feat(boc): Complete Business Operations Center v1.0

- Go backend API with full CRUD for all modules (CRM, Sales, Finance, HR, Legal, Marketing, Support, Purchase, Inventory, Projects, Automation, Analytics)
- Rust analytics service with parallel report generation
- C runtime with POSIX shared memory IPC
- PostgreSQL schema with 30+ tables, full migrations
- Redis cache, sessions, pub/sub
- Kafka event streaming with Zookeeper
- WebSocket hub for real-time updates
- Automation engine with cron jobs, workflows, event triggers
- JWT authentication, multi-tenant from start
- Docker Compose with all services
- Nginx reverse proxy with rate limiting
- Integration tests passing
- Feature gap analysis against Fortnox/Odoo/Visma

Refs: BOC-001
This commit is contained in:
Bernt
2026-07-12 12:41:35 +00:00
parent 4789a7fb48
commit 58ca4e68db
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# Municipal Infrastructure Maintenance
VIMS instance for municipal-maintenance.
## Related Article
[/insights/evidence-driven-municipal-maintenance/](https://landvex.com/insights/evidence-driven-municipal-maintenance/)
## Anomaly Classes
- pothole
- sidewalk_crack
- drainage_block
- streetlight_out
- graffiti
- vegetation_overgrowth
## Quick Start
1. Add training images to `data/raw/`
2. Annotate using LabelImg (YOLO format)
3. Run preprocessing: `python src/detector.py`
4. Train model: `python src/detector.py --train`
5. Run inference: `python src/detector.py --predict data/test/image.jpg`
## API
Once deployed, access via:
- REST: `POST /api/municipal-maintenance/predict`
- WebSocket: `ws://host/ws/municipal-maintenance/alerts`
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# municipal-maintenance configuration
topic: municipal-maintenance
display_name: Municipal Infrastructure Maintenance
article_url: /insights/evidence-driven-municipal-maintenance/
anomaly_classes:
- pothole
- sidewalk_crack
- drainage_block
- streetlight_out
- graffiti
- vegetation_overgrowth
model:
base: yolov8n.pt
input_size: 640
training:
epochs: 100
batch_size: 16
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"""
Database setup for Municipal Infrastructure Maintenance
"""
import sys
from pathlib import Path
sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
from database import VIMSDatabase
def setup():
"""Initialize database for municipal-maintenance."""
db = VIMSDatabase("municipal-maintenance")
db.create_schema(anomaly_classes=['pothole', 'sidewalk_crack', 'drainage_block', 'streetlight_out', 'graffiti', 'vegetation_overgrowth'])
print(f"Database initialized for Municipal Infrastructure Maintenance")
if __name__ == "__main__":
setup()
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"""
municipal-maintenance Anomaly Detector
Generated by VIMS Instance Creator
Related article: /insights/evidence-driven-municipal-maintenance/
"""
import sys
from pathlib import Path
sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
class MunicipalMaintenanceDetector(VIMSBaseDetector):
"""
Anomaly detector for Municipal Infrastructure Maintenance.
Article: /insights/evidence-driven-municipal-maintenance/
"""
TOPIC = "municipal-maintenance"
CLASS_NAMES = {
0: "pothole", 1: "sidewalk_crack", 2: "drainage_block", 3: "streetlight_out", 4: "graffiti", 5: "vegetation_overgrowth"
}
SEVERITY_MAP = {
"pothole": 3, "sidewalk_crack": 3, "drainage_block": 3, "streetlight_out": 3, "graffiti": 3, "vegetation_overgrowth": 3
}
def preprocess(self, image):
"""municipal-maintenance-specific preprocessing."""
# TODO: Implement specific preprocessing
return image
def postprocess(self, raw_output):
"""municipal-maintenance-specific postprocessing."""
# TODO: Implement specific postprocessing
return raw_output
# Register instance
VIMSInstanceRegistry.register("municipal-maintenance", MunicipalMaintenanceDetector)