Create 4D construction simulations by linking BIM elements to project schedules. Generate time-based visualizations, sequence analysis, and construction phasing with Gantt integration.
日本語の概要は準備中です。原文の説明を表示しています。
Generate professional cost estimation reports from CWICR calculations. HTML, PDF, Excel outputs with charts and breakdowns.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Generate professional cost reports from CWICR calculations - executive summaries, detailed breakdowns, charts, and export to multiple formats.
import pandas as pd
from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
from pathlib import Path
import json
@dataclass
class ReportSection:
"""Report section content."""
title: str
content: str
chart_type: Optional[str] = None
chart_data: Optional[Dict] = None
@dataclass
class CostReport:
"""Complete cost report."""
project_name: str
generated_date: datetime
total_cost: float
currency: str
sections: List[ReportSection]
line_items: List[Dict]
summary: Dict[str, Any]
class CWICRReportGenerator:
"""Generate cost estimation reports."""
def __init__(self, project_name: str = "Project",
currency: str = "USD"):
self.project_name = project_name
self.currency = currency
self.sections: List[ReportSection] = []
self.line_items: List[Dict] = []
def add_summary(self, summary_data: Dict[str, float]):
"""Add executive summary section."""
content = f"""
<div class="summary-box">
<h3>Cost Summary</h3>
<table class="summary-table">
<tr><td>Labor</td><td class="amount">${summary_data.get('labor', 0):,.2f}</td></tr>
<tr><td>Materials</td><td class="amount">${summary_data.get('material', 0):,.2f}</td></tr>
<tr><td>Equipment</td><td class="amount">${summary_data.get('equipment', 0):,.2f}</td></tr>
<tr><td>Overhead</td><td class="amount">${summary_data.get('overhead', 0):,.2f}</td></tr>
<tr><td>Profit</td><td class="amount">${summary_data.get('profit', 0):,.2f}</td></tr>
<tr class="total"><td>TOTAL</td><td class="amount">${summary_data.get('total', 0):,.2f}</td></tr>
</table>
</div>
"""
self.sections.append(ReportSection(
title="Executive Summary",
content=content,
chart_type="pie",
chart_data={
'labels': ['Labor', 'Materials', 'Equipment', 'Overhead', 'Profit'],
'values': [
summary_data.get('labor', 0),
summary_data.get('material', 0),
summary_data.get('equipment', 0),
summary_data.get('overhead', 0),
summary_data.get('profit', 0)
]
}
))
def add_breakdown_by_category(self, breakdown: Dict[str, float]):
"""Add breakdown by category section."""
rows = ""
for category, cost in sorted(breakdown.items(), key=lambda x: -x[1]):
rows += f"<tr><td>{category}</td><td class='amount'>${cost:,.2f}</td></tr>"
content = f"""
<table class="detail-table">
<thead><tr><th>Category</th><th>Cost</th></tr></thead>
<tbody>{rows}</tbody>
</table>
"""
self.sections.append(ReportSection(
title="Cost by Category",
content=content,
chart_type="bar",
chart_data={
'labels': list(breakdown.keys()),
'values': list(breakdown.values())
}
))
def add_line_items(self, items: List[Dict]):
"""Add detailed line items."""
self.line_items = items
rows = ""
for item in items[:50]: # Limit for report
rows += f"""
<tr>
<td>{item.get('code', '')}</td>
<td>{item.get('description', '')[:50]}</td>
<td>{item.get('quantity', 0):,.2f}</td>
<td>{item.get('unit', '')}</td>
<td class="amount">${item.get('unit_price', 0):,.2f}</td>
<td class="amount">${item.get('total', 0):,.2f}</td>
</tr>
"""
content = f"""
<table class="line-items">
<thead>
<tr>
<th>Code</th>
<th>Description</th>
<th>Qty</th>
<th>Unit</th>
<th>Unit Price</th>
<th>Total</th>
</tr>
</thead>
<tbody>{rows}</tbody>
</table>
"""
self.sections.append(ReportSection(
title="Line Items",
content=content
))
def generate_html(self) -> str:
"""Generate HTML report."""
