什么是 IFC 数据提取？

IFC 数据提取技能利用 IfcOpenShell 填补了复杂 BIM 模型与可用数据之间的空白。它允许开发人员和 BIM 经理通过编程方式访问中立厂商 IFC 文件中的项目元数据、构件属性和空间层级。

通过使用此 Openclaw Skills 实现，用户可以自动检索建筑信息，确保不同建筑、工程和施工 (AEC) 软件平台之间的互操作性。该技能将原始 IFC 数据转换为干净、结构化的格式，适用于报告和进一步的技术分析。

下载入口:https://github.com/openclaw/skills/tree/main/skills/datadrivenconstruction/ifc-data-extraction

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

2. 手动安装

将技能文件夹复制到以下位置之一

优先级：工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

IFC 数据提取 应用场景

• 用于建筑成本估算和采购的自动工程量清单 (QTO)。
• 提取属性集和材料数据，用于可持续性审计和碳足迹计算。
• 生成空间关系图，以了解建筑层级和包含关系。
• 将 BIM 模型转换为 CSV 或 SQL 等结构化格式，用于下游数据分析。
• 根据项目要求和建筑标准验证模型数据。

1. 使用开源 IfcOpenShell 库加载 IFC 文件来初始化提取器。
2. 根据项目内部架构向模型查询特定的 IFC 类型，如墙、楼板或空间。
3. 遍历构件关系以识别空间包含、材料关联和分配的属性集。
4. 计算几何数据，包括包围盒和体积（如果空间或碰撞分析需要）。
5. 将处理后的数据导出为结构化格式，如 pandas DataFrame、Excel 表格或 SQL 数据库，用于外部报告。

IFC 数据提取 配置指南

要开始使用此技能，请使用 pip 安装所需的依赖项：

pip install ifcopenshell pandas numpy sqlalchemy openpyxl

确保您的工作目录中有一个 IFC 文件（架构版本 IFC2x3 或 IFC4），以便开始使用此 Openclaw Skills 资源进行提取。

IFC 数据提取 数据架构与分类体系

该技能将提取的 BIM 数据组织成以下架构：

name: "ifc-data-extraction" description: "Extract structured data from IFC (Industry Foundation Classes) files using IfcOpenShell. Parse BIM models, extract quantities, properties, spatial relationships, and export to various formats."

IFC Data Extraction

Overview

This skill provides comprehensive IFC file parsing and data extraction using IfcOpenShell. Extract element data, quantities, properties, and relationships from BIM models for analysis and reporting.

Based on Open BIM Standards - Working with vendor-neutral IFC format for maximum interoperability.

"IFC является открытым стандартом для обмена BIM-данными, позволяющим извлекать информацию независимо от программного обеспечения." — DDC Methodology

Quick Start

import ifcopenshell
import ifcopenshell.util.element as element_util
import pandas as pd

# Open IFC file
ifc = ifcopenshell.open("model.ifc")

# Get project info
project = ifc.by_type("IfcProject")[0]
print(f"Project: {project.Name}")

# Extract all walls
walls = ifc.by_type("IfcWall")
print(f"Total walls: {len(walls)}")

# Get wall data
wall_data = []
for wall in walls:
    psets = element_util.get_psets(wall)
    wall_data.append({
        'GlobalId': wall.GlobalId,
        'Name': wall.Name,
        'Type': wall.is_a(),
        'Level': get_level(wall),
        'Properties': psets
    })

df = pd.DataFrame(wall_data)
print(df.head())

Core Extraction Functions

Element Extractor Class

import ifcopenshell
import ifcopenshell.util.element as element_util
import ifcopenshell.util.placement as placement_util
import ifcopenshell.geom
import pandas as pd
from typing import List, Dict, Optional, Any

class IFCExtractor:
    """Extract data from IFC files"""

    def __init__(self, ifc_path: str):
        self.model = ifcopenshell.open(ifc_path)
        self.settings = ifcopenshell.geom.settings()

    def get_project_info(self) -> Dict:
        """Extract project metadata"""
        project = self.model.by_type("IfcProject")[0]
        site = self.model.by_type("IfcSite")
        building = self.model.by_type("IfcBuilding")

