Daily Report Generator for Construction Sites
Automate the creation of comprehensive daily construction reports by aggregating data from multiple sources into professional documentation.
Business Case
Problem: Site managers spend 45-60 minutes daily on:
- Collecting information from foremen
- Checking weather conditions
- Compiling worker counts and hours
- Writing narrative summaries
- Formatting and distributing reports
Solution: Automated system that:
- Pulls data from Google Sheets/project database
- Integrates weather API data
- Aggregates worker timesheets
- Generates professional PDF reports
- Distributes to stakeholders automatically
ROI: 80% reduction in daily reporting time (45 min → 9 min for review)
Report Structure
┌──────────────────────────────────────────────────────────────────────┐ │ DAILY CONSTRUCTION REPORT │ │ │ │ Project: ЖК Солнечный, Корпус 2 Date: 24.01.2026 │ │ Report #: DCR-2026-024 Weather: ☁️ -5°C │ ├──────────────────────────────────────────────────────────────────────┤ │ │ │ 1. WEATHER CONDITIONS │ │ ┌────────────┬────────────┬────────────┬────────────┐ │ │ │ Morning │ Afternoon │ Evening │ Impact │ │ │ │ -8°C ☀️ │ -5°C ☁️ │ -7°C 🌙 │ Normal │ │ │ └────────────┴────────────┴────────────┴────────────┘ │ │ │ │ 2. WORKFORCE │ │ ┌────────────────────────────────────────────────────┐ │ │ │ Category │ Planned │ Actual │ Hours │ │ │ ├────────────────────────────────────────────────────┤ │ │ │ GC Supervision │ 3 │ 3 │ 27 │ │ │ │ Electrical │ 12 │ 11 │ 88 │ │ │ │ Plumbing │ 8 │ 8 │ 64 │ │ │ │ HVAC │ 6 │ 6 │ 48 │ │ │ │ TOTAL │ 29 │ 28 │ 227 │ │ │ └────────────────────────────────────────────────────┘ │ │ │ │ 3. WORK COMPLETED TODAY │ │ • Electrical: Completed rough-in floors 5-6 │ │ • Plumbing: Installed risers section A │ │ • HVAC: Ductwork installation 60% complete │ │ │ │ 4. WORK PLANNED FOR TOMORROW │ │ • Electrical: Begin rough-in floor 7 │ │ • Plumbing: Continue risers section B │ │ • HVAC: Complete ductwork, begin testing │ │ │ │ 5. ISSUES / DELAYS │ │ • Material delay: Electrical panels (ETA: 26.01) │ │ • Weather: Expected snow may delay exterior work │ │ │ │ 6. SAFETY │ │ ✅ No incidents │ │ ✅ Toolbox talk completed: Fall protection │ │ │ │ 7. PHOTOS │ │ [Photo 1: Floor 5 electrical] [Photo 2: Riser installation] │ │ │ │ ───────────────────────────────────────────────────────────────── │ │ Prepared by: Иван Петров, Site Manager │ │ Approved by: ___________________ │ │ Distribution: Owner, Architect, PM │ └──────────────────────────────────────────────────────────────────────┘
Python Implementation
pythonimport pandas as pd from datetime import datetime, date from typing import Optional, List, Dict import requests from reportlab.lib import colors from reportlab.lib.pagesizes import A4 from reportlab.platypus import SimpleDocTemplate, Table, TableStyle, Paragraph, Spacer, Image from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle from reportlab.lib.units import cm import os class DailyReportGenerator: """Generate professional daily construction reports""" def __init__(self, config: dict): self.config = config self.weather_api_key = config.get('weather_api_key') self.project_name = config.get('project_name') self.report_date = config.get('report_date', date.today()) def get_weather_data(self, location: str) -> dict: """Fetch weather data from API""" if not self.weather_api_key: return self._mock_weather() url = f"https://api.openweathermap.org/data/2.5/weather" params = { 'q': location, 'appid': self.weather_api_key, 'units': 'metric', 'lang': 'ru' } response = requests.get(url, params=params) if response.status_code == 200: data = response.json() return { 'temp': round(data['main']['temp']), 'description': data['weather'][0]['description'], 'humidity': data['main']['humidity'], 'wind_speed': round(data['wind']['speed']), 