accelY": "y_acceleration",
+                "travelDist": "total_distance",
+                # composite_v/relative_dist might not be direct fields in GNSS, handle later if needed
+                "composite_v": "speed", # Placeholder, adjust if needed
+                "relative_dist": None,  # Placeholder, likely not in GNSS data
+                "type": None # Will be set later
+            },
+            "db_columns": ["ID", "second", "usecond", "speed", "y_speed", "x_speed",
+                           "yaw", "yaw_rate", "latitude_dd", "longitude_dd",
+                           "x_acceleration", "y_acceleration", "total_distance"] # Actual cols to SELECT
+        }
+
+    def _get_can_config(self):
+        # Define columns needed from DB/CAN signals for both EGO and OBJ
+        return {
+             "mapping": { # Map unified output columns to CAN signals or direct fields
+                 # EGO mappings (VUT = Vehicle Under Test)
+                "simTime": ("second", "usecond"),
+                "simFrame": "ID",
+                "canid": "canid", # Keep CAN ID for reference if needed
+                "v": "VUT_Speed_mps",
+                "speedX": "VUT_Speed_x_mps",
+                "speedY": "VUT_Speed_y_mps",
+                "speedH": "VUT_Yaw_Rate",
+                "posX": "VUT_GPS_Latitude",   # Source before projection
+                "posY": "VUT_GPS_Longitude",  # Source before projection
+                "posH": "VUT_Heading",
+                "accelX": "VUT_Acc_X",
+                "accelY": "VUT_Acc_Y",
+                # OBJ mappings (UFO = Unidentified Flying Object / Other Vehicle)
+                "v_obj": "Speed_mps",
+                "speedX_obj": "UFO_Speed_x_mps",
+                "speedY_obj": "UFO_Speed_y_mps",
+                "speedH_obj": "Yaw_Rate",
+                "posX_obj": "GPS_Latitude",    # Source before projection
+                "posY_obj": "GPS_Longitude",   # Source before projection
+                "posH_obj": "Heading",
+                "accelX_obj": "Acc_X",
+                "accelY_obj": "Acc_Y",
+                # Relative Mappings
+                "composite_v": "VUT_Rel_speed_long_mps",
+                "relative_dist": "VUT_Dist_MRP_Abs",
+                # travelDist often calculated, not direct CAN signal
+                "travelDist": None, # Placeholder
+                "travelDist_obj": None # Placeholder
+             },
+             "db_columns": ["ID", "second", "usecond", "timestamp", "canid", "len", "frame"] # Core DB columns
+        }
+
+    def _init_keyword_mapping(self):
+        """Maps keywords in filenames to table configurations and output CSV names."""
+        self.keyword_mapping = {
+            "gnss": ("gnss_table", OUTPUT_CSV_OBJSTATE), # GNSS likely represents ego, writing to ObjState first? Revisit logic if needed.
+            "can2": ("can_table", OUTPUT_CSV_OBJSTATE), # Process CAN data into the combined ObjState file
+        }
+
+    def process_zip(self) -> None:
+        """Extracts and processes DB files from the configured ZIP path."""
+        print(f"Processing ZIP: {self.config.zip_path}")
+        output_dir = self.config.output_dir # Already created in Config
+
+        try:
+            with zipfile.ZipFile(self.config.zip_path, "r") as zip_ref:
+                db_files_to_process = []
+                for file_info in zip_ref.infolist():
+                    # Check if it's a DB file in the CANdata directory
+                    if 'CANdata/' in file_info.filename and file_info.filename.endswith('.db'):
+                         # Check if the filename contains any of the keywords
+                        match = self._match_keyword(file_info.filename)
+                        if match:
+                            table_type, csv_name = match
+                            db_files_to_process.append((file_info, table_type, csv_name))
+
+                if not db_files_to_process:
+                    print("No relevant DB files found in CANdata/ matching keywords.")
+                    return
+
+                # Process matched DB files
+                with tempfile.TemporaryDirectory() as tmp_dir_str:
+                    tmp_dir = Path(tmp_dir_str)
+                    for file_info, table_type, csv_name in db_files_to_process:
+                        print(f"Processing DB: {file_info.filename} for table type {table_type}")
+                        extracted_path = tmp_dir / Path(file_info.filename).name
+                        try:
+                            # Extract the specific DB file
+                            with zip_ref.open(file_info.filename) as source, open(extracted_path, "wb") as target:
+                                shutil.copyfileobj(source, target)
+
+                            # Process the extracted DB file
+                            self._process_db_file(extracted_path, output_dir, table_type, csv_name)
+
+                        except (sqlite3.Error, pd.errors.EmptyDataError, FileNotFoundError, KeyError) as e:
+                             print(f"Error processing DB file {file_info.filename}: {e}")
+                        except Exception as e:
+                            print(f"Unexpected error processing DB file {file_info.filename}: {e}")
+                        finally:
+                            if extracted_path.exists():
+                                extracted_path.unlink() # Clean up extracted file
+
+        except zipfile.BadZipFile:
+            print(f"Error: Bad ZIP file: {self.config.zip_path}")
+        except FileNotFoundError:
+            print(f"Error: ZIP file not found: {self.config.zip_path}")
+        except Exception as e:
+            print(f"An error occurred during ZIP processing: {e}")
+
+
+    def _match_keyword(self, filename: str) -> Optional[Tuple[str, str]]:
+        """Finds the first matching keyword configuration for a filename."""
+        for keyword, (table_type, csv_name) in self.keyword_mapping.items():
+            if keyword in filename:
+                return table_type, csv_name
+        return None
+
+    def _process_db_file(
+        self, db_path: Path, output_dir: Path, table_type: str, csv_name: str
+    ) -> None:
+        """Connects to SQLite DB and processes the specified table type."""
+        output_csv_path = output_dir / csv_name
+        try:
+            # Use URI for read-only connection
+            conn_str = f"file:{db_path}?mode=ro"
+            with sqlite3.connect(conn_str, uri=True) as conn:
+                cursor = conn.cursor()
+                if not self._check_table_exists(cursor, table_type):
+                     print(f"Table '{table_type}' does not exist in {db_path.name}. Skipping.")
+                     return
+                if self._check_table_empty(cursor, table_type):
+                    print(f"Table '{table_type}' in {db_path.name} is empty. Skipping.")
+                    return
+
+                print(f"Exporting data from table '{table_type}' to {output_csv_path}")
+                if table_type == "can_table":
+                    self._process_can_table_optimized(cursor, output_csv_path)
+                elif table_type == "gnss_table":
+                     # Pass output_path directly, avoid intermediate steps
+                    self._process_gnss_table(cursor, output_csv_path)
+                else:
+                    print(f"Warning: No specific processor for table type '{table_type}'. Skipping.")
+
+        except sqlite3.OperationalError as e:
+             print(f"Database operational error for {db_path.name}: {e}. Check file integrity/permissions.")
+        except sqlite3.DatabaseError as e:
+            print(f"Database error connecting to {db_path.name}: {e}")
+        except Exception as e:
+            print(f"Unexpected error processing DB {db_path.name}: {e}")
+
+    def _check_table_exists(self, cursor, table_name: str) -> bool:
+        """Checks if a table exists in the database."""
+        try:
+            cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name=?;", (table_name,))
+            return cursor.fetchone() is not None
+        except sqlite3.Error as e:
+            print(f"Error checking existence of table {table_name}: {e}")
+            return False # Assume not exists on error
+
+
+    def _check_table_empty(self, cursor, table_name: str) -> bool:
+        """Checks if a table is empty."""
+        try:
+            cursor.execute(f"SELECT COUNT(*) FROM {table_name}") # Use COUNT(*) for efficiency
+            count = cursor.fetchone()[0]
+            return count == 0
+        except sqlite3.Error as e:
+            # If error occurs (e.g., table doesn't exist after check - race condition?), treat as problematic/empty
+            print(f"Error checking if table {table_name} is empty: {e}")
+            return True
+
+    def _process_gnss_table(self, cursor, output_path: Path) -> None:
+        """Processes gnss_table data and writes directly to CSV."""
+        config = self.table_config["gnss_table"]
+        db_columns = config["db_columns"]
+        output_columns = config["output_columns"]
+        mapping = config["mapping"]
+
+        try:
+            cursor.execute(f"SELECT {', '.join(db_columns)} FROM gnss_table")
+            rows = cursor.fetchall()
+            if not rows:
+                print("No data found in gnss_table.")
+                return
+
+            processed_data = []
+            for row in rows:
+                row_dict = dict(zip(db_columns, row))
+                record = {}
+
+                # Calculate simTime
+                record["simTime"] = round(row_dict.get("second", 0) + row_dict.get("usecond", 0) / 1e6, 2)
+
+                # Map other columns
+                for out_col in output_columns:
+                    if out_col == "simTime": continue # Already handled
+                    if out_col == "playerId":
+                         record[out_col] = PLAYER_ID_EGO # Assuming GNSS is ego
+                         continue
+                    if out_col == "type":
+                         record[out_col] = DEFAULT_TYPE
+                         continue
+
+                    source_info = mapping.get(out_col)
+                    if source_info is None:
+                         record[out_col] = 0.0 # Or np.nan if preferred
+                    elif isinstance(source_info, tuple):
+                         # This case was only for simTime, handled above
+                         record[out_col] = 0.0
+                    else: # Direct mapping from db_columns
+                         raw_value = row_dict.get(source_info)
+                         if raw_value is not None:
+                              # Handle projection for position columns
+                             if out_col == "posX":
+                                 # Assuming source_info = "latitude_dd"
+                                 lat = row_dict.get(mapping["posX"])
+                                 lon = row_dict.get(mapping["posY"])
+                                 if lat is not None and lon is not None:
+                                     proj_x, _ = self.projection(lon, lat)
+                                     record[out_col] = round(proj_x, 6)
+                                 else:
+                                     record[out_col] = 0.0
+                             elif out_col == "posY":
+                                 # Assuming source_info = "longitude_dd"
+                                 lat = row_dict.get(mapping["posX"])
+                                 lon = row_dict.get(mapping["posY"])
+                                 if lat is not None and lon is not None:
+                                     _, proj_y = self.projection(lon, lat)
+                                     record[out_col] = round(proj_y, 6)
+                                 else:
+                                     record[out_col] = 0.0
+                             elif out_col in ["composite_v", "relative_dist"]:
+                                 # Handle these based on source if available, else default
+                                 record[out_col] = round(float(raw_value), 3) if source_info else 0.0
+                             else:
+                                 # General case: round numeric values
+                                 try:
+                                      record[out_col] = round(float(raw_value), 3)
+                                 except (ValueError, TypeError):
+                                      record[out_col] = raw_value # Keep as is if not numeric
+                         else:
+                            record[out_col] = 0.0 # Default for missing source data
+
+                processed_data.append(record)
+
+            if processed_data:
+                df_final = pd.DataFrame(processed_data)[output_columns] # Ensure column order
+                df_final.to_csv(output_path, index=False, encoding="utf-8")
+                print(f"Successfully wrote GNSS data to {output_path}")
+            else:
+                 print("No processable records found in gnss_table.")
+
+        except sqlite3.Error as e:
+            print(f"SQL error during GNSS processing: {e}")
+        except Exception as e:
+            print(f"Unexpected error during GNSS processing: {e}")
+
+
+    def _process_can_table_optimized(self, cursor, output_path: Path) -> None:
+        """Processes CAN data directly into the final merged DataFrame format."""
+        config = self.table_config["can_table"]
+        db_columns = config["db_columns"]
+        mapping = config["mapping"]
+
+        try:
+            cursor.execute(f"SELECT {', '.join(db_columns)} FROM can_table")
+            rows = cursor.fetchall()
+            if not rows:
+                print("No data found in can_table.")
+                return
+
+            all_records = []
+            for row in rows:
+                row_dict = dict(zip(db_columns, row))
+                # Decode CAN frame if DBC is available
+                decoded_signals = self._decode_can_frame(row_dict)
+                # Create a unified record combining DB fields and decoded signals
+                record = self._create_unified_can_record(row_dict, decoded_signals, mapping)
+                if record: # Only add if parsing was successful
+                     all_records.append(record)
+
+            if not all_records:
+                print("No CAN records could be successfully processed.")
+                return
+
+            # Convert raw records to DataFrame for easier manipulation
+            df_raw = pd.DataFrame(all_records)
+
+            # Separate EGO and OBJ data based on available columns
+            df_ego = self._extract_vehicle_data(df_raw, PLAYER_ID_EGO)
+            df_obj = self._extract_vehicle_data(df_raw, PLAYER_ID_OBJ)
+
+            # Project coordinates
+            df_ego = self._project_coordinates(df_ego, 'posX', 'posY')
+            df_obj = self._project_coordinates(df_obj, 'posX', 'posY') # Use same column names after extraction
+
+             # Add calculated/default columns
+            df_ego['type'] = DEFAULT_TYPE
+            df_obj['type'] = DEFAULT_TYPE
+            # Note: travelDist is often calculated later or not available directly
+
+            # Ensure both have the same columns before merging
+            final_columns = self.EGO_COLS_NEW # Target columns
+            df_ego = df_ego.reindex(columns=final_columns)
+            df_obj = df_obj.reindex(columns=final_columns)
+
+            # Merge EGO and OBJ dataframes
+            df_merged = pd.concat([df_ego, df_obj], ignore_index=True)
+
+            # Sort and clean up
+            df_merged.sort_values(by=["simTime", "simFrame", "playerId"], inplace=True)
+            df_merged.reset_index(drop=True, inplace=True)
+
+            # Fill potential NaNs introduced by reindexing or missing data
+            # Choose appropriate fill strategy (e.g., 0, forward fill, or leave as NaN)
+            df_merged.fillna(0.0, inplace=True) # Example: fill with 0.0
+
+            # Save the final merged DataFrame
+            df_merged.to_csv(output_path, index=False, encoding="utf-8")
+            print(f"Successfully processed CAN data and wrote merged output to {output_path}")
+
+        except sqlite3.Error as e:
+            print(f"SQL error during CAN processing: {e}")
+        except KeyError as e:
+             print(f"Key error during CAN processing - mapping issue? Missing key: {e}")
+        except Exception as e:
+            print(f"Unexpected error during CAN processing: {e}")
+            import traceback
+            traceback.print_exc() # Print detailed traceback for debugging
+
+
+    def _decode_can_frame(self, row_dict: Dict) -> Dict[str, Any]:
+        """Decodes CAN frame using DBC file if available."""
+        decoded_signals = {}
+        if self.dbc and 'canid' in row_dict and 'frame' in row_dict and 'len' in row_dict:
+            can_id = row_dict['canid']
+            frame_bytes = bytes(row_dict['frame'][:row_dict['len']]) # Ensure correct length
+            try:
+                message_def = self.dbc.get_message_by_frame_id(can_id)
+                decoded_signals = message_def.decode(frame_bytes, decode_choices=False, allow_truncated=True) # Allow truncated
+            except KeyError:
+                # Optional: print(f"Warning: CAN ID 0x{can_id:X} not found in DBC.")
+                pass # Ignore unknown IDs silently
+            except ValueError as e:
+                 print(f"Warning: Decoding ValueError for CAN ID 0x{can_id:X} (length {row_dict['len']}, data: {frame_bytes.hex()}): {e}")
+            except Exception as e:
+                print(f"Warning: Error decoding CAN ID 0x{can_id:X}: {e}")
+        return decoded_signals
+
+    def _create_unified_can_record(self, row_dict: Dict, decoded_signals: Dict, mapping: Dict) -> Optional[Dict[str, Any]]:
+        """Creates a single record combining DB fields and decoded signals based on mapping."""
