🤖 Day 55: Automated Reporting and Scripting for Dredging
🤖 DAY 55: AUTOMATED DREDGING REPORTING & SCRIPTING
⏱️ Estimated Reading Time: 18 Minutes | 🎓 Level: Professional Hydrographer / Data Automation
From Manual Excel to Python‑Powered Real‑Time Dashboards and Alerts
Instructor: Engr. Rokib Hossain | River Warrior Academy
📖 Table of Contents (Serialised)
- Why Automate Dredging Reporting?
- Data Sources: Meter Logs, Surveys, Sensor APIs
- Python Script Example: Daily Production Report
- Interactive Script Simulator (Run in Your Mind)
- Building a Live Dashboard (Power BI / Plotly Dash)
- Automated Email Alerts for Exceedances
- Integrating with Hypack / PDS API
- Case Study: Jamuna River Automated Reporting
- Automation Checklist
- Resources & Python Libraries
- Frequently Asked Questions
- Action Items & Next Steps
1. Why Automate Dredging Reporting?
Manual production reports take hours and are prone to errors. Automation with Python scripts:
- ✅ Saves 5‑10 hours per week.
- ✅ Eliminates transcription errors.
- ✅ Enables real‑time dashboards for the client.
- ✅ Triggers alerts when production drops or turbidity exceeds limits.
🧠 Golden Rule: Automate only after you have manually validated the data flow for one month. Understand the process before scripting.
🌊 River Warrior Pro-Tip: Jamuna Python Script
We wrote a Python script that pulled data from the dredge’s SCADA system every hour, computed cumulative volume, and emailed a summary to the client. The client loved the transparency – and we won the next contract.
2. Data Sources: Meter Logs, Surveys, Sensor APIs
| Source | Format | Access method |
|---|---|---|
| Flow / density meters那样Modbus, CSV export那样SCADA API or serial logger | ||
| Dredge position (RTK GNSS)那样NMEA sentences那样Serial or TCP stream |
📌 For automation, standardise on CSV or JSON. Most industrial systems can export to these formats.
3. Python Script Example: Daily Production Report
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
# Load hourly production logs (CSV with columns: timestamp, volume_m3)
df = pd.read_csv('production_log.csv', parse_dates=['timestamp'])
df['date'] = df['timestamp'].dt.date
# Aggregate daily production
daily = df.groupby('date')['volume_m3'].sum().reset_index()
daily.columns = ['Date', 'Daily Production (m³)']
# Calculate cumulative and remaining
target_total = 500000
daily['Cumulative'] = daily['Daily Production (m³)'].cumsum()
daily['Remaining'] = target_total - daily['Cumulative']
daily['% Complete'] = daily['Cumulative'] / target_total * 100
# Save report
daily.to_csv('daily_production_report.csv', index=False)
# Plot S‑curve
plt.figure(figsize=(10,6))
plt.plot(daily['Date'], daily['Cumulative'], label='Actual')
# Add planned linear line (example)
planned_days = (daily['Date'].max() - daily['Date'].min()).days
planned = [ (target_total / planned_days) * i for i in range(planned_days+1)]
plt.plot(daily['Date'], planned, '--', label='Planned')
plt.title('Dredge Production S-Curve')
plt.xlabel('Date')
plt.ylabel('Cumulative volume (m³)')
plt.legend()
plt.grid(True)
plt.savefig('s_curve.png')
print("Report generated: daily_production_report.csv and s_curve.png")This script can be scheduled daily (e.g., with cron or Task Scheduler).
📊 Script Simulator (Production Forecast)
Simulate the output of the Python script with sample data:
Contract volume (m³):
Days completed:
Average daily production (m³/day):
Cumulative: 360,000 m³ (72%) | Remaining: 140,000 m³ | At this rate, finish in 41.7 days.
4. Building a Live Dashboard (Power BI / Plotly Dash)
Dashboards provide real‑time visualisation for site offices. Options:
- Power BI: Connect to Excel/CSV on SharePoint, refresh every hour. Free for small teams.
- Plotly Dash (Python): Custom web dashboard with live graphs, hosted on a local server or cloud.
- Grafana: Excellent for time‑series data from APIs.
5. Automated Email Alerts for Exceedances
Use Python’s `smtplib` to send alerts when:
- Daily production < 80% of target for 2 consecutive days.
- Turbidity exceeds permit limit (e.g., >50 NTU).
- Downtime > 4 hours in a shift.
import smtplib
from email.message import EmailMessage
def send_alert(subject, body):
msg = EmailMessage()
msg.set_content(body)
msg['Subject'] = subject
msg['From'] = 'alerts@riverwarrior.com'
msg['To'] = 'project_manager@client.com'
with smtplib.SMTP('smtp.gmail.com', 587) as s:
s.starttls()
s.login('your_email', 'password')
s.send_message(msg)⚠️ Use environment variables for passwords – never hard‑code in scripts.
6. Integrating with Hypack / PDS API
Modern acquisition software often provides APIs:
- Hypack: ODBC database, direct SQL queries to project database.
- PDS (Teledyne): REST API for real‑time depth and production data.
- QINSy: SQLite database (easy to query with Python).
Example querying Hypack SQLite:
import sqlite3
conn = sqlite3.connect('hypack_project.db')
df = pd.read_sql_query("SELECT timestamp, volume FROM production", conn)7. Case Study: Jamuna River Automated Reporting (2026)
Challenge: Client required daily production reports by 8 AM, but manual compilation took 2 hours each day.
- Solution: Python script that:
- Extracted hourly meter data from SCADA (Modbus).
- Queried daily survey results from shared folder (CSV).
- Generated Excel report and S‑curve.
- Emailed PDF to client at 7 AM automatically.
- Result: 50 hours saved per month, zero errors, client satisfaction increased.
- Lesson: Automation freed the hydrographer to focus on data analysis instead of paperwork.
🌊 The script also sent a nightly summary to the project manager’s mobile (via Telegram API).
8. Automation Checklist
- Identify all data sources (meters, surveys, sensors).
- Standardise file formats (CSV, JSON) and naming conventions.
- Write Python script to read and aggregate data.
- Implement error handling (e.g., missing file, connection loss).
- Schedule script daily (cron / Task Scheduler).
- Set up email alerts for script failures.
- Create dashboard (Power BI / Plotly) for real‑time view.
- Train team to interpret automated outputs.
- Review automation accuracy weekly against manual checks.
- Document the code and data flow.
Click items to track progress (saved in browser).
9. Resources & Python Libraries
10. Frequently Asked Questions
Do I need to be a professional programmer to automate reporting?
Basic Python knowledge (pandas, file I/O) is sufficient. Many templates are available online. Start with a small script and expand.
What if my dredge system has no API?
You can still automate by reading exported CSV files from a shared folder. Use a script that monitors the folder for new files.
How do I schedule a Python script daily?
On Windows, use Task Scheduler. On Linux, use cron. Example: `0 7 * * * python /path/to/script.py` runs at 7 AM.
Is it safe to connect to Hypack database directly?
Yes, but only read‑only. Never write to the database while Hypack is open. Use a copy of the database.
What is the biggest risk in automation?
Silent data errors (e.g., missing file not handled). Always implement error logging and send a “script completed with warnings” email.
11. Action Items & Next Steps
- 📌 Identify one repetitive task in your current dredge reporting.
- 📌 Write a small Python script to automate it (even if only reading a CSV).
- 📌 Use the interactive simulator to understand production forecasting.
- 📌 Proceed to Day 56: Advanced River Training & Stabilisation.
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