        return {
            'project_id': project.GlobalId,
            'project_name': project.Name,
            'description': project.Description,
            'site_count': len(site),
            'building_count': len(building),
            'schema': self.model.schema
        }

    def get_all_elements(self, element_types: List[str] = None) -> pd.DataFrame:
        """Extract all elements of specified types"""
        if element_types is None:
            element_types = [
                'IfcWall', 'IfcSlab', 'IfcColumn', 'IfcBeam',
                'IfcDoor', 'IfcWindow', 'IfcStair', 'IfcRoof'
            ]

        all_elements = []

        for ifc_type in element_types:
            elements = self.model.by_type(ifc_type)

            for elem in elements:
                data = self._extract_element_data(elem)
                data['IFC_Type'] = ifc_type
                all_elements.append(data)

        return pd.DataFrame(all_elements)

    def _extract_element_data(self, element) -> Dict:
        """Extract data from single element"""
        # Basic info
        data = {
            'GlobalId': element.GlobalId,
            'Name': element.Name,
            'Description': element.Description,
            'ObjectType': element.ObjectType if hasattr(element, 'ObjectType') else None
        }

        # Get level/storey
        data['Level'] = self._get_element_level(element)

        # Get material
        data['Material'] = self._get_element_material(element)

        # Get type
        data['TypeName'] = self._get_element_type(element)

        # Get all property sets
        psets = element_util.get_psets(element)
        data['PropertySets'] = psets

        # Extract common quantities
        base_quantities = psets.get('BaseQuantities', {})
        data.update({
            'Length': base_quantities.get('Length'),
            'Width': base_quantities.get('Width'),
            'Height': base_quantities.get('Height'),
            'Area': base_quantities.get('NetSideArea') or base_quantities.get('GrossArea'),
            'Volume': base_quantities.get('NetVolume') or base_quantities.get('GrossVolume')
        })

        return data

    def _get_element_level(self, element) -> Optional[str]:
        """Get the building storey for an element"""
        if hasattr(element, 'ContainedInStructure'):
            for rel in element.ContainedInStructure or []:
                if rel.RelatingStructure.is_a('IfcBuildingStorey'):
                    return rel.RelatingStructure.Name
        return None

    def _get_element_material(self, element) -> Optional[str]:
        """Get material name for element"""
        if hasattr(element, 'HasAssociations'):
            for rel in element.HasAssociations or []:
                if rel.is_a('IfcRelAssociatesMaterial'):
                    material = rel.RelatingMaterial
                    if hasattr(material, 'Name'):
                        return material.Name
                    elif hasattr(material, 'ForLayerSet'):
                        layers = material.ForLayerSet.MaterialLayers
                        if layers:
                            return layers[0].Material.Name
        return None

    def _get_element_type(self, element) -> Optional[str]:
        """Get element type name"""
        if hasattr(element, 'IsTypedBy'):
            for rel in element.IsTypedBy or []:
                return rel.RelatingType.Name
        return None

    def extract_quantities(self) -> pd.DataFrame:
        """Extract quantities for all elements"""
        elements = self.get_all_elements()

        # Group by category and level
        quantities = elements.groupby(['IFC_Type', 'Level']).agg({
            'GlobalId': 'count',
            'Volume': 'sum',
            'Area': 'sum',
            'Length': 'sum'
        }).rename(columns={'GlobalId': 'Count'}).reset_index()

        return quantities

    def extract_levels(self) -> pd.DataFrame:
        """Extract building levels/storeys"""
        storeys = self.model.by_type("IfcBuildingStorey")

        level_data = []
        for storey in storeys:
            level_data.append({
                'GlobalId': storey.GlobalId,
                'Name': storey.Name,
                'Elevation': storey.Elevation,
                'Description': storey.Description
            })

        return pd.DataFrame(level_data).sort_values('Elevation')

    def extract_spaces(self) -> pd.DataFrame:
        """Extract spaces/rooms"""
        spaces = self.model.by_type("IfcSpace")

        space_data = []
        for space in spaces:
            psets = element_util.get_psets(space)
            base_qty = psets.get('BaseQuantities', {})

            space_data.append({
                'GlobalId': space.GlobalId,
                'Name': space.Name,
                'LongName': space.LongName,
                'Level': self._get_element_level(space),
                'Area': base_qty.get('NetFloorArea'),
                'Volume': base_qty.get('NetVolume'),
                'Height': base_qty.get('Height')
            })