'icon': self._get_weather_icon(data['weather'][0]['main']) } return self._mock_weather() def _get_weather_icon(self, condition: str) -> str: icons = { 'Clear': '☀️', 'Clouds': '☁️', 'Rain': '🌧️', 'Snow': '❄️', 'Thunderstorm': '⛈️', 'Mist': '🌫️' } return icons.get(condition, '🌤️') def _mock_weather(self) -> dict: return { 'temp': -5, 'description': 'облачно', 'humidity': 65, 'wind_speed': 3, 'icon': '☁️' } def get_workforce_data(self, source: pd.DataFrame) -> dict: """Aggregate workforce data from timesheet""" # Expected columns: trade, worker_name, hours_worked, planned_hours summary = source.groupby('trade').agg({ 'worker_name': 'count', 'hours_worked': 'sum', 'planned_hours': 'sum' }).reset_index() summary.columns = ['trade', 'actual_count', 'actual_hours', 'planned_hours'] # Calculate planned count (assuming 8-hour shifts) summary['planned_count'] = (summary['planned_hours'] / 8).astype(int) return { 'trades': summary.to_dict('records'), 'total_workers': summary['actual_count'].sum(), 'total_hours': summary['actual_hours'].sum(), 'total_planned': summary['planned_count'].sum() } def get_work_completed(self, tasks: pd.DataFrame) -> List[dict]: """Extract completed work from task system""" # Filter completed tasks for today completed = tasks[ (tasks['date'] == self.report_date.strftime('%d.%m.%Y')) & (tasks['status'].isin(['Completed', 'Partial'])) ] work_items = [] for _, row in completed.iterrows(): work_items.append({ 'trade': row['trade'], 'description': row['description'], 'status': row['status'], 'notes': row.get('notes', '') }) return work_items def get_work_planned(self, tasks: pd.DataFrame) -> List[dict]: """Get planned work for tomorrow""" tomorrow = self.report_date + pd.Timedelta(days=1) planned = tasks[ tasks['date'] == tomorrow.strftime('%d.%m.%Y') ] work_items = [] for _, row in planned.iterrows(): work_items.append({ 'trade': row['trade'], 'description': row['description'], 'priority': row.get('priority', 'Medium') }) return work_items def get_issues(self, issues_log: pd.DataFrame) -> List[dict]: """Get active issues and delays""" active = issues_log[ (issues_log['status'] == 'Open') | (issues_log['date_reported'] == self.report_date.strftime('%d.%m.%Y')) ] return active[['category', 'description', 'impact', 'resolution_date']].to_dict('records') def get_safety_data(self, safety_log: pd.DataFrame) -> dict: """Get safety information for the day""" today_incidents = safety_log[ safety_log['date'] == self.report_date.strftime('%d.%m.%Y') ] return { 'incidents': len(today_incidents[today_incidents['type'] == 'Incident']), 'near_misses': len(today_incidents[today_incidents['type'] == 'Near Miss']), 'toolbox_talk': today_incidents[ today_incidents['type'] == 'Toolbox Talk' ]['topic'].tolist(), 'observations': today_incidents[ today_incidents['type'] == 'Observation' ]['description'].tolist() } def generate_report(self, data: dict, output_path: str) -> str: """Generate PDF report""" doc = SimpleDocTemplate( output_path, pagesize=A4, rightMargin=2*cm, leftMargin=2*cm, topMargin=2*cm, bottomMargin=2*cm ) styles = getSampleStyleSheet() title_style = ParagraphStyle( 'Title', parent=styles['Heading1'], fontSize=16, alignment=1, spaceAfter=12 ) heading_style = ParagraphStyle( 'Heading', parent=styles['Heading2'], fontSize=12, spaceBefore=12, spaceAfter=6 ) elements = [] # Title elements.append(Paragraph( f"DAILY CONSTRUCTION REPORT", title_style )) # Header info header_data = [ ['Project:', self.project_name, 'Date:', self.report_date.strftime('%d.%m.