ifc-to-excel

IFC 到 Excel 转换

业务案例

问题陈述

IFC（Industry Foundation Classes）是开放的 BIM 标准，但：

• 读取 IFC 需要专用软件
• 提取属性需要编程知识
• 批量处理需要手动操作，耗时较长
• 与分析工具集成较为复杂

解决方案

IfcExporter.exe 可将 IFC 文件转换为结构化 Excel 数据库，让 BIM 数据便于用于分析、验证和报告。

业务价值

• 开放标准 - 处理任意 IFC 文件（2x3、4x、4.3）
• 无需许可证 - 无需 BIM 软件即可离线运行
• 数据提取 - 提取所有属性、工程量和材料
• 3D 几何 - 导出为 Collada DAE 格式
• 适用于工作流 - 集成到 ETL 工作流中

技术实现

CLI 语法

IfcExporter.exe <input_ifc> [options]

支持的 IFC 版本

版本	架构	描述
IFC2x3	MVD	最常见的交换格式
IFC4	ADD1	增强的属性
IFC4x1	Alignment	支持基础设施
IFC4x3	最新	完整支持基础设施

输出格式

输出	描述
.xlsx	包含元素和属性的 Excel 数据库
.dae	带有匹配 ID 的 Collada 3D 几何

选项

选项	描述
bbox	包含元素边界框
-no-xlsx	跳过 Excel 导出
-no-collada	跳过 3D 几何导出

示例

# Basic conversion (XLSX + DAE)
IfcExporter.exe "C:\Models\Building.ifc"

# With bounding boxes
IfcExporter.exe "C:\Models\Building.ifc" bbox

# Excel only (no 3D geometry)
IfcExporter.exe "C:\Models\Building.ifc" -no-collada

# Batch processing
for /R "C:\IFC_Models" %f in (*.ifc) do IfcExporter.exe "%f" bbox

Python 集成

import subprocess
import pandas as pd
from pathlib import Path
from typing import List, Optional, Dict, Any, Set
from dataclasses import dataclass, field
from enum import Enum
import json


class IFCVersion(Enum):
    """IFC schema versions."""
    IFC2X3 = "IFC2X3"
    IFC4 = "IFC4"
    IFC4X1 = "IFC4X1"
    IFC4X3 = "IFC4X3"


class IFCEntityType(Enum):
    """Common IFC entity types."""
    IFCWALL = "IfcWall"
    IFCWALLSTANDARDCASE = "IfcWallStandardCase"
    IFCSLAB = "IfcSlab"
    IFCCOLUMN = "IfcColumn"
    IFCBEAM = "IfcBeam"
    IFCDOOR = "IfcDoor"
    IFCWINDOW = "IfcWindow"
    IFCROOF = "IfcRoof"
    IFCSTAIR = "IfcStair"
    IFCRAILING = "IfcRailing"
    IFCFURNISHINGELEMENT = "IfcFurnishingElement"
    IFCSPACE = "IfcSpace"
    IFCBUILDINGSTOREY = "IfcBuildingStorey"
    IFCBUILDING = "IfcBuilding"
    IFCSITE = "IfcSite"


@dataclass
class IFCElement:
    """Represents an IFC element."""
    global_id: str
    ifc_type: str
    name: str
    description: Optional[str]
    object_type: Optional[str]
    level: Optional[str]

    # Quantities
    area: Optional[float] = None
    volume: Optional[float] = None
    length: Optional[float] = None
    height: Optional[float] = None
    width: Optional[float] = None

    # Bounding box (if exported)
    bbox_min_x: Optional[float] = None
    bbox_min_y: Optional[float] = None
    bbox_min_z: Optional[float] = None
    bbox_max_x: Optional[float] = None
    bbox_max_y: Optional[float] = None
    bbox_max_z: Optional[float] = None

    # Properties
    properties: Dict[str, Any] = field(default_factory=dict)
    materials: List[str] = field(default_factory=list)


@dataclass
class IFCProperty:
    """Represents an IFC property."""
    pset_name: str
    property_name: str
    value: Any
    value_type: str