+        record = {}
+        try:
+            # Handle time and frame ID first
+            record["simTime"] = round(row_dict.get("second", 0) + row_dict.get("usecond", 0) / 1e6, 2)
+            record["simFrame"] = row_dict.get("ID")
+            record["canid"] = f"0x{row_dict.get('canid', 0):X}" # Store CAN ID if needed
+
+            # Populate record using the mapping config
+            for target_col, source_info in mapping.items():
+                if target_col in ["simTime", "simFrame", "canid"]: continue # Already handled
+                if isinstance(source_info, tuple): continue # Should only be time
+
+                # source_info is now the signal name (or None)
+                signal_name = source_info
+                if signal_name and signal_name in decoded_signals:
+                    # Value from decoded CAN signal
+                    raw_value = decoded_signals[signal_name]
+                    try:
+                        # Apply scaling/offset if needed (cantools handles this)
+                        # Round appropriately, especially for floats
+                        if isinstance(raw_value, (int, float)):
+                            # Be cautious with lat/lon precision before projection
+                            if "Latitude" in target_col or "Longitude" in target_col:
+                                record[target_col] = float(raw_value) # Keep precision for projection
+                            else:
+                                record[target_col] = round(float(raw_value), 6)
+                        else:
+                             record[target_col] = raw_value # Keep non-numeric as is (e.g., enums)
+                    except (ValueError, TypeError):
+                        record[target_col] = raw_value # Assign raw value if conversion fails
+                # If signal not found or source_info is None, leave it empty for now
+                # Will be filled later or during DataFrame processing
+
+            return record
+
+        except Exception as e:
+             print(f"Error creating unified record for row {row_dict.get('ID')}: {e}")
+             return None
+
+
+    def _extract_vehicle_data(self, df_raw: pd.DataFrame, player_id: int) -> pd.DataFrame:
+        """Extracts and renames columns for a specific vehicle (EGO or OBJ)."""
+        df_vehicle = pd.DataFrame()
+        df_vehicle["simTime"] = df_raw["simTime"]
+        df_vehicle["simFrame"] = df_raw["simFrame"]
+        df_vehicle["playerId"] = player_id
+
+        if player_id == PLAYER_ID_EGO:
+            # Select EGO columns (not ending in _obj) + relative columns
+            ego_cols = {target: source for target, source in self.table_config['can_table']['mapping'].items()
+                        if source and not isinstance(source, tuple) and not target.endswith('_obj')}
+            rename_map = {}
+            select_cols_raw = []
+            for target_col, source_info in ego_cols.items():
+                 if source_info: # Mapped signal/field name in df_raw
+                    select_cols_raw.append(target_col) # Column names in df_raw are already target names
+                    rename_map[target_col] = target_col # No rename needed here
+
+            # Include relative speed and distance for ego frame
+            relative_cols = ["composite_v", "relative_dist"]
+            select_cols_raw.extend(relative_cols)
+            for col in relative_cols:
+                 rename_map[col] = col
+
+            # Select and rename
+            df_vehicle_temp = df_raw[list(set(select_cols_raw) & set(df_raw.columns))] # Select available columns
+            df_vehicle = pd.concat([df_vehicle, df_vehicle_temp], axis=1)
+
+
+        elif player_id == PLAYER_ID_OBJ:
+            # Select OBJ columns (ending in _obj)
+            obj_cols = {target: source for target, source in self.table_config['can_table']['mapping'].items()
+                        if source and not isinstance(source, tuple) and target.endswith('_obj')}
+            rename_map = {}
+            select_cols_raw = []
+            for target_col, source_info in obj_cols.items():
+                if source_info:
+                     select_cols_raw.append(target_col) # Original _obj column name
+                     # Map from VUT_XXX_obj -> VUT_XXX
+                     rename_map[target_col] = self.OBJ_COLS_MAPPING.get(target_col, target_col) # Rename to standard name
+
+            # Select and rename
+            df_vehicle_temp = df_raw[list(set(select_cols_raw) & set(df_raw.columns))] # Select available columns
+            df_vehicle_temp.rename(columns=rename_map, inplace=True)
+            df_vehicle = pd.concat([df_vehicle, df_vehicle_temp], axis=1)
+
+            # Copy relative speed/distance from ego calculation (assuming it's relative *to* ego)
+            if "composite_v" in df_raw.columns:
+                df_vehicle["composite_v"] = df_raw["composite_v"]
+            if "relative_dist" in df_raw.columns:
+                df_vehicle["relative_dist"] = df_raw["relative_dist"]
+
+
+        # Drop rows where essential position data might be missing after selection/renaming
+        # Adjust required columns as necessary
+        required_pos = ['posX', 'posY', 'posH']
+        df_vehicle.dropna(subset=[col for col in required_pos if col in df_vehicle.columns], inplace=True)
+
+        return df_vehicle
+
+    def _project_coordinates(self, df: pd.DataFrame, lat_col: str, lon_col: str) -> pd.DataFrame:
+        """Applies UTM projection to latitude and longitude columns."""
+        if lat_col in df.columns and lon_col in df.columns:
+            # Ensure data is numeric and handle potential errors/missing values
+            lat = pd.to_numeric(df[lat_col], errors='coerce')
+            lon = pd.to_numeric(df[lon_col], errors='coerce')
+            valid_coords = lat.notna() & lon.notna()
+
+            if valid_coords.any():
+                x, y = self.projection(lon[valid_coords].values, lat[valid_coords].values)
+                # Update DataFrame, assign NaN where original coords were invalid
+                df.loc[valid_coords, lat_col] = np.round(x, 6) # Overwrite latitude col with X
+                df.loc[valid_coords, lon_col] = np.round(y, 6) # Overwrite longitude col with Y
+                df.loc[~valid_coords, [lat_col, lon_col]] = np.nan # Set invalid coords to NaN
+            else:
+                 # No valid coordinates found, set columns to NaN or handle as needed
+                 df[lat_col] = np.nan
+                 df[lon_col] = np.nan
+
+             # Rename columns AFTER projection for clarity
+            df.rename(columns={lat_col: 'posX', lon_col: 'posY'}, inplace=True)
+        else:
+            # Ensure columns exist even if projection didn't happen
+            if 'posX' not in df.columns: df['posX'] = np.nan
+            if 'posY' not in df.columns: df['posY'] = np.nan
+            print(f"Warning: Latitude ('{lat_col}') or Longitude ('{lon_col}') columns not found for projection.")
+
+        return df
+
+
+# --- Polynomial Fitting (Largely unchanged, minor cleanup) ---
+class PolynomialCurvatureFitting:
+    """Calculates curvature and its derivative using polynomial fitting."""
+    def __init__(self, lane_map_path: Path, degree: int = 3):
+        self.lane_map_path = lane_map_path
+        self.degree = degree
+        self.data = self._load_data()
+        if self.data is not None:
+            self.points = self.data[["centerLine_x", "centerLine_y"]].values
+            self.x_data, self.y_data = self.points[:, 0], self.points[:, 1]
+        else:
+            self.points = np.empty((0, 2))
+            self.x_data, self.y_data = np.array([]), np.array([])
+
+    def _load_data(self) -> Optional[pd.DataFrame]:
+        """Loads lane map data safely."""
+        if not self.lane_map_path.exists() or self.lane_map_path.stat().st_size == 0:
+            print(f"Warning: LaneMap file not found or empty: {self.lane_map_path}")
+            return None
+        try:
+            return pd.read_csv(self.lane_map_path)
+        except pd.errors.EmptyDataError:
+            print(f"Warning: LaneMap file is empty: {self.lane_map_path}")
+            return None
+        except Exception as e:
+            print(f"Error reading LaneMap file {self.lane_map_path}: {e}")
+            return None
+
+    def curvature(self, coefficients: np.ndarray, x: float) -> float:
+        """Computes curvature of the polynomial at x."""
+        if len(coefficients) < 3: # Need at least degree 2 for curvature
+             return 0.0
+        first_deriv_coeffs = np.polyder(coefficients)
+        second_deriv_coeffs = np.polyder(first_deriv_coeffs)
+        dy_dx = np.polyval(first_deriv_coeffs, x)
+        d2y_dx2 = np.polyval(second_deriv_coeffs, x)
+        denominator = (1 + dy_dx**2)**1.5
+        return np.abs(d2y_dx2) / denominator if denominator != 0 else np.inf
+
+    def curvature_derivative(self, coefficients: np.ndarray, x: float) -> float:
+        """Computes the derivative of curvature with respect to x."""
+        if len(coefficients) < 4: # Need at least degree 3 for derivative of curvature
+             return 0.0
+        first_deriv_coeffs = np.polyder(coefficients)
+        second_deriv_coeffs = np.polyder(first_deriv_coeffs)
+        third_deriv_coeffs = np.polyder(second_deriv_coeffs)
+
+        dy_dx = np.polyval(first_deriv_coeffs, x)
+        d2y_dx2 = np.polyval(second_deriv_coeffs, x)
+        d3y_dx3 = np.polyval(third_deriv_coeffs, x)
+
+        denominator = (1 + dy_dx**2)**2.5 # Note the power is 2.5 or 5/2
+        if denominator == 0:
+            return np.inf
+
+        numerator = d3y_dx3 * (1 + dy_dx**2) - 3 * dy_dx * d2y_dx2 * d2y_dx2 # Corrected term order? Verify formula
+        # Standard formula: (d3y_dx3*(1 + dy_dx**2) - 3*dy_dx*(d2y_dx2**2)) / ((1 + dy_dx**2)**(5/2)) * sign(d2y_dx2)
+        # Let's stick to the provided calculation logic but ensure denominator is correct
+        # The provided formula in the original code seems to be for dk/ds (arc length), not dk/dx.
+        # Re-implementing dk/dx based on standard calculus:
+        term1 = d3y_dx3 * (1 + dy_dx**2)**(3/2)
+        term2 = d2y_dx2 * (3/2) * (1 + dy_dx**2)**(1/2) * (2 * dy_dx * d2y_dx2) # Chain rule
+        numerator_dk_dx = term1 - term2
+        denominator_dk_dx = (1 + dy_dx**2)**3
+
+        if denominator_dk_dx == 0:
+             return np.inf
+
+        # Take absolute value or not? Original didn't. Let's omit abs() for derivative.
+        return numerator_dk_dx / denominator_dk_dx
+        # dk_dx = (d3y_dx3 * (1 + dy_dx ** 2) - 3 * dy_dx * d2y_dx2 ** 2) / (
+        #         (1 + dy_dx ** 2) ** (5/2) # Original had power 3 ?? Double check this formula source
+        # ) * np.sign(d2y_dx2) # Need sign of curvature
+        # return dk_dx
+
+    def polynomial_fit(
+        self, x_window: np.ndarray, y_window: np.ndarray
+    ) -> Tuple[Optional[np.ndarray], Optional[np.poly1d]]:
+        """Performs polynomial fitting, handling potential rank warnings."""
+        if len(x_window) <= self.degree:
+            print(f"Warning: Window size {len(x_window)} is <= degree {self.degree}. Cannot fit.")
+            return None, None
+        try:
+            # Use warnings context manager if needed, but RankWarning often indicates insufficient data variability
+            # with warnings.catch_warnings():
+            #     warnings.filterwarnings('error', category=np.RankWarning) # Or ignore
+            coefficients = np.polyfit(x_window, y_window, self.degree)
+            return coefficients, np.poly1d(coefficients)
+        except np.RankWarning:
+             print(f"Warning: Rank deficient fitting for window. Check data variability.")
+             # Attempt lower degree fit? Or return None? For now, return None.
+             # try:
+             #      coefficients = np.polyfit(x_window, y_window, len(x_window) - 1)
+             #      return coefficients, np.poly1d(coefficients)
+             # except:
+             return None, None
+        except Exception as e:
+             print(f"Error during polynomial fit: {e}")
+             return None, None
+
+
+    def find_best_window(self, point: Tuple[float, float], window_size: int) -> Optional[int]:
+        """Finds the start index of the window whose center is closest to the point."""
+        if len(self.x_data) < window_size:
+            print("Warning: Not enough data points for the specified window size.")
+            return None
+
+        x_point, y_point = point
+        min_dist_sq = np.inf
+        best_start_index = -1
+
+        # Calculate window centers more efficiently
+        # Use rolling mean if window_size is large, otherwise simple loop is fine
+        num_windows = len(self.x_data) - window_size + 1
+        if num_windows <= 0: return None
+
+        for start in range(num_windows):
+            x_center = np.mean(self.x_data[start : start + window_size])
+            y_center = np.mean(self.y_data[start : start + window_size])
+            dist_sq = (x_point - x_center)**2 + (y_point - y_center)**2
+            if dist_sq < min_dist_sq:
+                min_dist_sq = dist_sq
+                best_start_index = start
+
+        return best_start_index if best_start_index != -1 else None
+
+
+    def find_projection(
+        self,
+        x_target: float,
+        y_target: float,
+        polynomial: np.poly1d,
+        x_range: Tuple[float, float],
+        search_points: int = 100, # Number of points instead of step size
+    ) -> Optional[Tuple[float, float, float]]:
+        """Finds the approximate closest point on the polynomial within the x_range."""
+        if x_range[1] <= x_range[0]: return None # Invalid range
+
+        x_values = np.linspace(x_range[0], x_range[1], search_points)
+        y_values = polynomial(x_values)
+        distances_sq = (x_target - x_values)**2 + (y_target - y_values)**2
+
+        if len(distances_sq) == 0: return None
+
+        min_idx = np.argmin(distances_sq)
+        min_distance = np.sqrt(distances_sq[min_idx])
+
+        return x_values[min_idx], y_values[min_idx], min_distance
+
+    def fit_and_project(
+        self, points: np.ndarray, window_size: int
+    ) -> List[Dict[str, Any]]:
+        """Fits polynomial and calculates curvature for each point in the input array."""
+        if self.data is None or len(self.x_data) < window_size:
+            print("Insufficient LaneMap data for fitting.")
+            # Return default values for all points
+            return [
+                {
+                    "projection": (np.nan, np.nan),
+                    "curvHor": np.nan,
+                    "curvHorDot": np.nan,
+                    "laneOffset": np.nan,
+                }
+            ] * len(points)
+
+        results = []
+        if points.ndim != 2 or points.shape[1] != 2:
+             raise ValueError("Input points must be a 2D numpy array with shape (n, 2).")
+
+        for x_target, y_target in points:
+            result = { # Default result
+                "projection": (np.nan, np.nan),
+                "curvHor": np.nan,
+                "curvHorDot": np.nan,
+                "laneOffset": np.nan,
+            }
+
+            best_start = self.find_best_window((x_target, y_target), window_size)
+            if best_start is None:
+                results.append(result)
+                continue
+
+            x_window = self.x_data[best_start : best_start + window_size]
+            y_window = self.y_data[best_start : best_start + window_size]
+
+            coefficients, polynomial = self.polynomial_fit(x_window, y_window)
+            if coefficients is None or polynomial is None:
+                results.append(result)
+                continue
+
+            x_min, x_max = np.min(x_window), np.max(x_window)
+            projection_result = self.find_projection(
+                x_target, y_target, polynomial, (x_min, x_max)
+            )
+
+            if projection_result is None:
+                results.append(result)
+                continue
+
+            proj_x, proj_y, min_distance = projection_result
+
+            curv_hor = self.curvature(coefficients, proj_x)
+            curv_hor_dot = self.curvature_derivative(coefficients, proj_x)
+
+            result = {
+                "projection": (round(proj_x, 6), round(proj_y, 6)),
+                "curvHor": round(curv_hor, 6),
+                "curvHorDot": round(curv_hor_dot, 6),
+                "laneOffset": round(min_distance, 6),
+            }
+            results.append(result)
+
+        return results
+
+
+# --- Data Quality Analyzer (Optimized) ---
+class DataQualityAnalyzer:
+    """Analyzes data quality metrics, focusing on frame loss."""
