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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# Playground Safety Inspection
VIMS instance for playground-safety.
## Related Article
[/insights/evidence-driven-municipal-maintenance/](https://landvex.com/insights/evidence-driven-municipal-maintenance/)
## Anomaly Classes
- broken_equipment
- sharp_edges
- missing_safety_surface
- trip_hazard
- vandalism
## 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/playground-safety/predict`
- WebSocket: `ws://host/ws/playground-safety/alerts`
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# playground-safety configuration
topic: playground-safety
display_name: Playground Safety Inspection
article_url: /insights/evidence-driven-municipal-maintenance/
anomaly_classes:
- broken_equipment
- sharp_edges
- missing_safety_surface
- trip_hazard
- vandalism
model:
base: yolov8n.pt
input_size: 640
training:
epochs: 100
batch_size: 16
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"""
Database setup for Playground Safety Inspection
"""
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 playground-safety."""
db = VIMSDatabase("playground-safety")
db.create_schema(anomaly_classes=['broken_equipment', 'sharp_edges', 'missing_safety_surface', 'trip_hazard', 'vandalism'])
print(f"Database initialized for Playground Safety Inspection")
if __name__ == "__main__":
setup()
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"""
playground-safety 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 PlaygroundSafetyDetector(VIMSBaseDetector):
"""
Anomaly detector for Playground Safety Inspection.
Article: /insights/evidence-driven-municipal-maintenance/
"""
TOPIC = "playground-safety"
CLASS_NAMES = {
0: "broken_equipment", 1: "sharp_edges", 2: "missing_safety_surface", 3: "trip_hazard", 4: "vandalism"
}
SEVERITY_MAP = {
"broken_equipment": 3, "sharp_edges": 3, "missing_safety_surface": 3, "trip_hazard": 3, "vandalism": 3
}
def preprocess(self, image):
"""playground-safety-specific preprocessing."""
# TODO: Implement specific preprocessing
return image
def postprocess(self, raw_output):
"""playground-safety-specific postprocessing."""
# TODO: Implement specific postprocessing
return raw_output
# Register instance
VIMSInstanceRegistry.register("playground-safety", PlaygroundSafetyDetector)