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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# Parking Enforcement
VIMS instance for parking-enforcement.
## Related Article
[/insights/official-data-vs-observed-reality/](https://landvex.com/insights/official-data-vs-observed-reality/)
## Anomaly Classes
- illegal_parking
- expired_meter
- blocked_access
- fire_hydrant_violation
- handicap_violation
## 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/parking-enforcement/predict`
- WebSocket: `ws://host/ws/parking-enforcement/alerts`
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# parking-enforcement configuration
topic: parking-enforcement
display_name: Parking Enforcement
article_url: /insights/official-data-vs-observed-reality/
anomaly_classes:
- illegal_parking
- expired_meter
- blocked_access
- fire_hydrant_violation
- handicap_violation
model:
base: yolov8n.pt
input_size: 640
training:
epochs: 100
batch_size: 16
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"""
Database setup for Parking Enforcement
"""
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 parking-enforcement."""
db = VIMSDatabase("parking-enforcement")
db.create_schema(anomaly_classes=['illegal_parking', 'expired_meter', 'blocked_access', 'fire_hydrant_violation', 'handicap_violation'])
print(f"Database initialized for Parking Enforcement")
if __name__ == "__main__":
setup()
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"""
parking-enforcement Anomaly Detector
Generated by VIMS Instance Creator
Related article: /insights/official-data-vs-observed-reality/
"""
import sys
from pathlib import Path
sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
class ParkingEnforcementDetector(VIMSBaseDetector):
"""
Anomaly detector for Parking Enforcement.
Article: /insights/official-data-vs-observed-reality/
"""
TOPIC = "parking-enforcement"
CLASS_NAMES = {
0: "illegal_parking", 1: "expired_meter", 2: "blocked_access", 3: "fire_hydrant_violation", 4: "handicap_violation"
}
SEVERITY_MAP = {
"illegal_parking": 3, "expired_meter": 3, "blocked_access": 3, "fire_hydrant_violation": 3, "handicap_violation": 3
}
def preprocess(self, image):
"""parking-enforcement-specific preprocessing."""
# TODO: Implement specific preprocessing
return image
def postprocess(self, raw_output):
"""parking-enforcement-specific postprocessing."""
# TODO: Implement specific postprocessing
return raw_output
# Register instance
VIMSInstanceRegistry.register("parking-enforcement", ParkingEnforcementDetector)