+
+    def __init__(self, df: Optional[pd.DataFrame] = None):
+        self.df = df if df is not None and not df.empty else pd.DataFrame() # Ensure df is DataFrame
+
+    def analyze_frame_loss(self) -> Dict[str, Any]:
+        """Analyzes frame loss characteristics."""
+        metrics = {
+            "total_frames_data": 0,
+            "unique_frames_count": 0,
+            "min_frame": np.nan,
+            "max_frame": np.nan,
+            "expected_frames": 0,
+            "dropped_frames_count": 0,
+            "loss_rate": np.nan,
+            "max_consecutive_loss": 0,
+            "max_loss_start_frame": np.nan,
+            "max_loss_end_frame": np.nan,
+            "loss_intervals_distribution": {},
+            "valid": False, # Indicate if analysis was possible
+            "message": ""
+        }
+
+        if self.df.empty or 'simFrame' not in self.df.columns:
+            metrics["message"] = "DataFrame is empty or 'simFrame' column is missing."
+            return metrics
+
+        # Drop rows with NaN simFrame and ensure integer type
+        frames_series = self.df['simFrame'].dropna().astype(int)
+        metrics["total_frames_data"] = len(frames_series)
+
+        if frames_series.empty:
+            metrics["message"] = "No valid 'simFrame' data found after dropping NaN."
+            return metrics
+
+        unique_frames = sorted(frames_series.unique())
+        metrics["unique_frames_count"] = len(unique_frames)
+
+        if metrics["unique_frames_count"] < 2:
+            metrics["message"] = "Less than two unique frames; cannot analyze loss."
+            metrics["valid"] = True # Data exists, just not enough to analyze loss
+            if metrics["unique_frames_count"] == 1:
+                 metrics["min_frame"] = unique_frames[0]
+                 metrics["max_frame"] = unique_frames[0]
+                 metrics["expected_frames"] = 1
+            return metrics
+
+        metrics["min_frame"] = unique_frames[0]
+        metrics["max_frame"] = unique_frames[-1]
+        metrics["expected_frames"] = metrics["max_frame"] - metrics["min_frame"] + 1
+
+        # Calculate differences between consecutive unique frames
+        frame_diffs = np.diff(unique_frames)
+        # Gaps are where diff > 1. The number of lost frames in a gap is diff - 1.
+        gaps = frame_diffs[frame_diffs > 1]
+        lost_frames_in_gaps = gaps - 1
+
+        metrics["dropped_frames_count"] = int(lost_frames_in_gaps.sum())
+
+        if metrics["expected_frames"] > 0:
+            metrics["loss_rate"] = round(metrics["dropped_frames_count"] / metrics["expected_frames"], 4)
+        else:
+             metrics["loss_rate"] = 0.0 # Avoid division by zero if min_frame == max_frame (already handled)
+
+
+        if len(lost_frames_in_gaps) > 0:
+            metrics["max_consecutive_loss"] = int(lost_frames_in_gaps.max())
+            # Find where the max loss occurred
+            max_loss_indices = np.where(frame_diffs == metrics["max_consecutive_loss"] + 1)[0]
+            # Get the first occurrence start/end frames
+            max_loss_idx = max_loss_indices[0]
+            metrics["max_loss_start_frame"] = unique_frames[max_loss_idx]
+            metrics["max_loss_end_frame"] = unique_frames[max_loss_idx + 1]
+
+            # Count distribution of loss interval lengths
+            loss_counts = Counter(lost_frames_in_gaps)
+            metrics["loss_intervals_distribution"] = {int(k): int(v) for k, v in loss_counts.items()}
+        else:
+            metrics["max_consecutive_loss"] = 0
+
+        metrics["valid"] = True
+        metrics["message"] = "Frame loss analysis complete."
+        return metrics
+
+
+def get_all_csv_files(path: Path) -> List[Path]:
+    """Gets all CSV files in path, excluding specific ones."""
+    excluded_files = {OUTPUT_CSV_LANEMAP, ROADMARK_CSV}
+    return [
+        file_path
+        for file_path in path.rglob("*.csv") # Recursive search
+        if file_path.is_file() and file_path.name not in excluded_files
+    ]
+
+
+def run_frame_loss_analysis_on_folder(path: Path) -> Dict[str, Dict[str, Any]]:
+    """Runs frame loss analysis on all relevant CSV files in a folder."""
+    analysis_results = {}
+    csv_files = get_all_csv_files(path)
+
+    if not csv_files:
+        print(f"No relevant CSV files found in {path}")
+        return analysis_results
+
+    for file_path in csv_files:
+        file_name = file_path.name
+        if file_name in {OUTPUT_CSV_FUNCTION, OUTPUT_CSV_OBU}: # Skip specific files if needed
+            print(f"Skipping frame analysis for: {file_name}")
+            continue
+
+        print(f"Analyzing frame loss for: {file_name}")
+        if file_path.stat().st_size == 0:
+            print(f"File {file_name} is empty. Skipping analysis.")
+            analysis_results[file_name] = {"valid": False, "message": "File is empty."}
+            continue
+
+        try:
+            # Read only necessary column if possible, handle errors
+            df = pd.read_csv(file_path, usecols=['simFrame'], index_col=False,
+                             on_bad_lines='warn') # 'warn' or 'skip'
+            analyzer = DataQualityAnalyzer(df)
+            metrics = analyzer.analyze_frame_loss()
+            analysis_results[file_name] = metrics
+
+            # Optionally print a summary here
+            if metrics["valid"]:
+                 print(f"  Loss Rate: {metrics.get('loss_rate', np.nan)*100:.2f}%, "
+                       f"Dropped: {metrics.get('dropped_frames_count', 'N/A')}, "
+                       f"Max Gap: {metrics.get('max_consecutive_loss', 'N/A')}")
+            else:
+                 print(f"  Analysis failed: {metrics.get('message')}")
+
+
+        except pd.errors.EmptyDataError:
+            print(f"File {file_name} contains no data after reading.")
+            analysis_results[file_name] = {"valid": False, "message": "Empty data after read."}
+        except ValueError as ve: # Handle case where simFrame might not be present
+            print(f"ValueError processing file {file_name}: {ve}. Is 'simFrame' column present?")
+            analysis_results[file_name] = {"valid": False, "message": f"ValueError: {ve}"}
+        except Exception as e:
+            print(f"Unexpected error processing file {file_name}: {e}")
+            analysis_results[file_name] = {"valid": False, "message": f"Unexpected error: {e}"}
+
+    return analysis_results
+
+
+def data_precheck(output_dir: Path, max_allowed_loss_rate: float = 0.20) -> bool:
+    """Checks data quality, focusing on frame loss rate."""
+    print(f"--- Running Data Quality Precheck on: {output_dir} ---")
+    if not output_dir.exists() or not output_dir.is_dir():
+        print(f"Error: Output directory does not exist: {output_dir}")
+        return False
+
+    try:
+        frame_loss_results = run_frame_loss_analysis_on_folder(output_dir)
+    except Exception as e:
+        print(f"Critical error during frame loss analysis: {e}")
+        return False # Treat critical error as failure
+
+    if not frame_loss_results:
+         print("Warning: No files were analyzed for frame loss.")
+         # Decide if this is a failure or just a warning. Let's treat it as OK for now.
+         return True
+
+    all_checks_passed = True
+    for file_name, metrics in frame_loss_results.items():
+        if metrics.get("valid", False):
+            loss_rate = metrics.get("loss_rate", np.nan)
+            if pd.isna(loss_rate):
+                 print(f"  {file_name}: Loss rate could not be calculated.")
+                 # Decide if NaN loss rate is acceptable.
+            elif loss_rate > max_allowed_loss_rate:
+                print(f"  FAIL: {file_name} - Frame loss rate ({loss_rate*100:.2f}%) exceeds threshold ({max_allowed_loss_rate*100:.1f}%).")
+                all_checks_passed = False
+            else:
+                print(f"  PASS: {file_name} - Frame loss rate ({loss_rate*100:.2f}%) is acceptable.")
+        else:
+            print(f"  WARN: {file_name} - Frame loss analysis could not be completed ({metrics.get('message', 'Unknown reason')}).")
+            # Decide if inability to analyze is a failure. Let's allow it for now.
+
+    print(f"--- Data Quality Precheck {'PASSED' if all_checks_passed else 'FAILED'} ---")
+    return all_checks_passed
+
+
+# --- Final Preprocessing Step ---
+class FinalDataProcessor:
+    """Merges processed CSVs, adds curvature, and handles traffic lights."""
+
+    def __init__(self, config: Config):
+        self.config = config
+        self.output_dir = config.output_dir
+
+    def process(self) -> bool:
+        """执行最终数据合并和处理步骤。"""
+        print("--- Starting Final Data Processing ---")
+        try:
+            # 1. Load main object state data
+            obj_state_path = self.output_dir / OUTPUT_CSV_OBJSTATE
+            lane_map_path = self.output_dir / OUTPUT_CSV_LANEMAP
+
+            if not obj_state_path.exists():
+                print(f"Error: Required input file not found: {obj_state_path}")
+                return False
+                
+            # Load and process data
+            df_object = pd.read_csv(obj_state_path, dtype={"simTime": float}, low_memory=False)
+            
+            # Process and merge data
+            df_merged = self._merge_optional_data(df_object)
+            
+            # Save final merged file directly to output directory
+            merged_csv_path = self.output_dir / OUTPUT_CSV_MERGED
+            print(f'merged_csv_path:{merged_csv_path}')
+            df_merged.to_csv(merged_csv_path, index=False, float_format='%.6f')
+            print(f"Successfully created final merged file: {merged_csv_path}")
+            
+            # Clean up intermediate files
+            if obj_state_path.exists():
+                obj_state_path.unlink()
+            
+            print("--- Final Data Processing Finished ---")
+            return True
+            
+        except Exception as e:
+            print(f"An unexpected error occurred during final data processing: {e}")
+            import traceback
+            traceback.print_exc()
+            return False
+
+    def _merge_optional_data(self, df_object: pd.DataFrame) -> pd.DataFrame:
+        """加载和合并可选数据"""
+        df_merged = df_object.copy()
+
+        # --- 合并 EgoMap ---
+        egomap_path = self.output_dir / OUTPUT_CSV_EGOMAP
+        if egomap_path.exists() and egomap_path.stat().st_size > 0:
+            try:
+                df_ego = pd.read_csv(egomap_path, dtype={"simTime": float})
+                # 删除 simFrame 列，因为使用主数据的 simFrame
+                if 'simFrame' in df_ego.columns:
+                    df_ego = df_ego.drop(columns=['simFrame'])
+                
+                # 按时间和ID排序
+                df_ego.sort_values(['simTime', 'playerId'], inplace=True)
+                df_merged.sort_values(['simTime', 'playerId'], inplace=True)
+                
+                # 使用 merge_asof 进行就近合并，不包括 simFrame
+                df_merged = pd.merge_asof(
+                    df_merged,
+                    df_ego,
+                    on='simTime',
+                    by='playerId',
+                    direction='nearest',
+                    tolerance=0.01  # 10ms tolerance
+                )
+                print("EgoMap data merged.")
+            except Exception as e:
+                print(f"Warning: Could not merge EgoMap data from {egomap_path}: {e}")
+
+        # --- Merge Function ---
+        function_path = self.output_dir / OUTPUT_CSV_FUNCTION
+        if function_path.exists() and function_path.stat().st_size > 0:
+             try:
+                df_function = pd.read_csv(function_path, dtype={"timestamp": float}, low_memory=False).drop_duplicates()
+                # 删除 simFrame 列
+                if 'simFrame' in df_function.columns:
+                    df_function = df_function.drop(columns=['simFrame'])
+
+                if 'simTime' in df_function.columns:
+                    df_function['simTime'] = df_function['simTime'].round(2)
+
+                    common_cols = list(set(df_merged.columns) & set(df_function.columns) - {'simTime'})
+                    df_function.drop(columns=common_cols, inplace=True, errors='ignore')
+
+                    df_merged = pd.merge(df_merged, df_function, on=["simTime"], how="left")
+                    print("Function data merged.")
+                else:
+                    print("Warning: 'simTime' column not found in Function.csv. Cannot merge.")
+             except Exception as e:
+                print(f"Warning: Could not merge Function data from {function_path}: {e}")
+        else:
+            print("Function data not found or empty, skipping merge.")
+
+        # --- Merge OBU ---
+        obu_path = self.output_dir / OUTPUT_CSV_OBU
+        if obu_path.exists() and obu_path.stat().st_size > 0:
+             try:
+                df_obu = pd.read_csv(obu_path, dtype={"simTime": float}, low_memory=False).drop_duplicates()
+                # 删除 simFrame 列
+                if 'simFrame' in df_obu.columns:
+                    df_obu = df_obu.drop(columns=['simFrame'])
+
+                df_obu['simTime'] = df_obu['simTime'].round(2)
+
+                common_cols = list(set(df_merged.columns) & set(df_obu.columns) - {'simTime'})
+                df_obu.drop(columns=common_cols, inplace=True, errors='ignore')
+
+                df_merged = pd.merge(df_merged, df_obu, on=["simTime"], how="left")
+                print("OBU data merged.")
+             except Exception as e:
+                print(f"Warning: Could not merge OBU data from {obu_path}: {e}")
+        else:
+            print("OBU data not found or empty, skipping merge.")
+
+        return df_merged
+
+
+    def _process_trafficlight_data(self) -> pd.DataFrame:
+        """Processes traffic light JSON data if available."""
+        # Check if json_path is provided and exists
+        if not self.config.json_path:
+            print("No traffic light JSON file provided. Skipping traffic light processing.")
+            return pd.DataFrame()
+
+        if not self.config.json_path.exists():
+            print("Traffic light JSON file not found. Skipping traffic light processing.")
+            return pd.DataFrame()
+
+        print(f"Processing traffic light data from: {self.config.json_path}")
+        valid_trafficlights = []
+        try:
+            with open(self.config.json_path, 'r', encoding='utf-8') as f:
+                # Read the whole file, assuming it's a JSON array or JSON objects per line
+                try:
+                    # Attempt to read as a single JSON array
+                    raw_data = json.load(f)
+                    if not isinstance(raw_data, list):
+                         raw_data = [raw_data] # Handle case of single JSON object
+                except json.JSONDecodeError:
+                    # If fails, assume JSON objects per line
+                    f.seek(0) # Reset file pointer
+                    raw_data = [json.loads(line) for line in f if line.strip()]
+
+            for entry in raw_data:
+                # Normalize entry if it's a string containing JSON
+                if isinstance(entry, str):
+                    try:
+                        entry = json.loads(entry)
+                    except json.JSONDecodeError:
+                        print(f"Warning: Skipping invalid JSON string in traffic light data: {entry[:100]}...")
