经 AI Skill Hub 精选评估,cad2data-Revit-IFC-DWG-DGN n8n工作流 获评「推荐使用」。这款n8n工作流在功能完整性、社区活跃度和易用性方面表现出色,AI 评分 7.5 分,适合有一定技术背景的用户使用。
基于n8n的开源工作流,自动化CAD文件转换,支持Revit IFC DWG DGN等格式,提高工作效率和准确性。
cad2data-Revit-IFC-DWG-DGN n8n工作流 是一款基于 Jupyter Notebook 开发的开源工具,专注于 n8n、ai-agents、autocad 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。
基于n8n的开源工作流,自动化CAD文件转换,支持Revit IFC DWG DGN等格式,提高工作效率和准确性。
cad2data-Revit-IFC-DWG-DGN n8n工作流 是一款基于 Jupyter Notebook 开发的开源工具,专注于 n8n、ai-agents、autocad 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。
# 克隆仓库 git clone https://github.com/datadrivenconstruction/cad2data-Revit-IFC-DWG-DGN cd cad2data-Revit-IFC-DWG-DGN # 查看安装说明 cat README.md # 按 README 完成环境依赖安装后即可使用
# 查看帮助 cad2data-revit-ifc-dwg-dgn --help # 基本运行 cad2data-revit-ifc-dwg-dgn [options] <input> # 详细使用说明请查阅文档 # https://github.com/datadrivenconstruction/cad2data-Revit-IFC-DWG-DGN
// n8n 工作流配置步骤 // 1. 在 n8n 中点击 "Import Workflow" // 2. 粘贴 JSON 文件内容或上传文件 // 3. 配置必要的 Credentials: // - Settings → Credentials → New // - 选择对应服务类型填写 API Key // 4. 激活工作流 (Toggle ON) // 5. 通过 Webhook 或定时触发器运行
<p align="center"> <a href="README.md">🇬🇧 English</a> • <a href="README.de.md">🇩🇪 Deutsch</a> • <a href="README.es.md">🇪🇸 Español</a> • <a href="README.fr.md">🇫🇷 Français</a> • <a href="README.ru.md">🇷🇺 Русский</a> • <a href="README.zh.md">🇨🇳 中文</a> • <a href="README.ar.md">🇸🇦 العربية</a> </p>
<p align="center"> <img src="https://github.com/datadrivenconstruction/cad2data-Revit-IFC-DWG-DGN/blob/main/DDC_in_additon/DDC_readme_content/CAD%20BIM%20Pipeline%20and%20Workflow.jpg" alt="Pipeline Overview" width="100%"/> </p> <p align="center">
<a href="LICENSE"> <img src="https://img.shields.io/badge/license-dual%3A%20MIT%20%2B%20proprietary-blue" alt="Dual License: MIT (workflows, scripts, docs) + Proprietary (binary converters)"> </a> <a href="https://datadrivenconstruction.io"> <img src="https://img.shields.io/badge/powered%20by-DataDrivenConstruction.io-orange" alt="Powered by DataDrivenConstruction.io"> </a> <img src="https://img.shields.io/badge/input-.rvt%20.dwg%20.ifc%20.dgn-blue?logo=autodesk&logoColor=white" alt="Input Formats"></br> <img src="https://img.shields.io/badge/output-.xlsx%20.csv%20.dae%20.html%20.pdf%20.ifc-green?logo=microsoft-excel&logoColor=white" alt="Output Formats"> <img src="https://img.shields.io/badge/ETL%20pipeline-Ready%20for%20CI/CD%20&%20Bots-success?logo=githubactions" alt="ETL Pipeline">
<a href="https://dify.ai/pricing" target="_blank"> <img alt="Static Badge" src="https://img.shields.io/badge/free-pricing?logo=free&color=%23155EEF&label=pricing&labelColor=%23528bff"> </a> </br>
<p align="center"> Automate your CAD/BIM data extraction and transformation using DDC UI, ComandPromts, Powershell or Workflows with no vendor lock-in, no Autodesk® or CAD licenses, and full control of your project data </p>
<p align="center"> <img src="https://github.com/datadrivenconstruction/cad2data-Revit-IFC-DWG-DGN/blob/main/DDC_in_additon/DDC_readme_content/DDC_GithubLogo.jpg" alt="Pipeline Overview" width="100%"/> </p> <p align="center"> <img src="https://datadrivenconstruction.io/wp-content/uploads/2025/09/bandicam-2025-09-14-13-05-05-897.gif" width="100%"/> <p align="center"> DataDrivenConstruction clients and users <br> <a href="https://datadrivenconstruction.io/"> <img src="https://github.com/datadrivenconstruction/cad2data-Revit-IFC-DWG-DGN/blob/main/DDC_in_additon/DDC_readme_content/Clients_DataDrivenConstruction_logos.png" width="95%"/> </a> <br></br> </p>
This pipeline automates the conversion of CAD/BIM files to Excel for quantity takeoffs, data analysis, and further processing. It supports offline operation and extensibility with Python or AI tools.