%Y')], ['Report #:', data.get('report_number', 'DCR-001'), 'Weather:', f"{data['weather']['icon']} {data['weather']['temp']}°C"] ] header_table = Table(header_data, colWidths=[3*cm, 6*cm, 3*cm, 4*cm]) header_table.setStyle(TableStyle([ ('FONTNAME', (0, 0), (-1, -1), 'Helvetica'), ('FONTSIZE', (0, 0), (-1, -1), 10), ('FONTNAME', (0, 0), (0, -1), 'Helvetica-Bold'), ('FONTNAME', (2, 0), (2, -1), 'Helvetica-Bold'), ])) elements.append(header_table) elements.append(Spacer(1, 12)) # Weather section elements.append(Paragraph("1. WEATHER CONDITIONS", heading_style)) weather = data['weather'] weather_text = f""" Temperature: {weather['temp']}°C | Humidity: {weather['humidity']}% | Wind: {weather['wind_speed']} m/s | Conditions: {weather['description']} """ elements.append(Paragraph(weather_text, styles['Normal'])) # Workforce section elements.append(Paragraph("2. WORKFORCE", heading_style)) workforce = data['workforce'] workforce_data = [['Trade', 'Planned', 'Actual', 'Hours']] for trade in workforce['trades']: workforce_data.append([ trade['trade'], str(trade['planned_count']), str(trade['actual_count']), str(int(trade['actual_hours'])) ]) workforce_data.append([ 'TOTAL', str(workforce['total_planned']), str(workforce['total_workers']), str(int(workforce['total_hours'])) ]) workforce_table = Table(workforce_data, colWidths=[6*cm, 3*cm, 3*cm, 3*cm]) workforce_table.setStyle(TableStyle([ ('BACKGROUND', (0, 0), (-1, 0), colors.grey), ('TEXTCOLOR', (0, 0), (-1, 0), colors.whitesmoke), ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'), ('FONTNAME', (0, -1), (-1, -1), 'Helvetica-Bold'), ('GRID', (0, 0), (-1, -1), 1, colors.black), ('ALIGN', (1, 0), (-1, -1), 'CENTER'), ])) elements.append(workforce_table) # Work completed elements.append(Paragraph("3. WORK COMPLETED TODAY", heading_style)) for item in data.get('work_completed', []): bullet = f"• {item['trade']}: {item['description']}" if item.get('notes'): bullet += f" ({item['notes']})" elements.append(Paragraph(bullet, styles['Normal'])) # Work planned elements.append(Paragraph("4. WORK PLANNED FOR TOMORROW", heading_style)) for item in data.get('work_planned', []): bullet = f"• {item['trade']}: {item['description']}" elements.append(Paragraph(bullet, styles['Normal'])) # Issues elements.append(Paragraph("5. ISSUES / DELAYS", heading_style)) issues = data.get('issues', []) if issues: for issue in issues: bullet = f"• {issue['category']}: {issue['description']}" if issue.get('resolution_date'): bullet += f" (ETA: {issue['resolution_date']})" elements.append(Paragraph(bullet, styles['Normal'])) else: elements.append(Paragraph("No significant issues reported.", styles['Normal'])) # Safety elements.append(Paragraph("6. SAFETY", heading_style)) safety = data.get('safety', {}) if safety.get('incidents', 0) == 0: elements.append(Paragraph("✅ No incidents reported", styles['Normal'])) else: elements.append(Paragraph(f"⚠️ {safety['incidents']} incident(s) reported", styles['Normal'])) if safety.get('toolbox_talk'): elements.append(Paragraph(f"✅ Toolbox talk: {', '.join(safety['toolbox_talk'])}", styles['Normal'])) # Signature block elements.append(Spacer(1, 24)) elements.append(Paragraph("─" * 60, styles['Normal'])) elements.append(Paragraph(f"Prepared by: {data.get('prepared_by', '_________________')}", styles['Normal'])) elements.append(Paragraph(f"Date: {datetime.now().strftime('%d.%m.%Y %H:%M')}", styles['Normal'])) # Build PDF doc.build(elements) return output_path # Usage Example def generate_daily_report( project_name: str, location: str, timesheet_path: str, tasks_path: str, output_dir: str ) -> str: """Generate daily report from source files""" # Initialize generator generator = DailyReportGenerator({ 'project_name': project_name, 'weather_api_key': os.environ.get('WEATHER_API_KEY'), 'report_date': date.today() }) # Load data timesheet = pd.read_excel(timesheet_path) tasks = pd.read_excel(tasks_path) # Compile report data report_data = { 'report_number': f"DCR-{date.today().strftime('%Y-%j')}", 'weather': generator.get_weather_data(location), 'workforce': generator.get_workforce_data(timesheet), 'work_completed': generator.get_work_completed(tasks), 