+                        continue
+
+                # Safely extract data using .get()
+                intersections = entry.get('intersections', [])
+                if not isinstance(intersections, list): continue # Skip if not a list
+
+                for intersection in intersections:
+                     if not isinstance(intersection, dict): continue
+                     timestamp_ms = intersection.get('intersectionTimestamp', 0)
+                     sim_time = round(int(timestamp_ms) / 1000, 2) # Convert ms to s and round
+
+                     phases = intersection.get('phases', [])
+                     if not isinstance(phases, list): continue
+
+                     for phase in phases:
+                         if not isinstance(phase, dict): continue
+                         phase_id = phase.get('phaseId', 0)
+
+                         phase_states = phase.get('phaseStates', [])
+                         if not isinstance(phase_states, list): continue
+
+                         for phase_state in phase_states:
+                             if not isinstance(phase_state, dict): continue
+                             # Check for startTime == 0 as per original logic
+                             if phase_state.get('startTime') == 0:
+                                 light_state = phase_state.get('light', 0) # Extract light state
+                                 data = {
+                                     'simTime': sim_time,
+                                     'phaseId': phase_id,
+                                     'stateMask': light_state,
+                                     # Add playerId for merging - assume applies to ego
+                                     'playerId': PLAYER_ID_EGO
+                                 }
+                                 valid_trafficlights.append(data)
+
+            if not valid_trafficlights:
+                print("No valid traffic light states (with startTime=0) found in JSON.")
+                return pd.DataFrame()
+
+            df_trafficlights = pd.DataFrame(valid_trafficlights)
+            # Drop duplicates based on relevant fields
+            df_trafficlights.drop_duplicates(subset=['simTime', 'playerId', 'phaseId', 'stateMask'], keep='first', inplace=True)
+            print(f"Processed {len(df_trafficlights)} unique traffic light state entries.")
+            return df_trafficlights
+
+        except json.JSONDecodeError as e:
+            print(f"Error decoding traffic light JSON file {self.config.json_path}: {e}")
+            return pd.DataFrame()
+        except Exception as e:
+            print(f"Unexpected error processing traffic light data: {e}")
+            return pd.DataFrame()
+
+
+# --- Rosbag Processing ---
+class RosbagProcessor:
+    """Extracts data from Rosbag files within a ZIP archive."""
+
+    # Mapping from filename parts to rostopics
+    ROSTOPIC_MAP = {
+        ('V2I', 'HazardousLocationW'): "/HazardousLocationWarning",
+        ('V2C', 'OtherVehicleRedLightViolationW'): "/c2v/GoThroughRadLight",
+        ('V2I', 'LeftTurnAssist'): "/LeftTurnAssistant",
+        ('V2V', 'LeftTurnAssist'): "/V2VLeftTurnAssistant",
+        ('V2I', 'RedLightViolationW'): "/SignalViolationWarning",
+        ('V2C', 'AbnormalVehicleW'): "/c2v/AbnormalVehicleWarnning",
+        ('V2C', 'SignalLightReminder'): "/c2v/TrafficLightInfo",
+        ('V2C', 'VulnerableRoadUserCollisionW'): "/c2v/VulnerableObject",
+        ('V2C', 'EmergencyVehiclesPriority'): "/c2v/EmergencyVehiclesPriority",
+        ('V2C', 'LitterW'): "/c2v/RoadSpillageWarning",
+        ('V2V', 'ForwardCollisionW'): "/V2VForwardCollisionWarning",
+        ('V2C', 'VisibilityW'): "/c2v/VisibilityWarinning",
+        ('V2V', 'EmergencyBrakeW'): "/V2VEmergencyBrakeWarning",
+        ('V2I', 'GreenLightOptimalSpeedAdvisory'): "/GreenLightOptimalSpeedAdvisory", # Check exact topic name
+        ('V2C', 'DynamicSpeedLimitingInformation'): "/c2v/DynamicSpeedLimit",
+        ('V2C', 'TrafficJamW'): "/c2v/TrafficJam",
+        ('V2C', 'DrivingLaneRecommendation'): "/c2v/LaneGuidance",
+        ('V2C', 'RampMerge'): "/c2v/RampMerging",
+        ('V2I', 'CooperativeIntersectionPassing'): "/CooperativeIntersectionPassing",
+        ('V2I', 'IntersectionCollisionW'): "/IntersectionCollisionWarning",
+        ('V2V', 'IntersectionCollisionW'): "/V2VIntersectionCollisionWarning",
+        ('V2V', 'BlindSpotW'): "/V2VBlindSpotWarning",
+        ('V2I', 'SpeedLimitW'): "/SpeedLimit",
+        ('V2I', 'VulnerableRoadUserCollisionW'): "/VulnerableRoadUserCollisionWarning",
+        ('V2I', 'CooperativeLaneChange'): "/CooperativeLaneChange",
+        ('V2V', 'CooperativeLaneChange'): "/V2VCooperativeLaneChange",
+        ('V2I', 'CooperativeVehicleMerge'): "/CooperativeVehicleMerge",
+        ('V2V', 'AbnormalVehicleW'): "/V2VAbnormalVehicleWarning",
+        ('V2V', 'ControlLossW'): "/V2VVehicleLossControlWarning",
+        ('V2V', 'EmergencyVehicleW'): '/V2VEmergencyVehicleWarning',
+        ('V2I', 'InVehicleSignage'): "/InVehicleSign",
+        ('V2V', 'DoNotPassW'): "/V2VDoNotPassWarning",
+        ('V2I', 'TrafficJamW'): "/TrafficJamWarning",
+        # Add more mappings as needed
+    }
+
+    def __init__(self, config: Config):
+        self.config = config
+        self.output_dir = config.output_dir
+
+    def _get_target_rostopic(self, zip_filename: str) -> Optional[str]:
+        """Determines the target rostopic based on keywords in the filename."""
+        for (kw1, kw2), topic in self.ROSTOPIC_MAP.items():
+            if kw1 in zip_filename and kw2 in zip_filename:
+                print(f"Identified target topic '{topic}' for {zip_filename}")
+                return topic
+        print(f"Warning: No specific rostopic mapping found for {zip_filename}.")
+        return None
+
+    def process_zip_for_rosbags(self) -> None:
+        """Finds, extracts, and processes rosbags from the ZIP file."""
+        print(f"--- Processing Rosbags in {self.config.zip_path} ---")
+        target_rostopic = self._get_target_rostopic(self.config.zip_path.stem)
+        if not target_rostopic:
+            print("Skipping Rosbag processing as no target topic was identified.")
+            return
+
+        with tempfile.TemporaryDirectory() as tmp_dir_str:
+            tmp_dir = Path(tmp_dir_str)
+            bag_files_extracted = []
+            try:
+                with zipfile.ZipFile(self.config.zip_path, 'r') as zip_ref:
+                    for member in zip_ref.infolist():
+                        # Extract Rosbag files
+                        if 'Rosbag/' in member.filename and member.filename.endswith('.bag'):
+                            try:
+                                extracted_path = Path(zip_ref.extract(member, path=tmp_dir))
+                                bag_files_extracted.append(extracted_path)
+                                print(f"Extracted Rosbag: {extracted_path.name}")
+                            except Exception as e:
+                                print(f"Error extracting Rosbag {member.filename}: {e}")
+
+                        # Extract HMIdata CSV files directly to output
+                        elif 'HMIdata/' in member.filename and member.filename.endswith('.csv'):
+                             try:
+                                 target_path = self.output_dir / Path(member.filename).name
+                                 with zip_ref.open(member) as source, open(target_path, "wb") as target:
+                                     shutil.copyfileobj(source, target)
+                                 print(f"Extracted HMI data: {target_path.name}")
+                             except Exception as e:
+                                 print(f"Error extracting HMI data {member.filename}: {e}")
+
+            except zipfile.BadZipFile:
+                print(f"Error: Bad ZIP file provided: {self.config.zip_path}")
+                return
+            except FileNotFoundError:
+                 print(f"Error: ZIP file not found: {self.config.zip_path}")
+                 return
+
+            if not bag_files_extracted:
+                 print("No Rosbag files found in the archive.")
+                 # Attempt extraction of HMI/RDB anyway if needed (already done above)
+                 return
+
+            # Process extracted bag files
+            for bag_path in bag_files_extracted:
+                print(f"Processing bag file: {bag_path.name}")
+                self._convert_bag_topic_to_csv(bag_path, target_rostopic)
+
+        print("--- Rosbag Processing Finished ---")
+
+    def _convert_bag_topic_to_csv(self, bag_file_path: Path, target_topic: str) -> None:
+        """Converts a specific topic from a single bag file to CSV."""
+        output_csv_path = self.output_dir / OUTPUT_CSV_OBU # Standard name for OBU data
+
+        try:
+            # Check if bagpy can handle Path object, else convert to str
+            bag_reader = bagreader(str(bag_file_path), verbose=False)
+
+            # Check if topic exists
+            available_topics = bag_reader.topic_table['Topics'].tolist() if hasattr(bag_reader, 'topic_table') and bag_reader.topic_table is not None else []
+            if target_topic not in available_topics:
+                print(f"Target topic '{target_topic}' not found in {bag_file_path.name}. Available: {available_topics}")
+                # Clean up temporary bagpy-generated files if possible
+                if hasattr(bag_reader, 'data_folder') and Path(bag_reader.data_folder).exists():
+                     shutil.rmtree(bag_reader.data_folder, ignore_errors=True)
+                return
+
+            # Extract message data to a temporary CSV created by bagpy
+            temp_csv_path_str = bag_reader.message_by_topic(target_topic)
+            temp_csv_path = Path(temp_csv_path_str)
+
+
+            if not temp_csv_path.exists() or temp_csv_path.stat().st_size == 0:
+                print(f"Warning: Bagpy generated an empty or non-existent CSV for topic '{target_topic}' from {bag_file_path.name}.")
+                return # Skip if empty
+
+            # Read the temporary CSV, process, and save to final location
+            df = pd.read_csv(temp_csv_path)
+
+            if df.empty:
+                print(f"Warning: Bagpy CSV for topic '{target_topic}' is empty after reading.")
+                return
+
+            # Clean columns: Drop 'Time', rename '*timestamp' -> 'simTime'
+            if 'Time' in df.columns:
+                df.drop(columns=['Time'], inplace=True)
+
+            rename_dict = {}
+            for col in df.columns:
+                if col.endswith('.timestamp'): # More specific match
+                    rename_dict[col] = 'simTime'
+                elif col.endswith('event_type'): # As per original code
+                    rename_dict[col] = 'event_Type'
+                # Add other renames if necessary
+
+            df.rename(columns=rename_dict, inplace=True)
+
+            # Ensure simTime is float and rounded (optional, do if needed for merging)
+            if 'simTime' in df.columns:
+                df['simTime'] = pd.to_numeric(df['simTime'], errors='coerce').round(2) # Example rounding
+
+            # Save processed data
+            df.to_csv(output_csv_path, index=False, float_format='%.6f')
+            print(f"Saved processed OBU data to: {output_csv_path}")
+
+        except ValueError as ve:
+             # Catch potential Bagpy internal errors if topic doesn't contain messages
+             print(f"ValueError processing bag {bag_file_path.name} (Topic: {target_topic}): {ve}. Topic might be empty.")
+        except ImportError as ie:
+             print(f"ImportError during bag processing: {ie}. Ensure all ROS dependencies are installed if needed by bagpy.")
+        except Exception as e:
+            print(f"Error processing bag file {bag_file_path.name} (Topic: {target_topic}): {e}")
+            import traceback
+            traceback.print_exc() # More details on unexpected errors
+        finally:
+            # Clean up temporary files/folders created by bagpy
+             if 'temp_csv_path' in locals() and temp_csv_path.exists():
+                 try:
+                    temp_csv_path.unlink() # Delete the specific CSV
+                 except OSError as ose:
+                      print(f"Warning: Could not delete bagpy temp csv {temp_csv_path}: {ose}")
+
+             if 'bag_reader' in locals() and hasattr(bag_reader, 'data_folder'):
+                 bagpy_folder = Path(bag_reader.data_folder)
+                 if bagpy_folder.exists() and bagpy_folder.is_dir():
+                     try:
+                        shutil.rmtree(bagpy_folder, ignore_errors=True) # Delete the folder bagpy made
+                     except OSError as ose:
+                          print(f"Warning: Could not delete bagpy temp folder {bagpy_folder}: {ose}")
+
+
+# --- Utility Functions ---
+def get_base_path() -> Path:
+    """Gets the base path of the script or executable."""
+    if getattr(sys, 'frozen', False) and hasattr(sys, '_MEIPASS'):
+        # Running in a PyInstaller bundle
+        return Path(sys._MEIPASS)
+    else:
+        # Running as a normal script
+        return Path(__file__).parent.resolve()
+
+
+def run_cpp_engine(config: Config):
+    """Runs the external C++ preprocessing engine."""
+    if not config.engine_path or not config.map_path:
+        print("C++ engine path or map path not configured. Skipping C++ engine execution.")
+        return True # Return True assuming it's optional or handled elsewhere
+
+    engine_cmd = [
+        str(config.engine_path),
+        str(config.map_path),
+        str(config.output_dir),
+        str(config.x_offset),
+        str(config.y_offset)
+    ]
+
+    print(f"--- Running C++ Preprocessing Engine ---")
+    print(f"Command: {' '.join(engine_cmd)}")
+
+    try:
+        result = subprocess.run(
+            engine_cmd,
+            check=True,          # Raise exception on non-zero exit code
+            capture_output=True, # Capture stdout/stderr
+            text=True,           # Decode output as text
+            cwd=config.engine_path.parent # Run from the engine's directory? Or script's? Adjust if needed.
+        )
+        print("C++ Engine Output:")
+        print(result.stdout)
+        if result.stderr:
+            print("C++ Engine Error Output:")
+            print(result.stderr)
+        print("--- C++ Engine Finished Successfully ---")
+        return True
+    except FileNotFoundError:
+         print(f"Error: C++ engine executable not found at {config.engine_path}.")
+         return False
+    except subprocess.CalledProcessError as e:
+        print(f"Error: C++ engine failed with exit code {e.returncode}.")