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⭐ <b>If you find our tools useful and would like to see more similar applications for the construction industry, please give our repositories a star.</b> Star DDC workflow on GitHub and be instantly notified of new releases. <p align="center"> <br> <img src="https://github.com/datadrivenconstruction/cad2data-Revit-IFC-DWG-DGN/blob/main/DDC_in_additon/DDC_readme_content/GitHub%20Star%20DDC.gif" width="100%"/> <br></br> </p>
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1. Install Node.js from nodejs.org. 2. Start n8n in Command Prompt:
npx n8n
Access at http://localhost:5678. 3. Download this repository from GitHub - Click the green "Code" button → "Download ZIP" - Unzip the folder 4. Run the Workflow - You're ready. Just click Execute Workflow in n8n to start process your CAD-BIM files <p align="center"> <img src="https://datadrivenconstruction.io/wp-content/uploads/2025/07/Install-Nodejs-and-n8n.png" alt="Pipeline Overview" width="100%"/> <br></br> </p>
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The key advantage of CLI tools is that AI can use them directly.
Modern AI coding assistants (Claude Code, Cursor, GitHub Copilot, Windsurf, Aider, Cline) can execute shell commands, read documentation, and build complete automation pipelines autonomously. This means:
You don't need to write code yourself — just describe what you want, and AI will integrate DDC converters into your workflow.
How it works: 1. Copy this documentation (or point AI to this README) 2. Describe your task in natural language: "Convert all Revit files in folder X to Excel, then analyze wall quantities" 3. AI reads the CLI syntax, writes the script, executes it, and processes the results
What AI can do with DDC converters: - ✅ Batch convert hundreds of CAD/BIM files automatically - ✅ Build ETL pipelines: Revit → Excel → Database → Dashboard - ✅ Create validation scripts that check BIM data quality - ✅ Generate reports from extracted data (PDF, HTML, Excel) - ✅ Integrate conversions into CI/CD pipelines - ✅ Chain multiple tools: convert → validate → classify → estimate costs - ✅ Schedule automated processing via cron/Task Scheduler
Example prompt for AI assistant:
I have Revit files in C:\Projects. Using DDC RvtExporter.exe located at C:\DDC\,
convert all .rvt files to Excel with bounding boxes, then create a Python script
that reads the XLSX files and generates a summary report of all wall types and their volumes.
The AI will: 1. Scan the folder for .rvt files 2. Execute RvtExporter.exe for each file with correct parameters 3. Write Python code to parse the resulting .xlsx files 4. Generate the summary report
This transforms DDC from a tool into an AI-native building block for construction data automation.