        return pd.DataFrame(space_data)

    def extract_materials(self) -> pd.DataFrame:
        """Extract material summary"""
        materials = {}

        for elem in self.model.by_type("IfcProduct"):
            material = self._get_element_material(elem)
            if material:
                if material not in materials:
                    materials[material] = {'count': 0, 'volume': 0}

                materials[material]['count'] += 1

                psets = element_util.get_psets(elem)
                volume = psets.get('BaseQuantities', {}).get('NetVolume', 0)
                if volume:
                    materials[material]['volume'] += volume

        return pd.DataFrame.from_dict(materials, orient='index').reset_index()

    def extract_relationships(self) -> pd.DataFrame:
        """Extract element relationships"""
        relationships = []

        # Spatial containment
        for rel in self.model.by_type("IfcRelContainedInSpatialStructure"):
            for elem in rel.RelatedElements:
                relationships.append({
                    'Element': elem.GlobalId,
                    'Element_Type': elem.is_a(),
                    'Relationship': 'ContainedIn',
                    'Related_To': rel.RelatingStructure.GlobalId,
                    'Related_Type': rel.RelatingStructure.is_a()
                })

        # Aggregation
        for rel in self.model.by_type("IfcRelAggregates"):
            for part in rel.RelatedObjects:
                relationships.append({
                    'Element': part.GlobalId,
                    'Element_Type': part.is_a(),
                    'Relationship': 'PartOf',
                    'Related_To': rel.RelatingObject.GlobalId,
                    'Related_Type': rel.RelatingObject.is_a()
                })

        return pd.DataFrame(relationships)

Geometry Extraction

Extract Geometry Data

import numpy as np

class IFCGeometryExtractor:
    """Extract geometry data from IFC elements"""

    def __init__(self, ifc_path: str):
        self.model = ifcopenshell.open(ifc_path)
        self.settings = ifcopenshell.geom.settings()
        self.settings.set(self.settings.USE_WORLD_COORDS, True)

    def get_element_geometry(self, element) -> Dict:
        """Extract geometry for single element"""
        try:
            shape = ifcopenshell.geom.create_shape(self.settings, element)

            verts = shape.geometry.verts
            faces = shape.geometry.faces

            # Calculate bounding box
            vertices = np.array(verts).reshape(-1, 3)
            min_coords = vertices.min(axis=0)
            max_coords = vertices.max(axis=0)
            dimensions = max_coords - min_coords

            return {
                'GlobalId': element.GlobalId,
                'vertices_count': len(vertices),
                'faces_count': len(faces) // 3,
                'min_x': min_coords[0],
                'min_y': min_coords[1],
                'min_z': min_coords[2],
                'max_x': max_coords[0],
                'max_y': max_coords[1],
                'max_z': max_coords[2],
                'length': dimensions[0],
                'width': dimensions[1],
                'height': dimensions[2],
                'center_x': (min_coords[0] + max_coords[0]) / 2,
                'center_y': (min_coords[1] + max_coords[1]) / 2,
                'center_z': (min_coords[2] + max_coords[2]) / 2
            }
        except:
            return {'GlobalId': element.GlobalId, 'error': 'Geometry extraction failed'}

    def get_bounding_boxes(self, element_type: str) -> pd.DataFrame:
        """Get bounding boxes for all elements of type"""
        elements = self.model.by_type(element_type)
        boxes = [self.get_element_geometry(e) for e in elements]
        return pd.DataFrame(boxes)

    def calculate_volumes(self, element_type: str) -> pd.DataFrame:
        """Calculate volumes using geometry"""
        elements = self.model.by_type(element_type)
        volumes = []

        for elem in elements:
            try:
                shape = ifcopenshell.geom.create_shape(self.settings, elem)
                # Calculate volume from mesh (simplified)
                verts = np.array(shape.geometry.verts).reshape(-1, 3)
                bbox_volume = np.prod(verts.max(axis=0) - verts.min(axis=0))

                volumes.append({
                    'GlobalId': elem.GlobalId,
                    'Name': elem.Name,
                    'BBox_Volume': bbox_volume
                })
            except:
                pass

        return pd.DataFrame(volumes)