@dataclass
class IFCMaterial:
    """Represents an IFC material."""
    name: str
    category: Optional[str]
    thickness: Optional[float]
    layer_position: Optional[int]


class IFCExporter:
    """IFC to Excel converter using DDC IfcExporter CLI."""

    def __init__(self, exporter_path: str = "IfcExporter.exe"):
        self.exporter = Path(exporter_path)
        if not self.exporter.exists():
            raise FileNotFoundError(f"IfcExporter not found: {exporter_path}")

    def convert(self, ifc_file: str,
                include_bbox: bool = True,
                export_xlsx: bool = True,
                export_collada: bool = True) -> Path:
        """Convert IFC file to Excel."""
        ifc_path = Path(ifc_file)
        if not ifc_path.exists():
            raise FileNotFoundError(f"IFC file not found: {ifc_file}")

        cmd = [str(self.exporter), str(ifc_path)]

        if include_bbox:
            cmd.append("bbox")
        if not export_xlsx:
            cmd.append("-no-xlsx")
        if not export_collada:
            cmd.append("-no-collada")

        result = subprocess.run(cmd, capture_output=True, text=True)

        if result.returncode != 0:
            raise RuntimeError(f"Export failed: {result.stderr}")

        return ifc_path.with_suffix('.xlsx')

    def batch_convert(self, folder: str,
                      include_subfolders: bool = True,
                      include_bbox: bool = True) -> List[Dict[str, Any]]:
        """Convert all IFC files in folder."""
        folder_path = Path(folder)
        pattern = "**/*.ifc" if include_subfolders else "*.ifc"

        results = []
        for ifc_file in folder_path.glob(pattern):
            try:
                output = self.convert(str(ifc_file), include_bbox)
                results.append({
                    'input': str(ifc_file),
                    'output': str(output),
                    'status': 'success'
                })
                print(f"✓ Converted: {ifc_file.name}")
            except Exception as e:
                results.append({
                    'input': str(ifc_file),
                    'output': None,
                    'status': 'failed',
                    'error': str(e)
                })
                print(f"✗ Failed: {ifc_file.name} - {e}")

        return results

    def read_elements(self, xlsx_file: str) -> pd.DataFrame:
        """Read converted Excel as DataFrame."""
        return pd.read_excel(xlsx_file, sheet_name="Elements")

    def get_element_types(self, xlsx_file: str) -> pd.DataFrame:
        """Get element type summary."""
        df = self.read_elements(xlsx_file)

        if 'IfcType' not in df.columns:
            raise ValueError("IfcType column not found")

        summary = df.groupby('IfcType').agg({
            'GlobalId': 'count',
            'Volume': 'sum' if 'Volume' in df.columns else 'count',
            'Area': 'sum' if 'Area' in df.columns else 'count'
        }).reset_index()

        summary.columns = ['IFC_Type', 'Count', 'Total_Volume', 'Total_Area']
        return summary.sort_values('Count', ascending=False)

    def get_levels(self, xlsx_file: str) -> pd.DataFrame:
        """Get building level summary."""
        df = self.read_elements(xlsx_file)

        level_col = None
        for col in ['Level', 'BuildingStorey', 'IfcBuildingStorey']:
            if col in df.columns:
                level_col = col
                break

        if level_col is None:
            return pd.DataFrame(columns=['Level', 'Element_Count'])

        summary = df.groupby(level_col).agg({
            'GlobalId': 'count'
        }).reset_index()
        summary.columns = ['Level', 'Element_Count']
        return summary

    def get_materials(self, xlsx_file: str) -> pd.DataFrame:
        """Get material summary."""
        df = self.read_elements(xlsx_file)

        if 'Material' not in df.columns:
            return pd.DataFrame(columns=['Material', 'Count'])

        summary = df.groupby('Material').agg({
            'GlobalId': 'count'
        }).reset_index()
        summary.columns = ['Material', 'Element_Count']
        return summary.sort_values('Element_Count', ascending=False)

    def get_quantities(self, xlsx_file: str,
                       group_by: str = 'IfcType') -> pd.DataFrame:
        """Get quantity takeoff summary."""
        df = self.read_elements(xlsx_file)