'work_planned': generator.get_work_planned(tasks), 'issues': [], # Load from issues log if available 'safety': { 'incidents': 0, 'toolbox_talk': ['Fall Protection'], 'near_misses': 0 }, 'prepared_by': 'Site Manager' } # Generate PDF output_path = os.path.join( output_dir, f"Daily_Report_{date.today().strftime('%Y%m%d')}.pdf" ) return generator.generate_report(report_data, output_path) if __name__ == "__main__": report_path = generate_daily_report( project_name="ЖК Солнечный, Корпус 2", location="Moscow,RU", timesheet_path="timesheet.xlsx", tasks_path="tasks.xlsx", output_dir="./reports" ) print(f"Report generated: {report_path}")
Data Sources Integration
From n8n Project Management System
python# Connect to Google Sheets used by n8n bot def get_data_from_project_management(spreadsheet_id: str) -> dict: """Pull data from n8n project management system""" import gspread gc = gspread.service_account() sh = gc.open_by_key(spreadsheet_id) # Get completed tasks tasks_sheet = sh.worksheet('Tasks') tasks = pd.DataFrame(tasks_sheet.get_all_records()) # Get workforce from worker responses workers_sheet = sh.worksheet('Workers') workers = pd.DataFrame(workers_sheet.get_all_records()) return { 'tasks': tasks, 'workers': workers }
From Timesheet System
python# Integrate with common timesheet formats def import_timesheet(source: str, format: str = 'excel') -> pd.DataFrame: """Import timesheet data from various sources""" if format == 'excel': df = pd.read_excel(source) elif format == 'csv': df = pd.read_csv(source) elif format == 'procore': df = fetch_procore_timesheet(source) # Standardize columns df = df.rename(columns={ 'Trade': 'trade', 'Worker': 'worker_name', 'Hours': 'hours_worked', 'Planned Hours': 'planned_hours' }) return df
n8n Workflow for Automation
yamlname: Daily Report Automation trigger: type: cron expression: "0 18 * * 1-6" # 6 PM daily steps: - collect_task_data: node: Google Sheets operation: readRows sheet: Tasks filter: Date = TODAY() - collect_timesheet: node: Google Sheets operation: readRows sheet: Timesheet filter: Date = TODAY() - get_weather: node: HTTP Request url: "https://api.openweathermap.org/data/2.5/weather" params: q: "Moscow,RU" appid: "{{$env.WEATHER_API_KEY}}" - generate_report: node: Code (Python) code: | from daily_report import generate_report return generate_report(items) - upload_to_drive: node: Google Drive operation: upload file: "={{$json.report_path}}" folder: "Daily Reports" - send_notification: node: Telegram operation: sendDocument chatId: "MANAGERS_GROUP_ID" document: "={{$json.drive_url}}" caption: | 📋 Daily Report - {{$now.format('DD.MM.YYYY')}} Workforce: {{$json.total_workers}} workers Tasks completed: {{$json.completed_tasks}} Issues: {{$json.open_issues}}
Report Distribution
pythondef distribute_report(report_path: str, recipients: dict): """Distribute report to stakeholders""" # Email distribution for email in recipients.get('email', []): send_email( to=email, subject=f"Daily Report - {date.today().strftime('%d.%m.%Y')}", body="Please find attached the daily construction report.", attachment=report_path ) # Telegram distribution for chat_id in recipients.get('telegram', []): send_telegram_document( chat_id=chat_id, document_path=report_path, caption=f"📋 Daily Report - {date.today().strftime('%d.%m.%Y')}" ) # Upload to project portal if portal_url := recipients.get('portal'): upload_to_portal(portal_url, report_path)
Best Practices
- Data Collection: Set up automated data collection to minimize manual input
- Review Time: Allow 5-10 minutes for manager review before distribution
- Photos: Include 3-5 key photos showing progress
- Issues: Be specific about impacts and resolution dates
- Distribution: Send by 6-7 PM to allow stakeholder review
"A good daily report tells the story of the day in 2 minutes or less."
