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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# Public Transport Infrastructure
VIMS instance for public-transport.
## Related Article
[/insights/why-cities-need-field-intelligence/](https://landvex.com/insights/why-cities-need-field-intelligence/)
## Anomaly Classes
- bus_stop_damage
- shelter_vandalism
- bench_broken
- schedule_missing
- accessibility_issue
## 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/public-transport/predict`
- WebSocket: `ws://host/ws/public-transport/alerts`
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# public-transport configuration
topic: public-transport
display_name: Public Transport Infrastructure
article_url: /insights/why-cities-need-field-intelligence/
anomaly_classes:
- bus_stop_damage
- shelter_vandalism
- bench_broken
- schedule_missing
- accessibility_issue
model:
base: yolov8n.pt
input_size: 640
training:
epochs: 100
batch_size: 16
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"""
Database setup for Public Transport Infrastructure
"""
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 public-transport."""
db = VIMSDatabase("public-transport")
db.create_schema(anomaly_classes=['bus_stop_damage', 'shelter_vandalism', 'bench_broken', 'schedule_missing', 'accessibility_issue'])
print(f"Database initialized for Public Transport Infrastructure")
if __name__ == "__main__":
setup()
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"""
public-transport Anomaly Detector
Generated by VIMS Instance Creator
Related article: /insights/why-cities-need-field-intelligence/
"""
import sys
from pathlib import Path
sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
class PublicTransportDetector(VIMSBaseDetector):
"""
Anomaly detector for Public Transport Infrastructure.
Article: /insights/why-cities-need-field-intelligence/
"""
TOPIC = "public-transport"
CLASS_NAMES = {
0: "bus_stop_damage", 1: "shelter_vandalism", 2: "bench_broken", 3: "schedule_missing", 4: "accessibility_issue"
}
SEVERITY_MAP = {
"bus_stop_damage": 3, "shelter_vandalism": 3, "bench_broken": 3, "schedule_missing": 3, "accessibility_issue": 3
}
def preprocess(self, image):
"""public-transport-specific preprocessing."""
# TODO: Implement specific preprocessing
return image
def postprocess(self, raw_output):
"""public-transport-specific postprocessing."""
# TODO: Implement specific postprocessing
return raw_output
# Register instance
VIMSInstanceRegistry.register("public-transport", PublicTransportDetector)