+        print("C++ Engine Output (stdout):")
+        print(e.stdout)
+        print("C++ Engine Output (stderr):")
+        print(e.stderr)
+        return False
+    except Exception as e:
+        print(f"An unexpected error occurred while running the C++ engine: {e}")
+        return False
+
+
+if __name__ == "__main__":
+    pass

core/processors/built_in/pgvil.py

@@ -0,0 +1,274 @@
+from pathlib import Path
+from typing import Dict, Any, Optional
+import pandas as pd
+import numpy as np
+from pyproj import Proj
+
+from core.processors.built_in.base import BaseDataProcessor
+
+class PGVILDataProcessor(BaseDataProcessor):
+    """处理仿真内置数据的处理器"""
+    
+    def __init__(self):
+        super().__init__("pgvil_processor")
+        self.required_columns = {
+            'simTime': float,
+            'simFrame': int,
+            'playerId': int,
+            'v': float,
+            'speedX': float,
+            'speedY': float,
+            'posH': float,
+            'speedH': float,
+            'posX': float,
+            'posY': float,
+            'accelX': float,
+            'accelY': float
+        }
+        # 初始化UTM投影
+        self.projection = Proj(proj='utm', zone=51, ellps='WGS84', preserve_units='m')
+        
+    def process_data(self, 
+                    input_path: Path,
+                    output_path: Path,
+                    **kwargs) -> Optional[Path]:
+        """处理PGVIL数据
+        
+        Args:
+            input_path: 输入文件路径
+            output_path: 输出目录路径
+            **kwargs: 额外参数
+                - utm_zone: UTM区域
+                - x_offset: X偏移
+                - y_offset: Y偏移
+        
+        Returns:
+            处理后的文件路径
+        """
+        try:
+            # 读取数据
+            df = pd.read_csv(input_path)
+            if df.empty:
+                print(f"输入文件为空: {input_path}")
+                return None
+                
+            # 基本数据清理
+            df = self._clean_data(df)
+            
+            # 坐标转换
+            utm_zone = kwargs.get('utm_zone', 51)
+            x_offset = kwargs.get('x_offset', 0.0)
+            y_offset = kwargs.get('y_offset', 0.0)
+            
+            df = self._process_coordinates(df, utm_zone, x_offset, y_offset)
+            
+            # 计算额外字段
+            df = self._calculate_additional_fields(df)
+            
+            # 确保输出目录存在
+            output_path.parent.mkdir(parents=True, exist_ok=True)
+            
+            # 保存处理后的数据
+            df.to_csv(output_path, index=False)
+            print(f"数据处理完成，已保存至: {output_path}")
+            
+            return output_path
+            
+        except Exception as e:
+            print(f"处理PGVIL数据时出错: {e}")
+            import traceback
+            traceback.print_exc()
+            return None
+    
+    def _clean_data(self, df: pd.DataFrame) -> pd.DataFrame:
+        """清理数据"""
+        # 删除重复行
+        df = df.drop_duplicates()
+        
+        # 确保所需列存在
+        for col, dtype in self.required_columns.items():
+            if col not in df.columns:
+                df[col] = 0 if dtype in (int, float) else ''
+            else:
+                df[col] = df[col].astype(dtype)
+        
+        # 处理空值
+        numeric_cols = [col for col, dtype in self.required_columns.items() 
+                       if dtype in (int, float)]
+        df[numeric_cols] = df[numeric_cols].fillna(0)
+        
+        # 按时间和ID排序
+        df.sort_values(['simTime', 'simFrame', 'playerId'], inplace=True)
+        
+        return df
+    
+    def _process_coordinates(self, 
+                           df: pd.DataFrame, 
+                           utm_zone: int,
+                           x_offset: float,
+                           y_offset: float) -> pd.DataFrame:
+        """处理坐标数据"""
+        if 'lat' in df.columns and 'lon' in df.columns:
+            # 原始经纬度转UTM
+            projection = Proj(proj='utm', zone=utm_zone, ellps='WGS84', preserve_units='m')
+            x, y = projection(df['lon'].values, df['lat'].values)
+            
+            # 应用偏移
+            df['posX'] = x + x_offset
+            df['posY'] = y + y_offset
+        
+        return df
+    
+    def _calculate_additional_fields(self, df: pd.DataFrame) -> pd.DataFrame:
+        """计算额外的字段"""
+        # 示例：计算合成速度
+        if all(col in df.columns for col in ['speedX', 'speedY']):
+            df['v'] = np.sqrt(df['speedX']**2 + df['speedY']**2)
+        
+        # 示例：计算行驶距离
+        if 'v' in df.columns and 'simTime' in df.columns:
+            df['travelDist'] = df.sort_values('simTime').groupby('playerId')['v'].cumsum() * df['simTime'].diff()
+        
+        return df
+    
+    def validate_output(self, output_path: Path) -> bool:
+        """验证输出数据
+        
+        Args:
+            output_path: 输出文件路径
+            
+        Returns:
+            验证是否通过
+        """
+        try:
+            if not output_path.exists():
+                print(f"输出文件不存在: {output_path}")
+                return False
+                
+            df = pd.read_csv(output_path)
+            
+            # 验证所需列
+            missing_cols = [col for col in self.required_columns.keys() 
+                          if col not in df.columns]
+            if missing_cols:
+                print(f"缺少必需列: {missing_cols}")
+                return False
+            
+            # 验证数据类型
+            for col, dtype in self.required_columns.items():
+                if df[col].dtype != dtype:
+                    print(f"列 {col} 的数据类型错误，应为 {dtype}")
+                    return False
+            
+            # 验证数值范围
+            if df['simTime'].min() < 0:
+                print("simTime存在负值")
+                return False
+            
+            if df['simFrame'].min() <= 0:
+                print("simFrame存在非正值")
+                return False
+            
+            return True
+            
+        except Exception as e:
+            print(f"验证输出数据时出错: {e}")
+            return False
+
+from typing import Dict, Optional
+from pathlib import Path
+import pandas as pd
+
+from .base import BaseProcessor
+from core.error_handler import ErrorHandler
+from core.config_manager import get_config
+
+class Config:
+    """PGVIL处理器配置类"""
+    def __init__(self,
+                output_dir: Path,
+                data_dir: Path):
+        self.output_dir = output_dir
+        self.data_dir = data_dir
+
+class PGVILProcessor(BaseProcessor):
+    """PGVIL数据处理器，实现PGVIL特有的处理逻辑"""
+    
+    def __init__(self, config: Config):
+        super().__init__(config.output_dir)
+        self.config = config
+        
+    @ErrorHandler.measure_performance
+    def process_built_in_data(self) -> Dict[str, Path]:
+        """实现PGVIL特有的内置数据处理逻辑
+        
+        处理顺序：
+        1. 处理CAN数据
+        2. 处理传感器数据
+        3. 处理其他PGVIL特有数据
+        4. 合并内置数据
+        
+        Returns:
+            处理结果文件路径字典
+        """
+        result_files = {}
+        
+        # 1. 处理CAN数据
+        print("1. 处理CAN数据...")
+        can_results = self._process_can_data()
+        if can_results:
+            result_files.update(can_results)
+        else:
+            print("警告: CAN数据处理失败或无数据")
+            
+        # 2. 处理传感器数据
+        print("\n2. 处理传感器数据...")
+        sensor_results = self._process_sensor_data()
+        if sensor_results:
+            result_files.update(sensor_results)
+        else:
+            print("警告: 传感器数据处理失败或无数据")
+            
+        # 3. 处理其他PGVIL特有数据
+        print("\n3. 处理其他PGVIL数据...")
+        other_results = self._process_other_data()
+        if other_results:
+            result_files.update(other_results)
+            
+        # 4. 合并内置数据
+        print("\n4. 合并内置数据...")
+        if not self._merge_built_in_data(result_files):
+            print("警告: 内置数据合并失败")
+            
+        return result_files
+        
+    def _process_can_data(self) -> Dict[str, Path]:
+        """处理CAN数据"""
+        # TODO: 实现CAN数据处理逻辑
+        return {}
+        
+    def _process_sensor_data(self) -> Dict[str, Path]:
+        """处理传感器数据"""
+        # TODO: 实现传感器数据处理逻辑
+        return {}
+        
+    def _process_other_data(self) -> Dict[str, Path]:
+        """处理其他PGVIL特有数据"""
+        # TODO: 实现其他数据处理逻辑
+        return {}
+        
+    def _merge_built_in_data(self, result_files: Dict[str, Path]) -> bool:
+        """合并PGVIL内置数据
+        
+        Args:
+            result_files: 处理结果文件路径字典
+            
+        Returns:
+            合并是否成功
+        """
+        try:
+            # 实现PGVIL特有的数据合并逻辑
+            return True
+        except Exception as e:
+            print(f"内置数据合并失败: {e}")
+            return False

core/resource_manager.py

@@ -0,0 +1,221 @@
+import zipfile
+from pathlib import Path
+import pandas as pd
+from typing import List, Optional
+import shutil
+
+class ResourceManager:
+    """管理插件资源和数据验证"""
+    
+    # 内置处理器类型及其对应的关键词
+    BUILT_IN_PROCESSORS = {
+        "lst": ["rosbag", "gnss", "can", "hmi"],
+        "pgvil": ["pgvil", "acu", "radar"]  # pgvil处理器支持的数据类型
+    }
+    
+    def __init__(self, resources_dir: Path):
+        self.resources_dir = resources_dir
+        if not self.resources_dir.exists():
+            self.resources_dir.mkdir(parents=True)
+            
+    def list_zip_folders(self, zip_path: Path, processor_type: str = "lst") -> List[str]:
+        """列出ZIP文件中的顶层文件夹，排除内置处理器的关键词文件夹
+        
+        Args:
+            zip_path: ZIP文件路径
+            processor_type: 内置处理器类型，可选 "lst" 或 "pgvil"
+            
+        Returns:
+            不包含内置处理器关键词的文件夹列表
+        """
+        folders = set()
+        built_in_keywords = self.BUILT_IN_PROCESSORS.get(processor_type, [])
+        
+        try:
+            with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+                for name in zip_ref.namelist():
+                    parts = Path(name).parts
+                    if len(parts) > 1:  # 至少包含一个文件夹
+                        folder = parts[0].lower()
+                        # 只返回不包含内置处理器关键词的文件夹
+                        if not any(keyword in folder for keyword in built_in_keywords):
+                            folders.add(parts[0])
+                            
+        except Exception as e:
+            print(f"读取ZIP文件出错: {e}")
+            return []
+            
+        return list(folders)
+        
+    def list_rosbag_files(self, zip_path: Path) -> List[str]:
+        """列出ZIP文件中的所有Rosbag文件"""
+        rosbag_files = set()
+        try:
+            with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+                for name in zip_ref.namelist():
+                    if 'Rosbag/' in name and name.endswith('.bag'):
+                        rosbag_files.add(name)
+        except Exception as e:
+            print(f"读取ZIP文件中的Rosbag失败: {e}")
+            return []
+        return list(rosbag_files)
+
+    def is_rosbag_file(self, zip_path: Path) -> bool:
+        """检查ZIP文件中是否包含Rosbag数据"""
+        try:
+            with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+                # 查找任何包含'rosbag'关键词且扩展名为.bag的文件
+                for name in zip_ref.namelist():
+                    if 'rosbag' in name.lower() and name.endswith('.bag'):
+                        return True
+            return False
+            
+        except Exception as e:
+            print(f"检查Rosbag文件失败: {e}")
+            return False
+
+    def validate_rosbag_output(self, output_path: Path) -> bool:
+        """验证Rosbag处理后的输出文件是否有效"""
+        try:
+            if not output_path.exists():
+                print(f"错误：输出文件不存在: {output_path}")
+                return False
+
+            df = pd.read_csv(output_path)
+            
+            # Rosbag数据必需列
+            required_columns = ['simTime', 'event_Type']
+            
+            # 检查必需列
+            missing_cols = [col for col in required_columns if col not in df.columns]
+            if missing_cols:
+                print(f"错误：缺少必需列: {missing_cols}")
+                return False
+
+            # 检查simTime列的有效性
+            if df['simTime'].isna().any():
+                print("错误：simTime列包含空值")
+                return False
+
+            return True
+            
+        except Exception as e:
+            print(f"验证Rosbag输出时出错: {e}")
+            return False
+            
+    def validate_plugin_output(self, output_path: Path) -> bool:
+        """验证插件输出文件是否有效"""
+        try:
+            if not output_path.exists():
+                print(f"错误：输出文件不存在: {output_path}")
+                return False
+
+            df = pd.read_csv(output_path)
+            required_columns = ['simTime', 'playerId', 'simFrame']
+            
+            # 检查必需列是否存在
+            missing_cols = [col for col in required_columns if col not in df.columns]
+            if missing_cols:
+                print(f"错误：缺少必需列: {missing_cols}")
+                return False
+                
+            # 检查空值
+            for col in required_columns:
+                if df[col].isna().any():
+                    print(f"错误：{col} 包含空值")
+                    return False
+            
+            return True
+            
+        except Exception as e:
+            print(f"验证输出文件时出错: {e}")
+            return False
+
+    def validate_plugin_df(self, df: pd.DataFrame) -> bool:
+        """验证插件输出的DataFrame是否符合要求"""
+        try:
+            required_columns = ['simTime', 'simFrame', 'playerId']
+            
+            missing_cols = [col for col in required_columns if col not in df.columns]
+            if missing_cols:
+                print(f"错误：缺少必需列: {missing_cols}")
+                return False
+                
+            if df['playerId'].isna().any():
+                print("错误：playerId 包含空值")
+                return False
+            if df['simTime'].isna().any():
+                print("错误：simTime 包含空值")
+                return False
+            if df['simFrame'].isna().any():
+                print("错误：simFrame 包含空值")
+                return False
+                
+            return True
+            
+        except Exception as e:
+            print(f"验证DataFrame时出错: {e}")
+            return False
+            
+    def merge_plugin_data(self, main_file: Path, plugin_file: Path, output_file: Path) -> bool:
+        try:
+            df_main = pd.read_csv(main_file)
+            df_plugin = pd.read_csv(plugin_file)
+            
+            print(f"主数据形状: {df_main.shape}")
+            print(f"插件数据形状: {df_plugin.shape}")
+            
+            # 只保留需要的列进行合并
+            merge_columns = ['simTime', 'playerId']
+            
+            # 确保时间戳精度匹配
+            df_main['simTime'] = df_main['simTime'].round(3)
+            df_plugin['simTime'] = df_plugin['simTime'].round(3)
+            
+            # 按时间排序
+            df_main.sort_values(['simTime', 'playerId'], inplace=True)
+            df_plugin.sort_values(['simTime', 'playerId'], inplace=True)
+
+            # 使用 merge_asof 进行基于时间的合并，只使用 simTime 和 playerId
+            df_merged = pd.merge_asof(
+                df_main,
+                df_plugin.drop('simFrame', axis=1, errors='ignore'),  # 删除插件数据中的 simFrame
+                on='simTime',
+                by=['playerId'],
+                direction='nearest',
+                tolerance=0.01  # 100ms的容差
+            )
+            
+            print(f"合并后的数据形状: {df_merged.shape}")
+            print(f"从插件添加的列: {[col for col in df_plugin.columns if col not in merge_columns and col != 'simFrame']}")
+            
+            df_merged.to_csv(output_file, index=False)
+            print(f"成功合并数据到: {output_file}")
+            
+            return True
+            
+        except Exception as e:
+            print(f"合并插件数据时出错: {e}")
+            import traceback
+            traceback.print_exc()
+            return False
+            
+    def copy_resource(self, resource_name: str, target_dir: Path) -> Optional[Path]:
+        """复制资源文件到目标目录"""
+        source_path = self.resources_dir / resource_name
+        if not source_path.exists():
+            return None
+            
+        try:
+            # 创建目标目录（如果不存在）
+            target_dir.mkdir(parents=True, exist_ok=True)
+            
+            # 复制文件
+            target_path = target_dir / resource_name
+            shutil.copy2(source_path, target_path)
+            
+            return target_path
+            
+        except Exception as e:
+            print(f"Error copying resource {resource_name}: {e}")
+            return None

docs/can_plugin_guide.md

@@ -0,0 +1,153 @@
+# CAN数据处理插件开发指南
+
+## 1. 概述
+本指南将帮助您创建自定义的CAN数据处理插件，以处理特定格式的CAN数据并将其集成到数据处理流程中。
+
+## 2. 快速开始
+
+### 2.1 准备工作
+1. 将您的DBC文件放在 `resources` 目录下
+2. 复制 `plugins/can_plugin_template.py` 为新文件，例如 `my_can_plugin.py`
+
+### 2.2 基本配置
+在您的插件文件中修改以下内容：
+
+```python
+class MyCanProcessor(CustomDataProcessorPlugin):  # 修改类名
+    def __init__(self):
+        self.dbc = None
+        self.custom_dbc_path = Path("resources/my_custom.dbc")  # 修改为您的DBC文件路径
+        self._load_dbc()
+```
+
+### 2.3 数据文件夹识别
+修改 `can_handle` 方法以匹配您的数据文件夹命名规则：
+
+```python
+def can_handle(self, zip_path: Path, folder_name: str) -> bool:
+    return folder_name.startswith('my_can_data_')  # 修改为您的文件夹前缀
+```
+
+### 2.4 定义输出列
+在 `get_required_columns` 方法中定义您需要的输出列：
+
+```python
+def get_required_columns(self) -> Dict[str, Any]:
+    return {
+        'simTime': float,      # 必需列
+        'playerId': int,       # 必需列
+        'my_signal_1': float,  # 自定义列
+        'my_signal_2': float,  # 自定义列
+    }
+```
+
+## 3. 数据处理配置
+
+### 3.1 DBC文件要求
+- DBC文件必须包含完整的CAN消息和信号定义
+- 确保信号有正确的比例因子和偏移量
+- 建议包含信号的单位信息
+
+### 3.2 信号映射示例
+在 `_process_can_data` 方法中映射CAN信号到输出列：
+
+```python
+record = {
+    'simTime': timestamp,
+    'playerId': 1,
+    'my_signal_1': decoded.get('CAN_Signal_Name_1', 0.0),
+    'my_signal_2': decoded.get('CAN_Signal_Name_2', 0.0),
+}
+```
+
+## 4. 输入数据格式要求
+
+### 4.1 ZIP文件结构
+```
+your_data.zip/
+    my_can_data_folder/      # 您的CAN数据文件夹
+        can_data_1.csv       # CAN数据文件
+        can_data_2.csv
+        ...