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Universal CAD/BIM Converter Overview
Introduction to the DDC Converter for Revit, IFC, DWG, and DGN pipelines – conversion, validation, and automation use cases. Watch Converter Overview on YouTube |
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DWG to Excel Converter Pipeline
Step-by-step guide on automating DWG to Excel data conversion using n8n, making CAD project data easy to use in reporting and analysis.Watch DWG to Excel Pipeline on YouTube |
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ETL with Revit and IFC
Learn how to build a complete ETL pipeline with Revit and IFC data: extract, transform, validate, and load project information into open formats. Watch ETL with Revit and IFC Tutorial on YouTube |
| Category | Capabilities |
|---|---|
| **BIM Processing** | IFC parsing, Revit data extraction, DWG/DGN conversion |
| **QTO Automation** | Quantity takeoffs, material schedules, cost linking |
| **Validation** | Model checking, data quality reports, parameter fill rates |
| **Reporting** | Daily reports, photo reports, progress tracking |
| **Cost Estimation** | Automated estimates using DDC CWICR database |
| **Integration** | n8n workflows, Excel sync, API connections |
🔗 Powered by DDC CWICR Database: OpenConstructionEstimate-DDC-CWICR
The cost estimation workflows connect to the DDC CWICR cost database containing 55,719 work items and 27,672 resources with detailed price breakdowns across 10+ regional markets. The resource-based methodology separates physical norms (labor hours, material quantities, equipment time) from volatile prices, ensuring transparent and auditable estimates.
📦 Database Downloads: DDC CWICR Releases — Excel, Parquet, CSV, Qdrant snapshots 🌐 Live Demo: openconstructionestimate.com — explore the database and semantic search
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#### ⚡️ 6.1 Construction Price Estimation Pipeline for Revit and IFC with LLM (AI) File: n8n_6_Construction_Price_Estimation_Pipeline.json
Automates cost estimation for building elements from CAD/BIM files. Uses AI to classify materials, search market prices, and generate comprehensive cost reports.
##### Key Features - AI Classification: Materials across EU/DE/US standards - Smart Pricing: Region-specific databases with fallbacks - Cost Analysis: Total costs, cost per unit, top 10 groups - Multi-Format Output: Excel workbook + HTML report with charts
<p align="center"> <img src="https://datadrivenconstruction.io/wp-content/uploads/2025/08/n8n_Construction_Price_Estimation_with_LLM_for_Revt_and_IFC-2.jpg" alt="Price Estimation" width="100%"/> </p>
##### Installation 1. Import Construction_Price_Estimation_Pipeline.json into n8n 2. Configure AI credentials (OpenAI/Anthropic) 3. Update Set Parameters node:
input_file_path: C:\Output\Project_Elements.xlsx
grouping_parameter: Type Name )
country: Germany
- Grouping parameter (group_by, e.g. 'Type Name', 'IfcType' for IFC or other) - Country (country for which the values will be calculated, e.g. 'Germany'or 'Brazil')
⏱️ Processing Time: 5-15 seconds per element group (depends on LLM speed)
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#### ⚡️ 6.2 CAD (BIM) Cost Estimation Pipeline 4D/5D with DDC CWICR File: n8n_4_CAD_(BIM)_Cost_Estimation_Pipeline_4D_5D_with_DDC_CWICR.json
🔗 Workflow Repository: OpenConstructionEstimate-DDC-CWICR
Automated cost estimation from Revit/IFC/DWG models. Extracts BIM data, classifies elements, decomposes into work items, and generates 4D/5D estimates with full resource breakdown.
