ARCHITECTURE · UNS
2,461 mappings·1,027-field schema·ISA-95 categorized·deterministic
A Unified Namespace solves where data lives. It says nothing about what the payload at each node means.
The UNS pattern gives every asset a place in one hierarchy and every consumer one broker to subscribe to. That is genuinely the right architecture, and it is only half the problem: publish Enterprise/Site/Area/Line/Cell/spindle and the topic is now canonical while the payload under it is still whatever the vendor emitted.
So two cells on the same line publish S1Temp and SPINDEL_TEMPERATUR to structurally identical topics, and every subscriber re-implements the same translation. The UNS has centralised the transport and left the semantics distributed, which is the expensive half.
Normalize at the edge, publish canonical. Field names are ISA-95 categorized, so a subscriber can filter on category rather than on a hand-maintained tag list.
| Raw vendor tag | Canonical field | ISA-95 category | |
|---|---|---|---|
| S1Temp | → | spindle_temperature | equipment_condition |
| SPINDEL_AUSLASTUNG | → | spindle_load_pct | equipment_performance |
| SPINDLE_SPEED_ACT | → | spindle_speed_rpm | equipment_performance |
| AC_Power | → | inverter_output_kw | energy_consumption |
Every row above is produced by the shipping engine, not written by hand. This page fails to build if any of them stops resolving to the field shown.
Resolution is a lookup, not an inference. A tag is matched against a curated corpus of 2,461 confirmed vendor mappings; only if that misses does it fall to weaker layers: a normalized fold that strips unit suffixes and separators, then a signal classifier that reads structure and unit evidence. Each layer carries a lower confidence, and the layer that answered is named in the response. A mapping you disagree with is visible rather than buried.
Below a confidence threshold the engine abstains. An evidence gate refuses a match it cannot justify and reports the tag unresolved, by name, with the score and the reasons, because a wrong field at high confidence is far more expensive than an honest miss. Six read-time validators then check what survives: sentinel detection, physics bounds, stuck sensors, dropout, operating mode and correlation.
Units are converted to SI where the source unit is known, and every conversion is reported with its factor. A value that fails a physics bound is nulled with the bound it violated attached, not silently dropped.
It does not invent fields. A tag outside the corpus that the signal layer cannot justify comes back unresolved and counted, and those names feed the roadmap for the next pack rather than a plausible guess in your data. It is a translation layer, not a historian: it holds no time series and replaces no SCADA system. And the container is a sandbox: it ships the deterministic layers and the canonical schema, not the production embedding, research and corpus-learning layers.
No key, no account, no sales call. The sandbox runs locally and answers with the same canonical schema production does.
Payloads are plain JSON, so they publish over MQTT or Sparkplug B without translation, and drop into HighByte, Ignition or any broker-backed hub as the modelling layer rather than competing with it.
Run the container beside your broker and publish canonical topics. See platform licensing for embedding the canonical data model in your own product, or pricing for usage-based API access.