+```
+
+### 4.2 CAN数据文件格式
+CSV文件必须包含以下列：
+- `timestamp`: 时间戳 (秒)
+- `can_id`: CAN消息ID (十六进制或十进制)
+- `data`: CAN数据 (十六进制字符串)
+
+示例：
+```csv
+timestamp,can_id,data
+1.234,0x123,0123456789ABCDEF
+1.235,0x456,FEDCBA9876543210
+```
+
+## 5. 调试技巧
+
+### 5.1 验证DBC文件加载
+运行插件时会自动打印可用的CAN消息和信号列表，确保：
+- 所有预期的消息ID都列出
+- 信号名称正确
+
+### 5.2 数据处理验证
+1. 使用小数据集进行测试
+2. 检查输出CSV文件中的数据类型和值范围
+3. 确认时间戳的连续性和合理性
+
+### 5.3 常见问题解决
+1. DBC文件未找到
+   - 检查文件路径是否正确
+   - 确认文件在resources目录下
+
+2. 信号解码失败
+   - 验证CAN ID是否匹配
+   - 检查数据长度是否符合DBC定义
+
+3. 数据类型错误
+   - 确保所有输出列都有正确的数据类型转换
+   - 检查是否处理了所有可能的空值情况
+
+## 6. 示例
+
+### 6.1 完整的插件示例
+参考 `plugins/can_plugin_template.py` 中的示例实现。
+
+### 6.2 自定义数据处理示例
+```python
+# 添加自定义数据处理逻辑
+def _process_can_data(self, can_file: Path) -> pd.DataFrame:
+    df = pd.read_csv(can_file)
+    processed_data = []
+    
+    for _, row in df.iterrows():
+        decoded = self._decode_can_message(row['can_id'], bytes.fromhex(row['data']))
+        if decoded:
+            # 添加您的自定义处理逻辑
+            speed = decoded.get('Vehicle_Speed', 0) * 3.6  # 转换为km/h
+            record = {
+                'simTime': row['timestamp'],
+                'playerId': 1,
+                'vehicle_speed': speed
+            }
+            processed_data.append(record)
+            
+    return pd.DataFrame(processed_data)
+```
+
+## 7. 注意事项
+
+1. 必需字段
+   - 确保输出数据包含 `simTime` 和 `playerId`
+   - 这些字段用于数据合并和同步
+
+2. 数据质量
+   - 处理异常值和无效数据
+   - 确保时间戳的单调递增
+   - 合理处理丢失的数据点
+
+3. 性能优化
+   - 考虑批量处理大量数据
+   - 使用适当的数据结构减少内存使用
+   - 添加进度提示以监控长时间运行的处理

docs/optimization_plan.md

@@ -0,0 +1,667 @@
+# 数据预处理系统优化方案
+
+## 概述
+
+通过对当前数据预处理系统的代码分析，我们发现了多个可以优化的方面，包括性能瓶颈、代码结构、错误处理和文档完善等。本文档提供了一个全面的优化方案，旨在提高系统的性能、可维护性和可扩展性。
+
+## 1. 性能优化
+
+### 1.1 数据处理流程优化
+
+#### 当前问题
+- `merge_data_process_LST.py` 中的数据处理流程存在多次不必要的数据转换和中间文件生成
+- 大量使用循环处理数据，而非利用 pandas 的向量化操作
+- 数据库查询未优化，可能导致内存使用过高
+
+#### 优化建议
+
+1. **减少中间文件生成**
+   - 移除 `OUTPUT_CSV_TEMP_OBJSTATE` 等临时文件，直接在内存中处理数据
+   - 使用 pandas 的 `pipe()` 方法创建数据处理管道，减少中间 DataFrame 的创建
+
+2. **利用 pandas 向量化操作**
+   - 将 `_process_gnss_table` 和 `_process_can_table_optimized` 中的循环替换为向量化操作
+   - 示例代码：
+   ```python
+   # 替换这样的循环处理：
+   processed_data = []
+   for row in rows:
+       row_dict = dict(zip(db_columns, row))
+       record = {}
+       # 处理每行数据...
+       processed_data.append(record)
+   df_final = pd.DataFrame(processed_data)
+   
+   # 使用向量化操作：
+   df = pd.DataFrame(rows, columns=db_columns)
+   df['simTime'] = (df['second'] + df['usecond'] / 1e6).round(2)
+   df['playerId'] = PLAYER_ID_EGO
+   # 其他列的向量化处理...
+   ```
+
+3. **优化数据库查询**
+   - 使用分批查询处理大型数据库，避免一次性加载所有数据
+   - 示例代码：
+   ```python
+   def _process_db_file_in_batches(self, db_path, output_dir, table_type, csv_name, batch_size=10000):
+       output_csv_path = output_dir / csv_name
+       with sqlite3.connect(f"file:{db_path}?mode=ro", uri=True) as conn:
+           # 获取总行数
+           cursor = conn.cursor()
+           cursor.execute(f"SELECT COUNT(*) FROM {table_type}")
+           total_rows = cursor.fetchone()[0]
+           
+           # 分批处理
+           with open(output_csv_path, 'w', newline='') as f:
+               writer = None  # 将在第一批数据后初始化
+               
+               for offset in range(0, total_rows, batch_size):
+                   query = f"SELECT {', '.join(db_columns)} FROM {table_type} LIMIT {batch_size} OFFSET {offset}"
+                   cursor.execute(query)
+                   batch_rows = cursor.fetchall()
+                   
+                   # 处理这一批数据
+                   batch_df = self._process_batch(batch_rows, db_columns)
+                   
+                   # 写入CSV（第一批包含表头）
+                   if offset == 0:
+                       batch_df.to_csv(f, index=False)
+                   else:
+                       batch_df.to_csv(f, index=False, header=False)
+   ```
+
+4. **并行处理**
+   - 使用 `concurrent.futures` 或 `multiprocessing` 并行处理独立的数据文件
+   - 示例代码：
+   ```python
+   from concurrent.futures import ProcessPoolExecutor
+   
+   def process_zip_parallel(self):
+       with zipfile.ZipFile(self.config.zip_path, "r") as zip_ref:
+           db_files_to_process = []
+           # 找出需要处理的文件...
+           
+           with tempfile.TemporaryDirectory() as tmp_dir_str:
+               tmp_dir = Path(tmp_dir_str)
+               # 提取所有文件
+               for file_info, table_type, csv_name in db_files_to_process:
+                   extracted_path = tmp_dir / Path(file_info.filename).name
+                   with zip_ref.open(file_info.filename) as source, open(extracted_path, "wb") as target:
+                       shutil.copyfileobj(source, target)
+               
+               # 并行处理文件
+               with ProcessPoolExecutor(max_workers=min(os.cpu_count(), len(db_files_to_process))) as executor:
+                   futures = []
+                   for file_info, table_type, csv_name in db_files_to_process:
+                       extracted_path = tmp_dir / Path(file_info.filename).name
+                       futures.append(executor.submit(
+                           self._process_db_file, extracted_path, self.config.output_dir, table_type, csv_name
+                       ))
+                   
+                   # 等待所有任务完成
+                   for future in futures:
+                       future.result()
+   ```
+
+### 1.2 内存优化
+
+#### 当前问题
+- 处理大型数据集时可能导致内存溢出
+- 多次创建完整的 DataFrame 副本
+
+#### 优化建议
+
+1. **使用迭代器和生成器**
+   - 对于大型文件处理，使用 pandas 的 `chunksize` 参数分块读取
+   - 示例代码：
+   ```python
+   def merge_large_csv_files(self, file1, file2, output_file, on_columns, chunksize=10000):
+       # 打开输出文件
+       with open(output_file, 'w', newline='') as f_out:
+           # 读取第一个文件的表头
+           df1_header = pd.read_csv(file1, nrows=0)
+           df2_header = pd.read_csv(file2, nrows=0)
+           
+           # 创建合并后的表头
+           merged_columns = list(df1_header.columns)
+           for col in df2_header.columns:
+               if col not in on_columns and col not in merged_columns:
+                   merged_columns.append(col)
+           
+           # 写入表头
+           writer = csv.writer(f_out)
+           writer.writerow(merged_columns)
+           
+           # 分块读取和处理第一个文件
+           for df1_chunk in pd.read_csv(file1, chunksize=chunksize):
+               # 对于每个块，读取第二个文件并合并
+               for df2_chunk in pd.read_csv(file2, chunksize=chunksize):
+                   merged_chunk = pd.merge(df1_chunk, df2_chunk, on=on_columns, how='outer')
+                   # 只写入数据，不写入表头
+                   merged_chunk.to_csv(f_out, mode='a', header=False, index=False)
+   ```
+
+2. **减少 DataFrame 复制**
+   - 使用 `inplace=True` 参数进行原地操作
+   - 使用 `.loc` 或 `.iloc` 进行赋值而不是创建新的 DataFrame
+   - 示例代码：
+   ```python
+   # 替换这样的代码：
+   df_new = df[some_condition]
+   df_new['new_column'] = some_value
+   
+   # 使用这样的代码：
+   df.loc[some_condition, 'new_column'] = some_value
+   ```
+
+## 2. 代码结构优化
+
+### 2.1 插件系统重构
+
+#### 当前问题
+- 插件加载机制不够灵活，无法动态配置
+- 插件接口定义较为简单，缺乏高级功能（如进度报告、取消操作等）
+- 缺少插件版本控制和依赖管理
+
+#### 优化建议
+
+1. **增强插件接口**
+   - 添加进度报告和取消操作的支持
+   - 添加插件元数据（作者、版本、依赖等）
+   - 示例代码：
+   ```python
+   from abc import ABC, abstractmethod
+   from pathlib import Path
+   import pandas as pd
+   from typing import Dict, Any, Optional, Callable
+   
+   class PluginMetadata:
+       """插件元数据类"""
+       def __init__(self, name: str, version: str, author: str, description: str, dependencies: Dict[str, str] = None):
+           self.name = name
+           self.version = version
+           self.author = author
+           self.description = description
+           self.dependencies = dependencies or {}
+   
+   class CustomDataProcessorPlugin(ABC):
+       """增强的插件接口"""
+       
+       @abstractmethod
+       def get_metadata(self) -> PluginMetadata:
+           """返回插件元数据"""
+           pass
+       
+       @abstractmethod
+       def can_handle(self, zip_path: Path, folder_name: str) -> bool:
+           """检查是否可以处理该数据"""
+           pass
+       
+       @abstractmethod
+       def process_data(self, zip_path: Path, folder_name: str, output_dir: Path, 
+                        progress_callback: Optional[Callable[[float, str], None]] = None,
+                        cancel_check: Optional[Callable[[], bool]] = None) -> Optional[Path]:
+           """处理数据并支持进度报告和取消"""
+           pass
+       
+       @abstractmethod
+       def get_required_columns(self) -> Dict[str, Any]:
+           """返回插件提供的列和类型"""
+           pass
+       
+       def validate_dependencies(self) -> bool:
+           """验证插件依赖是否满足"""
+           metadata = self.get_metadata()
+           for package, version in metadata.dependencies.items():
+               try:
+                   import importlib
+                   module = importlib.import_module(package)
+                   if hasattr(module, '__version__') and module.__version__ < version:
+                       print(f"警告: {package} 版本 {module.__version__} 低于所需的 {version}")
+                       return False
+               except ImportError:
+                   print(f"错误: 缺少依赖 {package} {version}")
+                   return False
+           return True
+   ```
+
+2. **改进插件管理器**
+   - 支持插件热加载和卸载
+   - 添加插件配置和优先级管理
+   - 示例代码：
+   ```python
+   class EnhancedPluginManager:
+       """增强的插件管理器"""
+       
+       def __init__(self, plugin_dir: Path, config_file: Optional[Path] = None):
+           self.plugin_dir = plugin_dir
+           self.config_file = config_file
+           self.plugins: Dict[str, Type[CustomDataProcessorPlugin]] = {}
+           self.plugin_instances: Dict[str, CustomDataProcessorPlugin] = {}
+           self.plugin_configs: Dict[str, Dict[str, Any]] = {}
+           self.plugin_priorities: Dict[str, int] = {}
+           
+           self._load_plugin_configs()
+           self._load_plugins()
+       
+       def _load_plugin_configs(self):
+           """加载插件配置"""
+           if not self.config_file or not self.config_file.exists():
+               return
+               
+           try:
+               import yaml
+               with open(self.config_file, 'r') as f:
+                   config = yaml.safe_load(f)
+                   
+               if 'plugins' in config:
+                   for plugin_name, plugin_config in config['plugins'].items():
+                       self.plugin_configs[plugin_name] = plugin_config
+                       if 'priority' in plugin_config:
+                           self.plugin_priorities[plugin_name] = plugin_config['priority']
+                       if 'enabled' in plugin_config and not plugin_config['enabled']:
+                           print(f"插件 {plugin_name} 已禁用")
+           except Exception as e:
+               print(f"加载插件配置失败: {e}")
+       
+       def reload_plugins(self):
+           """重新加载所有插件"""
+           self.plugins.clear()
+           self.plugin_instances.clear()
+           self._load_plugins()
+       
+       def get_sorted_plugins(self) -> List[str]:
+           """按优先级返回排序后的插件名称"""
+           return sorted(self.plugins.keys(), 
+                         key=lambda name: self.plugin_priorities.get(name, 0),
+                         reverse=True)
+   ```
+
+### 2.2 模块化重构
+
+#### 当前问题
+- `merge_data_process_LST.py` 文件过大，包含多个职责
+- 配置管理分散在多个地方
+- 错误处理不一致
+
+#### 优化建议
+
+1. **拆分大型模块**
+   - 将 `ZipCSVProcessor` 拆分为多个专注于单一职责的类
+   - 创建专门的配置管理模块
+   - 示例结构：
+   ```
+   core/
+     __init__.py
+     config.py          # 配置管理
+     plugin_interface.py
+     plugin_manager.py
+     resource_manager.py
+   processors/
+     __init__.py
+     base_processor.py  # 处理器基类
+     zip_processor.py   # ZIP文件处理
+     db_processor.py    # 数据库处理
+     gnss_processor.py  # GNSS数据处理
+     can_processor.py   # CAN数据处理
+     merge_processor.py # 数据合并处理
+   utils/
+     __init__.py
+     logging_utils.py   # 日志工具
+     error_utils.py     # 错误处理工具
+     projection_utils.py # 坐标投影工具
+   ```
+
+2. **统一配置管理**
+   - 创建集中式配置管理类
+   - 支持从文件、环境变量和命令行加载配置
+   - 示例代码：
+   ```python
+   import os
+   import argparse
+   import yaml
+   from pathlib import Path
+   from typing import Any, Dict, Optional
+   
+   class ConfigManager:
+       """统一配置管理类"""
+       
+       def __init__(self):
+           self.config: Dict[str, Any] = {}
+           self.config_file: Optional[Path] = None
+       
+       def load_defaults(self):
+           """加载默认配置"""
+           self.config = {
+               'zip_path': None,
+               'output_dir': Path('output'),