        if group_by not in df.columns:
            raise ValueError(f"Column {group_by} not found")

        agg_dict = {'GlobalId': 'count'}

        # Add numeric columns for aggregation
        numeric_cols = ['Volume', 'Area', 'Length', 'Width', 'Height']
        for col in numeric_cols:
            if col in df.columns:
                agg_dict[col] = 'sum'

        summary = df.groupby(group_by).agg(agg_dict).reset_index()
        return summary

    def filter_by_type(self, xlsx_file: str,
                       ifc_types: List[str]) -> pd.DataFrame:
        """Filter elements by IFC type."""
        df = self.read_elements(xlsx_file)
        return df[df['IfcType'].isin(ifc_types)]

    def get_properties(self, xlsx_file: str,
                       element_id: str) -> Dict[str, Any]:
        """Get all properties for specific element."""
        df = self.read_elements(xlsx_file)
        element = df[df['GlobalId'] == element_id]

        if element.empty:
            return {}

        # Convert row to dictionary, excluding NaN values
        props = element.iloc[0].dropna().to_dict()
        return props

    def validate_ifc_data(self, xlsx_file: str) -> Dict[str, Any]:
        """Validate IFC data quality."""
        df = self.read_elements(xlsx_file)

        validation = {
            'total_elements': len(df),
            'issues': []
        }

        # Check for missing GlobalIds
        if 'GlobalId' in df.columns:
            missing_ids = df['GlobalId'].isna().sum()
            if missing_ids > 0:
                validation['issues'].append(f"{missing_ids} elements missing GlobalId")

        # Check for missing names
        if 'Name' in df.columns:
            missing_names = df['Name'].isna().sum()
            if missing_names > 0:
                validation['issues'].append(f"{missing_names} elements missing Name")

        # Check for zero quantities
        for col in ['Volume', 'Area']:
            if col in df.columns:
                zero_qty = (df[col] == 0).sum()
                if zero_qty > 0:
                    validation['issues'].append(f"{zero_qty} elements with zero {col}")

        # Check for duplicate GlobalIds
        if 'GlobalId' in df.columns:
            duplicates = df['GlobalId'].duplicated().sum()
            if duplicates > 0:
                validation['issues'].append(f"{duplicates} duplicate GlobalIds")

        validation['is_valid'] = len(validation['issues']) == 0
        return validation


class IFCQuantityTakeoff:
    """Quantity takeoff from IFC data."""

    def __init__(self, exporter: IFCExporter):
        self.exporter = exporter

    def generate_qto(self, ifc_file: str) -> Dict[str, pd.DataFrame]:
        """Generate complete quantity takeoff."""
        xlsx = self.exporter.convert(ifc_file, include_bbox=True)
        df = self.exporter.read_elements(str(xlsx))

        qto = {}

        # Walls
        walls = df[df['IfcType'].str.contains('Wall', case=False, na=False)]
        if not walls.empty:
            qto['Walls'] = self._summarize_elements(walls, 'Type Name')

        # Slabs
        slabs = df[df['IfcType'].str.contains('Slab', case=False, na=False)]
        if not slabs.empty:
            qto['Slabs'] = self._summarize_elements(slabs, 'Type Name')

        # Columns
        columns = df[df['IfcType'].str.contains('Column', case=False, na=False)]
        if not columns.empty:
            qto['Columns'] = self._summarize_elements(columns, 'Type Name')

        # Beams
        beams = df[df['IfcType'].str.contains('Beam', case=False, na=False)]
        if not beams.empty:
            qto['Beams'] = self._summarize_elements(beams, 'Type Name')

        # Doors
        doors = df[df['IfcType'].str.contains('Door', case=False, na=False)]
        if not doors.empty:
            qto['Doors'] = self._summarize_elements(doors, 'Type Name')

