Vertical-Driven Architectures
Collection
Physical AI system designs by CodeNinja, reasoned on Praxis, made living with Hyper Ontology. • 57 items • Updated
choice stringlengths 7 40 | picked stringlengths 50 196 | why stringlengths 61 182 |
|---|---|---|
Work surface model | GLM 5.3, 753B parameters at FP8, bespoke licence, central node in the port's own data centre | Bespoke licence permits commercial, internal use and redistribution; the command centre role is committed by the requirement; runs on the ontology and memory, not an external service |
Per-frame closed-set detector model | RF-DETR, Apache-2.0, dock edge tier, about 61 to 68 MB at 16 bit (Nano to Large) | Small checkpoints keep pace with the camera stream; the authority holds the weights outright; detection survives an uplink loss |
Zero-shot open-vocabulary detector model | OWLv2, Apache-2.0, site tier, about 310 MB (base) and 875 MB (large) at 16 bit | Detects oil sheen, floating debris, smoke or vapour and damage cues on selected frames without port-specific training data |
Evidence triage and drafting model | Ministral 3 8B at FP8, Apache-2.0, site tier, about 12 GB | Fits the 48 GB PCIe class with room for concurrent requests; a describer and reader of evidence, never a localiser; European publisher suits an EU public buyer |
Object tracking model | Roboflow trackers (clean-room SORT, ByteTrack, OC-SORT, BoT-SORT), Apache-2.0, dock edge tier | Motion and association only, no re-identification branch, chosen on the port's own labelled clips |
Frontier hardware | 141 GB HBM GPU class, eight GPUs per node, one node (1,128 GB) | 753 GB of FP8 weights times the 1.2 planning factor is 904 GB, inside 1,128 GB with about 375 GB beside the weights |
Site inference hardware | 48 GB PCIe inference server class, one to four cards, air-cooled, one server at each of the two port sites | Sized by memory class, never by product name; one card holds the site models, more cards buy concurrency |
Edge AI compute | GPU-class edge module at each dock, sized by the dock site survey | Rated against salt air, wind and temperature; silicon fixed only at the survey |
Time synchronisation | OCP Time Card grandmaster with linuxptp and chrony | Frames, telemetry and detections share one clock across tiers |
Sensing | Cameras as payloads on the retained drone fleet (patrol nadir, berth survey, incident response; thermal as an annual trial), dock weather station, drone GNSS and telemetry, pipeline health sensors | Detections are geo-located from telemetry against the standardised berth plan |
Pattern: governing | System of Context, with a work surface where the authority's own people build their agents on memory | People decide every response; the loop learns through memory, not weight training |
Pattern: boundary | Events and evidence crops cross the boundary, not pixels | Hours of daily imagery cannot cross the dock uplink, and detection must survive its loss |
Port Drone Watch ontology and model register for Germany
The object model and the model and equipment register from Port Drone Watch: From Oil Sheen to Dispatched Work on the Port's Own Hardware in Germany, an open reference architecture by CodeNinja Atoms, in maritime and ports. It shows how a port authority's docked drone network becomes detections and dispatched work items: every detection is anchored to a real berth, quay asset or restricted zone and reaches a named responder role who decides. Part of the Vertical-Driven Architectures series; every design in the series is also a row in the cumulative dataset https://huggingface.co/datasets/CodeNinjatools/vertical-driven-architectures.
- Read the paper: https://codeatoms.ai/drone-detection-dispatch-port-germany/
- DOI: https://doi.org/10.5281/zenodo.23635729
- Source files and PDF: https://github.com/muhammadumar89/codeninja-research
- Live view: https://huggingface.co/spaces/CodeNinjatools/drone-detection-dispatch-port-germany
Files
| File | What it holds |
|---|---|
objects.json |
15 typed objects (Mission, Construction Work, Detection, Work Item, Flight Authorisation, Coverage Statement, Berth, Dock Station, Restricted Zone, Vessel, Quay Asset, Drone, Responder Role, Drone Operator, Weather Observation), each with its properties and 14 typed links written from chapter 5. Format hyper-ontology/1: designed with CodeNinja Praxis, implemented with Hyper Ontology. |
models.csv |
The model and equipment register: every model, tool and hardware class with its license and the reason for each choice. |
How to use it
import json
from huggingface_hub import hf_hub_download
p = hf_hub_download("CodeNinjatools/drone-detection-dispatch-port-germany-ontology", "objects.json", repo_type="dataset")
objects = json.load(open(p))["objects"]
print([o["label"] for o in objects])
Made with
CodeNinja Praxis, the CodeNinja Atoms platform for designing physical AI systems, and Hyper Ontology, which turns the object model into a living system.
Citation
CodeNinja Engineering Team and Muhammad Umar. 2026. Port Drone Watch: From Oil Sheen to Dispatched Work on the Port's Own Hardware in Germany. CodeNinja Atoms. https://doi.org/10.5281/zenodo.23635729. CC BY 4.0.
One of the CodeNinja Atoms sovereign AI reference architectures: every published design with its sector, country, compute and three-year cost. Its object model sits with the other industrial ontology examples.
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