<p align="left"> <a href="https://datadrivenconstruction.io"> <img src="https://github.com/datadrivenconstruction/cad2data-Revit-IFC-DWG-DGN/blob/main/DDC_in_additon/DDC_readme_content/CAD%20(Revit)%20to%205D-4D%20Cost%20and%20Time%20Estimate.jpg" alt="CAD to 5D-4D Cost Estimation" width="100%"> </a> </p>
| Stage | Name | Description |
|---|---|---|
| **0** | Collect BIM Data | Extract elements from Revit via DDC Converter |
| **1** | Project Detection | AI identifies project type (Residential, Commercial, etc.) |
| **2** | Phase Generation | AI creates construction phases |
| **3** | Element Assignment | AI maps BIM types to phases |
| **4** | Work Decomposition | AI breaks types into work items ("Brick Wall" → masonry, mortar) |
| **5** | Vector Search | Find matching rates in DDC CWICR via Qdrant |
| **6** | Unit Mapping | Convert BIM units to rate units |
| **7** | Cost Calculation | Qty × Unit Price for each work item |
| **7.5** | Validation | CTO review for completeness and duplicates |
| **8** | Aggregation | Sum by phases and categories |
| **9** | Report Generation | Create HTML and Excel outputs |
##### Key Features - Full BIM Integration: Native support for Revit, IFC, DWG via DDC Converter - AI-Powered Decomposition: Breaks complex BIM types into constituent work items - Semantic Pricing: Qdrant vector search with 55,719 pre-embedded work items - Multi-LLM Support: OpenAI GPT-4o, Claude, Gemini 2.5 Pro, xAI Grok, DeepSeek - CTO Validation: AI review stage checks completeness and catches duplicates - 9 Languages: AR, DE, EN, ES, FR, HI, PT, RU, ZH with regional pricing
| Component | Requirement | Description |
|---|---|---|
| **n8n** | v1.0+ (self-hosted) | Workflow automation platform |
| **Qdrant** | Cloud or self-hosted | Vector database for semantic search |
| **OpenAI API** | text-embedding-3-large | Generates embeddings for matching |
| **LLM API** | OpenAI / Claude / Gemini / Grok | AI models for classification |
| **DDC Converter** | RvtExporter.exe | Extracts BIM data to Excel |
| Code | Language | Price Level | Currency | Qdrant Collection |
|---|---|---|---|---|
AR | Arabic | Dubai | AED | ddc_cwicr_ar |
DE | German | Berlin | EUR | ddc_cwicr_de |
EN | English | Toronto | CAD | ddc_cwicr_en |
ES | Spanish | Barcelona | EUR | ddc_cwicr_es |
FR | French | Paris | EUR | ddc_cwicr_fr |
HI | Hindi | Mumbai | INR | ddc_cwicr_hi |
PT | Portuguese | São Paulo | BRL | ddc_cwicr_pt |
RU | Russian | St. Petersburg | RUB | ddc_cwicr_ru |
ZH | Chinese | Shanghai | CNY | ddc_cwicr_zh |
Reports are saved to the project folder:
project_YYYY-MM-DD.html ← Interactive report (opens in browser)
project_YYYY-MM-DD.xls ← Excel-compatible spreadsheet
<p align="center"> <img src="https://github.com/datadrivenconstruction/cad2data-Revit-IFC-DWG-DGN/blob/main/DDC_in_additon/DDC_readme_content/The%20generated%20report%20includes.jpg" width="100%"/> </p>
The workflow supports multiple AI providers. Enable your preferred model:
| Model | Status |
|---|---|
| OpenAI GPT-4o | ✅ Default |
| Claude Opus 4 | Disabled |
| Gemini 2.5 Pro | Disabled |
| xAI Grok | Disabled |
| DeepSeek | Disabled |
To switch models: Enable the desired model node and Disable others.
⏱️ Processing Time: Varies by project size and LLM model
In n8n versions 1.98.0–1.101.x, the os module is blocked, affecting libraries like pandas. Solution: Use the latest version with npx n8n@latest.
