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💬Tekan Play Demo untuk lihat Industrial Control Dashboard Agent.
Demo Complete — All Stages Generated
01 WhatsApp Command
02 Requirement Analysis
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03 Python Script Generation
1
📥
Ambil data SCADAFetch control system data
2
📊
Kira metrikCalculate KPIs
3
🚨
Kesan anomaliDetect process anomalies
4
📊
Dashboard & laporanGenerate control dashboard
import pandas as pd
import numpy as np
def dashboard_snapshot(plant_id):
tags = pd.read_sql("SELECT * FROM plc_tags WHERE plant_id = ?", plant_id)
snapshot = {}
for _, tag in tags.iterrows():
values = pd.read_sql("SELECT value FROM tag_history WHERE tag_id = ? ORDER BY ts DESC LIMIT 10", tag['id'])
mean = values['value'].mean()
std = values['value'].std()
latest = values['value'].iloc[0]
snapshot[tag['name']] = {'current': latest, 'target': tag['setpoint'], 'status': 'Normal' if abs(latest - tag['setpoint']) < 2*std else 'Warning' if abs(latest - tag['setpoint']) < 3*std else 'Critical'}
return snapshot
04 Block Diagram
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05 System Schematic
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Dashboard alerts should be verified by a control engineer before any action.
06 Simulasi Control Dashboard
Mula Dashboard
07 Control Dashboard Report
Terminal Log
[SISTEM] Industrial Control Dashboard Agent sedia
System Architecture
🤖 Agent Layer
SCADA parser
KPI calculator
Anomaly detector
🏭 Manufacturing Layer
PLC tags
SCADA historian
Control network
Reports
Safety Notes
- Dashboard alerts should be verified by a control engineer before any action.
- Configure role-based access for control data.
- Audit all dashboard configurations for compliance.
- Browser demo uses simulated data only.