Export Functions

Export to Various Formats

class IFCExporter:
    """Export IFC data to various formats"""

    def __init__(self, extractor: IFCExtractor):
        self.extractor = extractor

    def to_excel(self, output_path: str, include_all: bool = True):
        """Export to Excel with multiple sheets"""
        with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
            # Project info
            project_info = pd.DataFrame([self.extractor.get_project_info()])
            project_info.to_excel(writer, sheet_name='Project', index=False)

            # All elements
            if include_all:
                elements = self.extractor.get_all_elements()
                elements.to_excel(writer, sheet_name='Elements', index=False)

            # Quantities
            quantities = self.extractor.extract_quantities()
            quantities.to_excel(writer, sheet_name='Quantities', index=False)

            # Levels
            levels = self.extractor.extract_levels()
            levels.to_excel(writer, sheet_name='Levels', index=False)

            # Spaces
            spaces = self.extractor.extract_spaces()
            spaces.to_excel(writer, sheet_name='Spaces', index=False)

            # Materials
            materials = self.extractor.extract_materials()
            materials.to_excel(writer, sheet_name='Materials', index=False)

        return output_path

    def to_csv(self, output_dir: str):
        """Export to multiple CSV files"""
        import os
        os.makedirs(output_dir, exist_ok=True)

        exports = {
            'elements.csv': self.extractor.get_all_elements(),
            'quantities.csv': self.extractor.extract_quantities(),
            'levels.csv': self.extractor.extract_levels(),
            'spaces.csv': self.extractor.extract_spaces(),
            'materials.csv': self.extractor.extract_materials()
        }

        for filename, df in exports.items():
            df.to_csv(os.path.join(output_dir, filename), index=False)

        return output_dir

    def to_json(self, output_path: str):
        """Export to JSON"""
        import json

        data = {
            'project': self.extractor.get_project_info(),
            'elements': self.extractor.get_all_elements().to_dict('records'),
            'quantities': self.extractor.extract_quantities().to_dict('records'),
            'levels': self.extractor.extract_levels().to_dict('records'),
            'materials': self.extractor.extract_materials().to_dict('records')
        }

        with open(output_path, 'w', encoding='utf-8') as f:
            json.dump(data, f, indent=2, default=str)

        return output_path

    def to_database(self, connection_string: str, table_prefix: str = 'ifc_'):
        """Export to SQL database"""
        from sqlalchemy import create_engine

        engine = create_engine(connection_string)

        tables = {
            f'{table_prefix}elements': self.extractor.get_all_elements(),
            f'{table_prefix}quantities': self.extractor.extract_quantities(),
            f'{table_prefix}levels': self.extractor.extract_levels(),
            f'{table_prefix}spaces': self.extractor.extract_spaces(),
            f'{table_prefix}materials': self.extractor.extract_materials()
        }

        for table_name, df in tables.items():
            # Remove complex columns for database storage
            simple_df = df.select_dtypes(exclude=['object']).copy()
            for col in df.columns:
                if df[col].dtype == 'object':
                    simple_df[col] = df[col].astype(str)

            simple_df.to_sql(table_name, engine, if_exists='replace', index=False)

        return list(tables.keys())

Quick Reference

Property Set Lookup

# Common IFC Property Sets
PSETS = {
    'Pset_WallCommon': ['IsExternal', 'LoadBearing', 'FireRating'],
    'Pset_SlabCommon': ['IsExternal', 'LoadBearing', 'AcousticRating'],
    'Pset_ColumnCommon': ['IsExternal', 'LoadBearing'],
    'Pset_BeamCommon': ['LoadBearing', 'FireRating'],
    'Pset_DoorCommon': ['FireRating', 'AcousticRating', 'SecurityRating'],
    'Pset_WindowCommon': ['ThermalTransmittance', 'GlazingType'],
    'BaseQuantities': ['Length', 'Width', 'Height', 'Area', 'Volume']
}

Resources

• IfcOpenShell: https://ifcopenshell.org
• IFC Standard: https://www.buildingsmart.org/standards/bsi-standards/industry-foundation-classes/
• DDC Website: https://datadrivenconstruction.io

Next Steps

• See bim-validation-pipeline for validating extracted data
• See qto-report for quantity take-off reports
• See 4d-simulation for linking to schedules