本项目提供了一套自动化的 CAD/BIM 数据转换流水线,能够将 Revit、IFC、DWG 及 DGN 等格式的文件高效转换为 Excel 表格,用于工程量统计(Quantity Takeoff)、数据分析及后续处理。该工具支持离线运行,并具备极强的扩展性,开发者可以通过 Python 或 AI 工具轻松进行二次开发。
核心功能包括:实现 CAD/BIM 文件的自动化 Excel 转换,将构件映射为行、属性映��为列;支持导出带有元素 ID 匹配的 3D 多边形几何数据(DAE 格式);支持完全离线处理,无需依赖互联网、API 或额外的授权许可;同时具备高度的可扩展性,方便用户进行自定义的后期数据处理。
在使用本项目前,请确保您的系统已完成以下准备工作:1. 从 nodejs.org 下载并安装最新的 Node.js 环境;2. 在命令行中使用 `npx n8n` 命令启动 n8n 工作流引擎,并通过 `http://localhost:5678` 进行访问;3. 从 GitHub 下载本项目仓库的 ZIP 压缩包并完成解压。
本项目采用 CLI(命令行界面)设计,旨在让 AI 能够直接驱动。通过 CLI 模式,现代 AI 编程助手(如 Claude Code、Cursor、GitHub Copilot、Windsurf、Aider、Cline)可以直接执行 Shell 命令、读取文档并自主构建完整的自动化流水线。您无需编写复杂的代码,只需向 AI 描述需求,即可将 DDC 转换工具集成到您的工作流中。
本项目提供了直观的视频教程,帮助用户快速上手。通过 CLI 工具,您可以从 PowerShell、Batch 脚本或任何自动化环境中调用转换命令。例如,当您向 AI 发出“转换我的 Revit 模型”指令时,AI 会自动执行类似 `RvtExporter.exe "C:\Projects\Model.rvt" complete bbox schedule` 的命令来完成任务。
用户可以通过命令行参数进行高级配置。例如,在进行 Revit 转 IFC 的转换时,可以指定 `config` 参数来控制导出模式(如 `ExportBaseQuantities=true` 或 `SitePlacement=Shared`)。系统支持多种导出粒度,包括基础模式(309 类)、标准模式(724 类)以及包含所有 1209 类属性的完整模式,并可选择性输出 Bounding Box、Revit Schedules 或 PDF 图纸。
DDC 转换器是功能完备的命令行工具(CLI),能够无缝集成到任何自动化工作流中。无论是编写脚本、构建 CI/CD 流水线、开发 AI Agents,还是在低代码平台中使用,这些工具都能提供稳定、标准的数据转换接口,是构建智能化建筑工程应用的理想核心。
本项目是 AI 驱动应用的“燃料”。通过克隆仓库并向 AI 描述需求,您可以快速构建造价估算机器人、自动化施工工作流或智能助手。其模块化能力涵盖了 BIM 处理(IFC 解析、Revit 数据提取)、QTO 自动化(工程量统计、材料明细)、模型校验(数据质量报告)以及报表生成等全方位技能。
本项目包含详细的故障排除指南。如果您在转换过程中遇到文件路径错误、权限问题或格式不兼容等情况,可以参考 FAQ 章节提供的解决方案。对于复杂的转换逻辑或自定义配置需求,建议结合 CLI 参数说明进行排查。
该工具基于n8n的开源工作流,自动化CAD文件转换,支持Revit IFC DWG DGN等格式,提高工作效率和准确性,值得推荐。
该工具使用 NOASSERTION 协议,商用场景请仔细阅读协议条款,必要时咨询法律意见。
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
📄 NOASSERTION — 请查阅原始协议条款了解具体使用限制。
AI Skill Hub 点评:cad2data-Revit-IFC-DWG-DGN n8n工作流 的核心功能完整,质量良好。对于n8n 平台用户来说,这是一个值得纳入个人工具库的选择。建议先在非生产环境试用,再逐步推广。
| 原始名称 | cad2data-Revit-IFC-DWG-DGN |
| 原始描述 | 开源n8n工作流:Workflow for AI Agents enables automated conversion of CAD files (such as `.rvt`。⭐363 · Jupyter Notebook |
| Topics | n8nai-agentsautocadautodeskbim |
| GitHub | https://github.com/datadrivenconstruction/cad2data-Revit-IFC-DWG-DGN |
| License | NOASSERTION |
| 语言 | Jupyter Notebook |
收录时间:2026-05-14 · 更新时间:2026-05-16 · License:NOASSERTION · AI Skill Hub 不对第三方内容的准确性作法律背书。
选择 Agent 类型,复制安装指令后粘贴到对应客户端