+               'utm_zone': 51,
+               'x_offset': 0.0,
+               'y_offset': 0.0,
+               'plugins_dir': Path('plugins'),
+               'resources_dir': Path('resources'),
+               'log_level': 'INFO',
+           }
+       
+       def load_from_file(self, config_file: Path):
+           """从YAML文件加载配置"""
+           if not config_file.exists():
+               print(f"配置文件不存在: {config_file}")
+               return
+               
+           try:
+               with open(config_file, 'r') as f:
+                   file_config = yaml.safe_load(f)
+                   self.config.update(file_config)
+               self.config_file = config_file
+               print(f"已加载配置文件: {config_file}")
+           except Exception as e:
+               print(f"加载配置文件失败: {e}")
+       
+       def load_from_env(self, prefix: str = 'DATA_PROCESS_'):
+           """从环境变量加载配置"""
+           for key, value in os.environ.items():
+               if key.startswith(prefix):
+                   config_key = key[len(prefix):].lower()
+                   self.config[config_key] = value
+       
+       def load_from_args(self, args: argparse.Namespace):
+           """从命令行参数加载配置"""
+           for key, value in vars(args).items():
+               if value is not None:  # 只覆盖非None值
+                   self.config[key] = value
+       
+       def get(self, key: str, default: Any = None) -> Any:
+           """获取配置项"""
+           return self.config.get(key, default)
+       
+       def set(self, key: str, value: Any):
+           """设置配置项"""
+           self.config[key] = value
+       
+       def save(self, file_path: Optional[Path] = None):
+           """保存配置到文件"""
+           save_path = file_path or self.config_file
+           if not save_path:
+               print("未指定配置文件路径")
+               return False
+               
+           try:
+               with open(save_path, 'w') as f:
+                   yaml.dump(self.config, f, default_flow_style=False)
+               print(f"配置已保存到: {save_path}")
+               return True
+           except Exception as e:
+               print(f"保存配置失败: {e}")
+               return False
+   ```
+
+## 3. 错误处理和日志改进
+
+### 3.1 结构化日志系统
+
+#### 当前问题
+- 使用简单的 `print` 语句输出日志，难以过滤和分析
+- 缺乏日志级别和上下文信息
+- 无法将日志输出到文件或其他目标
+
+#### 优化建议
+
+1. **引入 logging 模块**
+   - 创建结构化的日志系统
+   - 支持不同的日志级别和格式
+   - 示例代码：
+   ```python
+   import logging
+   import sys
+   from pathlib import Path
+   from typing import Optional
+   
+   class LoggingManager:
+       """日志管理类"""
+       
+       def __init__(self):
+           self.logger = logging.getLogger('data_processor')
+           self.log_file: Optional[Path] = None
+           self.log_level = logging.INFO
+           self._configure_default_logger()
+       
+       def _configure_default_logger(self):
+           """配置默认日志器"""
+           self.logger.setLevel(logging.DEBUG)  # 设置最低级别
+           
+           # 创建控制台处理器
+           console_handler = logging.StreamHandler(sys.stdout)
+           console_handler.setLevel(self.log_level)
+           
+           # 设置格式
+           formatter = logging.Formatter(
+               '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+           )
+           console_handler.setFormatter(formatter)
+           
+           # 添加处理器
+           self.logger.addHandler(console_handler)
+       
+       def set_log_level(self, level: str):
+           """设置日志级别"""
+           level_map = {
+               'DEBUG': logging.DEBUG,
+               'INFO': logging.INFO,
+               'WARNING': logging.WARNING,
+               'ERROR': logging.ERROR,
+               'CRITICAL': logging.CRITICAL
+           }
+           
+           if level.upper() in level_map:
+               self.log_level = level_map[level.upper()]
+               for handler in self.logger.handlers:
+                   if isinstance(handler, logging.StreamHandler) and handler.stream == sys.stdout:
+                       handler.setLevel(self.log_level)
+           else:
+               self.logger.warning(f"未知的日志级别: {level}，使用默认级别 INFO")
+       
+       def add_file_handler(self, log_file: Path):
+           """添加文件处理器"""
+           try:
+               # 确保目录存在
+               log_file.parent.mkdir(parents=True, exist_ok=True)
+               
+               # 创建文件处理器
+               file_handler = logging.FileHandler(log_file, encoding='utf-8')
+               file_handler.setLevel(logging.DEBUG)  # 文件记录所有级别
+               
+               # 设置格式
+               formatter = logging.Formatter(
+                   '%(asctime)s - %(name)s - %(levelname)s - %(filename)s:%(lineno)d - %(message)s'
+               )
+               file_handler.setFormatter(formatter)
+               
+               # 添加处理器
+               self.logger.addHandler(file_handler)
+               self.log_file = log_file
+               self.logger.info(f"日志文件已设置为: {log_file}")
+           except Exception as e:
+               self.logger.error(f"设置日志文件失败: {e}")
+       
+       def get_logger(self, name: Optional[str] = None):
+           """获取命名日志器"""
+           if name:
+               return logging.getLogger(f"data_processor.{name}")
+           return self.logger
+   ```
+
+2. **在代码中使用结构化日志**
+   - 替换所有 `print` 语句为适当的日志调用
+   - 添加上下文信息和错误详情
+   - 示例代码：
+   ```python
+   # 创建日志管理器实例
+   logging_manager = LoggingManager()
+   
+   # 在主模块中配置
+   def configure_logging(args):
+       logging_manager.set_log_level(args.log_level)
+       if args.log_file:
+           logging_manager.add_file_handler(Path(args.log_file))
+   
+   # 在各个模块中使用
+   class ZipProcessor:
+       def __init__(self, config):
+           self.config = config
+           self.logger = logging_manager.get_logger('zip_processor')
+       
+       def process_zip(self):
+           self.logger.info(f"开始处理ZIP文件: {self.config.zip_path}")
+           try:
+               # 处理逻辑...
+               self.logger.debug(f"找到 {len(db_files)} 个数据库文件")
+           except zipfile.BadZipFile:
+               self.logger.error(f"无效的ZIP文件: {self.config.zip_path}")
+           except Exception as e:
+               self.logger.exception(f"处理ZIP文件时发生错误: {e}")
+   ```
+
+### 3.2 增强错误处理
+
+#### 当前问题
+- 错误处理不一致，有些地方捕获异常但没有提供足够的上下文
+- 缺少错误恢复机制
+- 用户无法轻松理解错误原因
+
+#### 优化建议
+
+1. **创建自定义异常类**
+   - 定义特定于应用程序的异常类型
+   - 提供更多上下文信息
+   - 示例代码：
+   ```python
+   class DataProcessError(Exception):
+       """数据处理错误的基类"""
+       pass
+   
+   class ConfigError(DataProcessError):
+       """配置错误"""
+       pass
+   
+   class DatabaseError(DataProcessError):
+       """数据库操作错误"""
+       pass
+   
+   class PluginError(DataProcessError):
+       """插件相关错误"""
+       pass
+   
+   class FileProcessError(DataProcessError):
+       """文件处理错误"""
+       def __init__(self, file_path, message, original_error=None):
+           self.file_path = file_path
+           self.original_error = original_error
+           super().__init__(f"处理文件 {file_path} 时出错: {message}")
+   ```
+
+2. **统一错误处理策略**
+   - 创建错误处理工具类
+   - 实现错误恢复和重试机制
+   - 示例代码：
+   ```python
+   import time
+   from functools import wraps
+   from typing import Callable, TypeVar, Any, Optional
+   
+   T = TypeVar('T')
+   
+   class ErrorHandler:
+       """错误处理工具类"""
+       
+       @staticmethod
+       def retry(max_attempts: int = 3, delay: float = 1.0, 
+                 backoff: float = 2.0, exceptions: tuple = (Exception,),
+                 logger: Optional[logging.Logger] = None):
+           """重试装饰器"""
+           def decorator(func: Callable[..., T]) -> Callable[..., T]:
+               @wraps(func)
+               def wrapper(*args, **kwargs) -> T:
+                   attempt = 1
+                   current_delay = delay
+                   
+                   while attempt <= max_attempts:
+                       try:
+                           return func(*args, **kwargs)
+                       except exceptions as e:
+                           if logger:
+                               logger.warning(
+                                   f"尝试 {attempt}/{max_attempts} 失败: {e}. "
+                                   f"{'重试中...' if attempt < max_attempts else '放弃.'}"
+                               )
+                           
+                           if attempt == max_attempts:
+                               raise
+                           
+                           time.sleep(current_delay)
+                           current_delay *= backoff
+                           attempt += 1
+               
+               return wrapper
+           return decorator
+       
+       @staticmethod
+       def safe_operation(default_value: Any = None, 
+                         logger: Optional[logging.Logger] = None):
+           """安全操作装饰器，出错时返回默认值"""
+           def decorator(func: Callable[..., T]) -> Callable[..., Optional[T]]:
+               @wraps(func)
+               def wrapper(*args, **kwargs) -> Optional[T]:
+                   try:
+                       return func(*args, **kwargs)
+                   except Exception as e:
+                       if logger:
+                           logger.error(f"操作失败: {e}")
+                       return default_value
+               
+               return wrapper
+           return decorator
+   ```
+
+## 4. 测试和文档完善
+
+### 4.1 单元测试
+
+#### 当前问题
+- 缺少自动化测试
+- 难以验证代码更改的影响
+
+#### 优化建议
+
+1. **创建测试框架**
+   - 使用 pytest 或 unittest 创建测试框架
+   - 为核心功能编写单元测试
+   - 示例代码：
+   ```python
+   # tests/test_config_manager.py
+   import pytest
+   import tempfile
+   from pathlib import Path
+   import os
+   from core.config import ConfigManager
+   
+   class TestConfigManager:
+       def setup_method(self):
+           self.config_manager = ConfigManager()
+           self.config_manager.load_defaults()
+       
+       def test_defaults(self):
+           assert self.config_manager.get('utm_zone') == 51
+           assert self.config_manager.get('x_offset') == 0.0
+           assert self.config_manager.get('output_dir') == Path('output')
+       
+       def test_load_from_file(self):
+           with tempfile.NamedTemporaryFile(mode='w', suffix='.yaml', delete=False) as f:
+               f.write("utm_zone: 52\nx_offset: 1.5\n")
+               temp_path = Path(f.name)
+           
+           try:
+               self.config_manager.load_from_file(temp_path)
+               assert self.config_manager.get('utm_zone') == 52
+               assert self.config_manager.get('x_offset') == 1.5
+               # 未覆盖的值应保

docs/plugin_system.md

@@ -0,0 +1,74 @@
+# LST数据处理插件系统
+
+本插件系统允许用户为自定义数据格式开发数据处理插件。插件处理的结果将被合并到主数据文件中。
+
+## 插件系统概述
+
+LST数据处理插件系统提供了一种灵活的方式来扩展数据处理功能，无需修改主程序代码。用户可以开发处理特定数据格式的插件，系统会自动加载并执行这些插件，并将处理结果合并到最终的数据文件中。
+
+## 开发新插件
+
+要创建新的数据处理插件，需要：
+
+1. 在 `plugins` 目录下创建新的 Python 文件
+2. 导入并继承 `CustomDataProcessorPlugin` 基类
+3. 实现所有必需的方法
+
+### 插件接口
+
+每个插件必须实现以下方法：
+
+```python
+def can_handle(self, zip_path: Path, folder_name: str) -> bool:
+    """判断插件是否可以处理指定文件夹中的数据"""
+    pass
+
+def process_data(self, zip_path: Path, folder_name: str, output_dir: Path) -> Optional[pd.DataFrame]:
+    """处理数据并返回处理后的DataFrame"""
+    pass
+
+def get_required_columns(self) -> Dict[str, Any]:
+    """返回插件将提供的列及其数据类型"""
+    pass
+```
+以下是一个处理自定义数据的示例插件：
+‘’‘
+from pathlib import Path
+from typing import Dict, Any, Optional
+import pandas as pd
+from plugins.plugin_interface import CustomDataProcessorPlugin
+
+class MyCustomDataProcessor(CustomDataProcessorPlugin):
+    def can_handle(self, zip_path: Path, folder_name: str) -> bool:
+        """判断插件是否可以处理指定文件夹中的数据"""
+        return folder_name.startswith('my_custom_data_')
+        
+    def get_required_columns(self) -> Dict[str, Any]:
+        """返回插件将提供的列及其数据类型"""
+        return {
+            'simTime': float,      # 必需
+            'simFrame': int,       # 必需
+            'playerId': int,       # 必需
+            'customField1': str,   # 自定义字段
+            'customField2': float  # 自定义字段
+        }
+        
+    def process_data(self, zip_path: Path, folder_name: str, output_dir: Path) -> Optional[pd.DataFrame]:
+        """处理数据并返回处理后的DataFrame"""
+        try:
+            # 1. 从zip中提取数据
+            # 2. 处理数据
+            # 3. 创建包含必需列的DataFrame
+            df = pd.DataFrame({
+                'simTime': [...],
+                'simFrame': [...],
+                'playerId': [...],
+                'customField1': [...],
+                'customField2': [...]