sections_html = ""
for section in self.sections:
sections_html += f"""
<section class="report-section">
<h2>{section.title}</h2>
{section.content}
</section>
"""
html = f"""
<!DOCTYPE html>
<html>
<head>
<title>Cost Report - {self.project_name}</title>
<style>
body {{ font-family: 'Segoe UI', Arial, sans-serif; margin: 40px; background: #f5f5f5; }}
.report-container {{ max-width: 1200px; margin: 0 auto; background: white; padding: 40px; box-shadow: 0 2px 10px rgba(0,0,0,0.1); }}
h1 {{ color: #2c3e50; border-bottom: 3px solid #3498db; padding-bottom: 10px; }}
h2 {{ color: #34495e; margin-top: 30px; }}
.summary-box {{ background: #ecf0f1; padding: 20px; border-radius: 8px; }}
table {{ width: 100%; border-collapse: collapse; margin: 20px 0; }}
th, td {{ padding: 12px; text-align: left; border-bottom: 1px solid #ddd; }}
th {{ background: #3498db; color: white; }}
.amount {{ text-align: right; font-family: monospace; }}
.total {{ font-weight: bold; background: #f8f9fa; }}
.line-items td {{ font-size: 0.9em; }}
.meta {{ color: #7f8c8d; font-size: 0.9em; margin-bottom: 20px; }}
</style>
</head>
<body>
<div class="report-container">
<h1>Cost Estimation Report</h1>
<div class="meta">
<p>Project: {self.project_name}</p>
<p>Generated: {datetime.now().strftime('%Y-%m-%d %H:%M')}</p>
<p>Currency: {self.currency}</p>
</div>
{sections_html}
<footer style="margin-top: 40px; color: #95a5a6; text-align: center;">
Generated by DDC CWICR | DataDrivenConstruction.io
</footer>
</div>
</body>
</html>
"""
return html
def save_html(self, output_path: str) -> str:
"""Save HTML report to file."""
html = self.generate_html()
with open(output_path, 'w', encoding='utf-8') as f:
f.write(html)
return output_path
def generate_excel(self, output_path: str) -> str:
"""Generate Excel report."""
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
# Summary sheet
if self.sections:
summary_data = []
for section in self.sections:
if section.chart_data:
for i, label in enumerate(section.chart_data.get('labels', [])):
summary_data.append({
'Category': label,
'Amount': section.chart_data.get('values', [])[i]
})
if summary_data:
pd.DataFrame(summary_data).to_excel(
writer, sheet_name='Summary', index=False)
# Line items sheet
if self.line_items:
pd.DataFrame(self.line_items).to_excel(
writer, sheet_name='Line Items', index=False)
return output_path
def generate_json(self) -> str:
"""Generate JSON report."""
report = {
'project_name': self.project_name,
'generated_date': datetime.now().isoformat(),
'currency': self.currency,
'sections': [
{
'title': s.title,
'chart_data': s.chart_data
} for s in self.sections
],
'line_items': self.line_items
}
return json.dumps(report, indent=2)
class QuickReport:
"""Quick report generation from cost data."""
@staticmethod
def from_dataframe(df: pd.DataFrame,
project_name: str = "Project") -> CWICRReportGenerator:
"""Generate report from cost DataFrame."""
gen = CWICRReportGenerator(project_name)
# Calculate summary
summary = {
'labor': df['labor_cost'].sum() if 'labor_cost' in df.columns else 0,
'material': df['material_cost'].sum() if 'material_cost' in df.columns else 0,
'equipment': df['equipment_cost'].sum() if 'equipment_cost' in df.columns else 0,
'overhead': df['overhead_cost'].sum() if 'overhead_cost' in df.columns else 0,
'profit': df['profit_cost'].sum() if 'profit_cost' in df.columns else 0,
'total': df['total_cost'].sum() if 'total_cost' in df.columns else 0
}
gen.add_summary(summary)
# Category breakdown
if 'category' in df.columns and 'total_cost' in df.columns:
breakdown = df.groupby('category')['total_cost'].sum().to_dict()
gen.add_breakdown_by_category(breakdown)
# Line items
items = df.to_dict('records')
gen.add_line_items(items)
return gen
# Create report generator
gen = CWICRReportGenerator("Office Building", currency="EUR")
# Add sections
gen.add_summary({
'labor': 125000,
'material': 350000,
'equipment': 75000,
'overhead': 82500,
'profit': 63250,
'total': 695750
})
# Save reports
gen.save_html("cost_report.html")
gen.generate_excel("cost_report.xlsx")
# Quick report from cost DataFrame
report = QuickReport.from_dataframe(cost_df, "My Project")
report.save_html("quick_report.html")
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概要と使いどころ
Create 4D construction simulations by linking BIM elements to project schedules. Generate time-based visualizations, sequence analysis, and construction phasing with Gantt integration.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze 5000+ IFC and Revit projects at scale for patterns, benchmarks, and insights. Big data analysis for construction.
日本語の概要は準備中です。原文の説明を表示しています。
Check construction AI systems against the EU AI Act (Regulation 2024/1689): classify risk of estimation, scheduling, CV and agent tools, document transparency, keep human oversight. Use when deploying or auditing AI in construction.
日本語の概要は準備中です。原文の説明を表示しています。
Orchestrate multiple AI agents for construction workflows: estimator, scheduler, document, QA and safety agents coordinated by a supervisor agent, with human checkpoints. Use when automating end-to-end project processes with agentic AI.
日本語の概要は準備中です。原文の説明を表示しています。
Create Apache Airflow DAGs for construction data pipelines. Orchestrate ETL, validation, and reporting workflows.
日本語の概要は準備中です。原文の説明を表示しています。
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日本語の概要は準備中です。原文の説明を表示しています。