        # Windows
        windows = df[df['IfcType'].str.contains('Window', case=False, na=False)]
        if not windows.empty:
            qto['Windows'] = self._summarize_elements(windows, 'Type Name')

        return qto

    def _summarize_elements(self, df: pd.DataFrame,
                            group_col: str) -> pd.DataFrame:
        """Summarize elements by grouping column."""
        if group_col not in df.columns:
            group_col = 'IfcType'

        agg_dict = {'GlobalId': 'count'}
        for col in ['Volume', 'Area', 'Length']:
            if col in df.columns:
                agg_dict[col] = 'sum'

        summary = df.groupby(group_col).agg(agg_dict).reset_index()
        summary.rename(columns={'GlobalId': 'Count'}, inplace=True)
        return summary

    def export_to_excel(self, qto: Dict[str, pd.DataFrame],
                        output_file: str):
        """Export QTO to multi-sheet Excel."""
        with pd.ExcelWriter(output_file, engine='openpyxl') as writer:
            for sheet_name, df in qto.items():
                df.to_excel(writer, sheet_name=sheet_name, index=False)


# Convenience functions
def convert_ifc_to_excel(ifc_file: str,
                         exporter_path: str = "IfcExporter.exe") -> str:
    """Quick conversion of IFC to Excel."""
    exporter = IFCExporter(exporter_path)
    output = exporter.convert(ifc_file)
    return str(output)


def get_ifc_summary(xlsx_file: str) -> Dict[str, Any]:
    """Get summary of converted IFC data."""
    df = pd.read_excel(xlsx_file, sheet_name="Elements")

    return {
        'total_elements': len(df),
        'ifc_types': df['IfcType'].nunique() if 'IfcType' in df.columns else 0,
        'levels': df['Level'].nunique() if 'Level' in df.columns else 0,
        'total_volume': df['Volume'].sum() if 'Volume' in df.columns else 0,
        'total_area': df['Area'].sum() if 'Area' in df.columns else 0
    }

输出结构

Excel 工作表

工作表	内容
Elements	所有带属性的 IFC 元素
Types	元素类型摘要
Levels	建筑楼层数据
Materials	材料分配
PropertySets	IFC 属性集

元素列

列	类型	描述
GlobalId	string	IFC GUID
IfcType	string	IFC 实体类型
Name	string	元素名称
Description	string	元素描述
Level	string	建筑楼层
Material	string	主要材料
Volume	float	体积（m³）
Area	float	表面积（m²）
Length	float	长度（m）
Height	float	高度（m）
Width	float	宽度（m）

快速入门

# Initialize exporter
exporter = IFCExporter("C:/DDC/IfcExporter.exe")

# Convert IFC to Excel
xlsx = exporter.convert("C:/Models/Building.ifc", include_bbox=True)

# Read elements
df = exporter.read_elements(str(xlsx))
print(f"Total elements: {len(df)}")

# Get element types
types = exporter.get_element_types(str(xlsx))
print(types)

# Get quantities by type
qto = exporter.get_quantities(str(xlsx), group_by='IfcType')
print(qto)

常见用例

1. 模型验证

exporter = IFCExporter()
xlsx = exporter.convert("model.ifc")
validation = exporter.validate_ifc_data(str(xlsx))

if not validation['is_valid']:
    print("Issues found:")
    for issue in validation['issues']:
        print(f"  - {issue}")

2. 工程量统计

qto_generator = IFCQuantityTakeoff(exporter)
qto = qto_generator.generate_qto("building.ifc")

for category, data in qto.items():
    print(f"\n{category}:")
    print(data.to_string(index=False))

3. 材料明细表

xlsx = exporter.convert("building.ifc")
materials = exporter.get_materials(str(xlsx))
print(materials)

与 DDC 工作流集成

# Full pipeline: IFC → Excel → Validation → Cost Estimate
exporter = IFCExporter("C:/DDC/IfcExporter.exe")

# 1. Convert IFC
xlsx = exporter.convert("project.ifc", include_bbox=True)

# 2. Validate data
validation = exporter.validate_ifc_data(str(xlsx))
print(f"Valid: {validation['is_valid']}")

# 3. Generate QTO
qto = IFCQuantityTakeoff(exporter)
quantities = qto.generate_qto("project.ifc")

# 4. Export for cost estimation
qto.export_to_excel(quantities, "project_qto.xlsx")

资源

• GitHub：cad2data Pipeline
• IFC 标准：buildingSMART
• DDC 图书：第 2.4 章 - CAD/BIM 数据提取