+            })
+            return df
+        except Exception as e:
+            print(f"处理数据时出错: {e}")
+            return None
+
+‘’‘

plugins/__init__.py

@@ -0,0 +1,3 @@
+"""
+插件包，包含所有自定义数据处理器的具体实现
+"""

plugins/radar_data_processor.py

@@ -0,0 +1,195 @@
+"""
+基于XML配置的雷达数据处理器示例
+使用XML配置文件来定义数据格式和处理参数
+示例数据格式（CSV）:
+1234567.89,1,15.6,12.3,30.5,-15.2
+1234567.90,2,25.1,8.7,-15.3,-12.8
+"""
+
+from pathlib import Path
+import pandas as pd
+from typing import Dict, Any, Optional
+import tempfile
+import zipfile
+import xml.etree.ElementTree as ET
+import numpy as np
+
+from core.plugin_interface import CustomDataProcessorPlugin
+
+class RadarDataProcessor(CustomDataProcessorPlugin):
+    """处理雷达数据的插件，使用XML配置文件"""
+    
+    def __init__(self):
+        """初始化插件，加载XML配置文件"""
+        self.config = None
+        self.config_path = Path("resources/radar_config.xml")
+        self.field_map = {}
+        self.delimiter = ','
+        self.processing_params = {}
+        self.columns = ['simTime', 'target_id', 'distance', 'velocity', 'angle', 'rcs']  # 移除 frame
+        self._load_config()
+    
+    def _load_config(self):
+        """加载并解析XML配置文件"""
+        try:
+            if not self.config_path.exists():
+                print(f"配置文件未找到: {self.config_path}")
+                return
+                
+            tree = ET.parse(self.config_path)
+            root = tree.getroot()
+            
+            # 解析字段配置
+            fields = root.find('./DataFormat/Fields')
+            if fields is not None:
+                for field in fields.findall('Field'):
+                    name = field.get('name')
+                    column = int(field.get('column'))
+                    dtype = field.get('type')
+                    self.field_map[column] = {
+                        'name': name,
+                        'type': dtype,
+                        'unit': field.get('unit', '')
+                    }
+            
+            # 解析分隔符
+            delimiter = root.find('./DataFormat/Delimiter')
+            if delimiter is not None:
+                self.delimiter = delimiter.text
+            
+            # 解析处理参数
+            processing = root.find('./Processing')
+            if processing is not None:
+                # 解析时间格式
+                time_format = processing.find('TimeFormat')
+                if time_format is not None:
+                    self.processing_params['time_format'] = time_format.text
+                
+                # 解析角度范围
+                angle_range = processing.find('AngleRange')
+                if angle_range is not None:
+                    self.processing_params['angle_min'] = float(angle_range.find('Min').text)
+                    self.processing_params['angle_max'] = float(angle_range.find('Max').text)
+                
+                # 解析距离范围
+                distance_range = processing.find('DistanceRange')
+                if distance_range is not None:
+                    self.processing_params['distance_min'] = float(distance_range.find('Min').text)
+                    self.processing_params['distance_max'] = float(distance_range.find('Max').text)
+                
+                # 解析过滤器参数
+                filters = processing.find('Filters')
+                if filters is not None:
+                    min_rcs = filters.find('MinRCS')
+                    if min_rcs is not None:
+                        self.processing_params['min_rcs'] = float(min_rcs.text)
+            
+            print(f"成功加载雷达配置文件: {self.config_path}")
+            
+        except Exception as e:
+            print(f"加载配置文件失败: {e}")
+            import traceback
+            traceback.print_exc()
+    
+    def can_handle(self, zip_path: Path, folder_name: str) -> bool:
+        """检查是否是雷达数据文件夹"""
+        # 修改判断逻辑，使其更灵活地匹配雷达数据文件夹
+        return 'radar' in folder_name.lower()
+    
+    def get_required_columns(self) -> Dict[str, Any]:
+        """定义输出数据的列和类型"""
+        return {
+            'simTime': float,      # 必需列
+            'playerId': int,       # 必需列
+            'simFrame': int,       # 必需列
+            'radar_distance': float,  # 雷达距离
+            'radar_velocity': float,  # 雷达速度
+            'radar_angle': float,     # 雷达角度
+            'radar_rcs': float,       # 雷达RCS
+        }
+    
+    def _convert_timestamp(self, timestamp: float) -> float:
+        """根据配置转换时间戳格式"""
+        time_format = self.processing_params.get('time_format', 'unix_timestamp')
+        if time_format == 'unix_timestamp':
+            return timestamp
+        # 添加其他时间格式的转换if需要
+        return timestamp
+    
+    def _filter_data(self, df: pd.DataFrame) -> pd.DataFrame:
+        """根据配置的参数过滤数据"""
+        if df.empty:
+            return df
+            
+        # 角度范围过滤
+        angle_min = self.processing_params.get('angle_min', -float('inf'))
+        angle_max = self.processing_params.get('angle_max', float('inf'))
+        df = df[df['angle'].between(angle_min, angle_max)]
+        
+        # 距离范围过滤
+        distance_min = self.processing_params.get('distance_min', 0)
+        distance_max = self.processing_params.get('distance_max', float('inf'))
+        df = df[df['distance'].between(distance_min, distance_max)]
+        
+        # RCS过滤
+        min_rcs = self.processing_params.get('min_rcs', -float('inf'))
+        df = df[df['rcs'] >= min_rcs]
+        
+        return df
+
+    def _process_extracted_files(self, file_paths: list) -> Optional[pd.DataFrame]:
+        """处理提取的雷达数据文件，直接存储到输出目录"""
+        all_data = []
+        frame_counter = 1
+        
+        for file_path in file_paths:
+            try:
+                if file_path.suffix.lower() == '.csv':
+                    # Read and process data
+                    df = pd.read_csv(file_path, delimiter=self.delimiter, header=None)
+                    if df.empty:
+                        continue
+                        
+                    df = df.iloc[:, :6]
+                    df.columns = ['simTime', 'target_id', 'distance', 'velocity', 'angle', 'rcs']
+                    
+                    # Add frame numbers and process data
+                    df['simFrame'] = range(frame_counter, frame_counter + len(df))
+                    frame_counter += len(df)
+                    
+                    # Round values for consistency
+                    for col in ['simTime', 'distance', 'velocity', 'angle', 'rcs']:
+                        df[col] = df[col].round(3)
+
+                    df['playerId'] = 1
+
+                    # Filter and rename columns
+                    filtered_df = self._filter_data(df)
+                    if not filtered_df.empty:
+                        filtered_df = filtered_df.rename(columns={
+                            'distance': 'radar_distance',
+                            'velocity': 'radar_velocity',
+                            'angle': 'radar_angle',
+                            'rcs': 'radar_rcs'
+                        })
+                        all_data.append(filtered_df)
+                        
+            except Exception as e:
+                print(f"处理文件失败: {file_path}: {e}")
+                continue
+                    
+        if not all_data:
+            return None
+        
+        # Merge all data and sort by time
+        final_df = pd.concat(all_data, ignore_index=True)
+        final_df.sort_values('simTime', inplace=True)
+        final_df['simFrame'] = range(1, len(final_df) + 1)
+        
+        # Ensure correct data types
+        for col, dtype in self.get_required_columns().items():
+            if col in final_df.columns:
+                final_df[col] = final_df[col].astype(dtype)
+        
+        final_df.reset_index(drop=True, inplace=True)
+        return final_df

resources/radar_config.xml

@@ -0,0 +1,28 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<RadarConfig>
+    <DataFormat>
+        <Fields>
+            <Field name="timestamp" type="float" column="0" />
+            <Field name="target_id" type="int" column="1" />
+            <Field name="distance" type="float" column="2" unit="m" />
+            <Field name="velocity" type="float" column="3" unit="m/s" />
+            <Field name="angle" type="float" column="4" unit="deg" />
+            <Field name="rcs" type="float" column="5" unit="dBsm" />
+        </Fields>
+        <Delimiter>,</Delimiter>
+    </DataFormat>
+    <Processing>
+        <TimeFormat>unix_timestamp</TimeFormat>
+        <AngleRange>
+            <Min>-60</Min>
+            <Max>60</Max>
+        </AngleRange>
+        <DistanceRange>
+            <Min>0</Min>
+            <Max>200</Max>
+        </DistanceRange>
+        <Filters>
+            <MinRCS>-20</MinRCS>
+        </Filters>
+    </Processing>
+</RadarConfig>

run.py

@@ -0,0 +1,300 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+LST数据处理系统入口文件
+支持多种数据格式和插件扩展
+"""
+
+import argparse
+import sys
+import traceback
+from pathlib import Path
+
+from core.config_manager import load_config, update_config
+from core.optimized_processor import process_lst_data, process_pgvil_data  # 新增导入
+from core.plugin_manager import PluginManager
+from core.resource_manager import ResourceManager
+
+
+def parse_arguments():
+    """解析命令行参数"""
+    parser = argparse.ArgumentParser(
+        description='数据处理系统，支持多种数据格式和插件扩展'
+    )
+
+    # 新增数据类型参数
+    parser.add_argument(
+        '--data-type',
+        type=str,
+        choices=['lst', 'pgvil'],
+        default='lst',
+        help='要处理的数据类型 (lst 或 pgvil)'
+    )
+    
+    # 定义参数
+    parser.add_argument(
+        '--zip-path',
+        type=Path,
+        default=Path('V2I_CSAE53-2020_HazardousLocationW_LST_02-01.zip'),
+        help='输入的ZIP数据文件路径'
+    )
+
+    parser.add_argument(
+        '--trafficlight-json',
+        type=Path,
+        default=None,
+        help='交通信号灯JSON配置文件路径'
+    )
+
+    parser.add_argument(
+        '--output-dir',
+        type=Path,
+        default=Path('output'),
+        help='输出目录的基础路径'
+    )
+
+    parser.add_argument(
+        '--utm-zone',
+        type=int,
+        default=51,
+        help='UTM坐标系区域 (默认: 51)'
+    )
+
+    parser.add_argument(
+        '--x-offset',
+        type=float,
+        default=0.0,
+        help='X坐标偏移量'
+    )
+                       
+    parser.add_argument(
+        '--y-offset',
+        type=float,
+        default=0.0,
+        help='Y坐标偏移量'
+    )
+                       
+    parser.add_argument(
+        '--config',
+        type=Path,
+        default=Path('config/config.json'),
+        help='配置文件路径'
+    )
+
+    parser.add_argument(
+        '--plugins-dir',
+        type=Path,
+        default=Path('plugins'),
+        help='插件目录路径'
+    )
+                       
+    parser.add_argument(
+        '--resources-dir',
+        type=Path,
+        default=Path('resources'),
+        help='资源文件目录路径'
+    )
+
+    parser.add_argument(
+        '--use-parallel',
+        action='store_true',
+        help='启用并行处理'
+    )
+                       
+    parser.add_argument(
+        '--no-parallel',
+        action='store_true',
+        help='禁用并行处理'
+    )
+                       
+    parser.add_argument(
+        '--max-workers',
+        type=int,
+        default=None,
+        help='并行处理的最大工作线程数'
+    )
+                       
+    parser.add_argument(
+        '--batch-size',
+        type=int,
+        default=10000,
+        help='处理大数据集时的批处理大小'
+    )
+                       
+    parser.add_argument(
+        '--log-level',
+        type=str,
+        choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'],
+        default='INFO',
+        help='日志级别'
+    )
+                       
+    parser.add_argument(
+        '--log-dir',
+        type=Path,
+        default=Path('logs'),
+        help='日志文件目录'
+    )
+                       
+    parser.add_argument(
+        '--no-log-file',
+        action='store_true',
+        help='禁用文件日志'
+    )
+
+    return parser.parse_args()
+
+
+def setup_config(args):
+    """设置配置"""
+    # 根据ZIP文件名创建输出目录
+    zip_name = args.zip_path.stem
+    output_dir = args.output_dir / zip_name
+    output_dir.mkdir(parents=True, exist_ok=True)
+    
+    print(f"输出目录: {output_dir}")
+
+    # 加载配置
+    config = load_config(args.config)
+    
+    # 更新配置中的输出目录为包含zip名称的子目录
+    config['paths']['output_dir'] = str(output_dir)
+    config['paths']['data_dir'] = str(output_dir)  # 确保数据也输出到同一目录
+    config['paths']['temp_dir'] = str(output_dir)  # 确保临时文件也在同一目录
+    
+    # 使用命令行参数覆盖配置文件中的设置
+    if args.use_parallel and args.no_parallel:
+        print("警告: 同时指定了 --use-parallel 和 --no-parallel，将使用 --use-parallel")
+        config['processing']['use_parallel'] = True
+    elif args.use_parallel:
+        config['processing']['use_parallel'] = True
+    elif args.no_parallel:
+        config['processing']['use_parallel'] = False
+        
+    if args.max_workers is not None:
+        config['processing']['max_workers'] = args.max_workers
+        
+    if args.batch_size != 10000:  # 不等于默认值
+        config['processing']['batch_size'] = args.batch_size
+        
+    # 更新日志配置
+    config['logging']['level'] = args.log_level
+    config['logging']['log_dir'] = str(args.log_dir)
+    config['logging']['log_to_file'] = not args.no_log_file
+    
+    # 更新坐标系配置
+    config['coordinates']['utm_zone'] = args.utm_zone
+    config['coordinates']['x_offset'] = args.x_offset
+    config['coordinates']['y_offset'] = args.y_offset
+    
+    # 更新路径配置
+    config['paths']['plugins_dir'] = str(args.plugins_dir)
+    config['paths']['resources_dir'] = str(args.resources_dir)
+    
+    # 应用更新后的配置
+    update_config(config)
+    
+    return output_dir
+
+
+def process_plugins(args, output_dir, final_csv_path):
+    """处理插件数据"""
+    # 初始化插件处理管理器
+    plugin_manager = PluginManager(args.plugins_dir)
+    resource_manager = ResourceManager(args.resources_dir)
+
+    # 处理自定义数据
+    print("处理并合并自定义数据...")
+    folders = resource_manager.list_zip_folders(args.zip_path)
+    
+    for folder in folders:
+        plugin = plugin_manager.get_plugin_for_data(args.zip_path, folder)
+        if not plugin:
+            print(f"未找到文件夹的插件: {folder}")
+            continue
+
+        print(f"使用插件 '{plugin.__name__}' 处理文件夹 '{folder}'")
+        plugin_instance = plugin()
+        plugin_output = plugin_instance.process_data(
+            args.zip_path,
+            folder,
+            output_dir
+        )
+        
+        if plugin_output is not None and not plugin_output.empty:
+            output_file = output_dir / f"{folder}_processed.csv"
+            print(f'插件输出文件: {output_file}')
+            plugin_output.to_csv(output_file, index=False)
+            
+            if not resource_manager.validate_plugin_output(output_file):
+                print(f"警告: 插件输出验证失败: {folder}")
+                continue
+                
+            # 合并自定义数据与主数据文件
+            print(f"合并 {folder} 数据...")
+            if resource_manager.merge_plugin_data(
+                final_csv_path,
+                output_file,
+                final_csv_path
+            ):
+                print(f"成功合并 {folder} 数据")
+            else:
+                print(f"警告: 合并 {folder} 数据失败")
+        else:
+            print(f"警告: 插件处理失败: {folder}")
+
+
+def main():
+    """主函数"""
+    args = parse_arguments()
+
+    try:
+        # 设置配置
+        output_dir = setup_config(args)
+        
+        print("开始数据处理流程")
+        print(f"从以下位置加载配置: {args.config}")
+        
+        # 根据数据类型选择处理流程
+        if args.data_type == 'lst':
+            final_csv_path = process_lst_data(
+                zip_data_path=args.zip_path,
+                output_base_dir=output_dir,
+                trafficlight_json_path=args.trafficlight_json, 
+                utm_zone=args.utm_zone,
+                x_offset=args.x_offset,
+                y_offset=args.y_offset
+            )
+        elif args.data_type == 'pgvil':
+            final_csv_path = process_pgvil_data(
+                zip_data_path=args.zip_path,
+                output_base_dir=output_dir,
+                utm_zone=args.utm_zone,
+                x_offset=args.x_offset,
+                y_offset=args.y_offset
+            )
+        else:
+            print(f"不支持的数据类型: {args.data_type}")
+            sys.exit(1)
+            
+        if not final_csv_path:
+            print(f"{args.data_type}内置数据处理失败")
+            sys.exit(1)
+            
+        print(f"\n{args.data_type}内置处理流程成功完成！")
+        
+        # 处理插件数据
+        process_plugins(args, output_dir, final_csv_path)
+
+        print("LST数据处理成功完成")
+        print(f"所有处理结果已保存到: {output_dir}")
+        sys.exit(0)
+
+    except Exception as e:
+        print(f"\n处理过程中出现错误: {e}")
+        traceback.print_exc()
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

utils/logging_utils.py

@@ -0,0 +1,42 @@
+from datetime import datetime
+
+def print_with_level(level, message):
+    """打印带有时间戳和级别的消息"""
+    timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+    print(f"{timestamp} - {level} - {message}")
+
+def debug(message):
+    print_with_level("DEBUG", message)
+
+def info(message):
+    print_with_level("INFO", message)
+
+def warning(message):
+    print_with_level("WARNING", message)
+
+def error(message):
+    print_with_level("ERROR", message)
+
+def critical(message):
+    print_with_level("CRITICAL", message)
+
+def exception(message):
+    """打印异常信息"""
+    import traceback
+    print_with_level("ERROR", f"{message}\n{traceback.format_exc()}")
+
+# 兼容性函数
+def get_logger(name=None, level=None, log_dir=None, log_to_file=True):
+    """返回一个模拟logger的对象，实际上只是打印到控制台"""
+    return type('Logger', (), {
+        'debug': debug,
+        'info': info,
+        'warning': warning,
+        'error': error,
+        'critical': critical,
+        'exception': exception
+    })()
+
+def set_global_level(level):
+    """为了保持兼容性，这个函数实际上不做任何事"""
+    pass