Vertical-Driven Architectures
Collection
Physical AI system designs by CodeNinja, reasoned on Praxis, made living with Hyper Ontology. • 33 items • Updated
choice stringclasses 5
values | picked stringlengths 26 71 | why stringlengths 119 211 |
|---|---|---|
Model register | Zero entries; all integrity logic is deterministic thresholds and rules | The failure modes are physical and protocol-level, so no learned model is needed, no weights are held and no license question arises |
Hardware class | Flameproof enclosure discipline | The junction boxes sit in the classified area, so the enclosure class is chosen against the zone at each exact position, not by part number |
Hardware class | Area classification as the gating document | The zone, gas group and temperature class are read from the operator's current classification drawing before any hardware is specified, and the position list is countersigned |
Hardware class | Reading an Ex certification marking | The Eex'n' marking is a Zone 2 restricted-breathing class, so a Zone 1 position would force a different, heavier enclosure and must be caught before ordering |
Hardware class | Equipment protection levels drive the purchase | Protection levels, not catalog convenience, decide the explosion proof junction boxes, 24VDC power supplies, breakers and M20 Exd glands |
Sensing | radar tank gauges on all 13 tanks | These are the field devices whose readings must arrive continuous, undistorted and unswapped; the design improves their signal path without modifying them |
Sensing | 24VDC field power supplies | They power the boosters and the loop electronics, and their health is a named failure mode with its own degraded status |
Sensing | 3-core 2.5 sq.mm CU/PVC/SWA/PVC loop cabling | The loop cabling is the medium the distortion travels in, so its schedule is recorded in the as-built against each loop |
Sensing | Modbus RTU loop polling via the boosters | The polling cycle is the pace of truth for every check, and the boosters are what make the three loops readable end to end |
Pattern | System of Context, primary | The central inventory picture is an ontology projection over the gauging host, never a shadow copy, so there is one place where tank truth lives |
Ground | The operator's own site application server, on-premises | The two containers run inside the operational technology boundary on the operator's own hardware, consistent with the Pakistani Cloud First posture, so nothing about tank truth depends on a link leaving the site |
Radar tank gauge integrity ontology and model register for a fuel terminal
The object model and the model and equipment register from Loop Integrity Watch: Ending Distorted Radar Level Readings and Tank-to-Tank Swapping Across the Tank Farm, an open reference architecture by CodeNinja for Pakistan. 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/tank-gauge-integrity-pakistan/
- DOI: https://doi.org/10.5281/zenodo.23157967
- Source files and PDF: https://github.com/muhammadumar89/codeninja-research
- Live view: https://huggingface.co/spaces/CodeNinjatools/tank-gauge-integrity-pakistan
Files
| File | What it holds |
|---|---|
objects.json |
15 typed objects (Fuel Tank, Radar Tank Gauge (RTG), RTG Communication Loop, Modbus Booster (Repeater), Explosion Proof Junction Box, Gauge Reading, Data Quality Event, Central Inventory Picture, Loop Wiring As-Built, Service Order, Warranty and Support Record, OEM Authorization Letter, HSE Work Permit, Instrumentation Technician, Location Engineer), each with its anchor system, properties, status vocabulary and 15 typed links. Format hyper-ontology/1: designed with Praxis, implemented with Hyper Ontology. |
models.csv |
The model and equipment register from the paper's Table 4, with the reason for each choice. |
How to use it
import json
from huggingface_hub import hf_hub_download
p = hf_hub_download("CodeNinjatools/tank-gauge-integrity-pakistan-ontology", "objects.json", repo_type="dataset")
objects = json.load(open(p))["objects"]
print([o["label"] for o in objects])
Made with
Designed on Praxis, CodeNinja's platform for designing physical AI systems. The object model imports into Hyper Ontology, which turns it into a living system. Both are in beta; access by request.
Citation
CodeNinja Engineering Team and Umar Bilal. 2026. Loop Integrity Watch: Ending Distorted Radar Level Readings and Tank-to-Tank Swapping Across the Tank Farm. CodeNinja. https://doi.org/10.5281/zenodo.23157967. CC BY 4.0.
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