77 lines
3.9 KiB
Markdown
77 lines
3.9 KiB
Markdown
# Roadmap
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## 0. Bootstrap (this milestone)
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Establish package boundaries, architecture documentation, example calculation
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configuration, basic domain/protocol models, tests, and an optional local
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TimescaleDB Compose design.
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## 1. ENLYZE API exploration client/CLI (completed baseline)
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A read-only, GET-only raw inspection CLI and fixture sanitizer are implemented.
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Exploration established a K7 candidate mass-flow signal and speed gate; the
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evidence and remaining uncertainties are recorded in `docs/enlyze-api.md`.
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Continue to record sanitized fixtures for newly verified semantics.
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## 2. Live material-consumption MVP (in progress)
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The integration engine, ENLYZE gateway, single-cycle polling service, atomic
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JSON checkpoint store, and configured continuous foreground polling runner are
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implemented and covered by synthetic tests. Configure the validated K7 instance
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in `config/k7-material-consumption.yaml`; start it with
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`production-analytics run material-poll --config config/k7-material-consumption.yaml`.
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Runtime options select the polling interval, state directory (default: ignored
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`data/state/material/`), and existing secrets-file handling. Only one writer may
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poll each machine/order state key.
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The runner now persists cumulative material-consumption snapshots for Grafana in
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PostgreSQL using `db/schema.sql`. JSON remains the restart checkpoint; PostgreSQL
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stores derived time-series snapshots only. No Timescale-specific features are used.
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A new order may first appear with a non-zero total after initial sample integration.
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Grafana dashboard validation remains pending. No HTTP endpoints, application
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containers, daemonization, or schedulers have been added.
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Detect the currently active Production Run and production order, continuously
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ingest new ENLYZE samples, maintain persistent incremental integration state,
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and store/expose cumulative material consumption for Grafana. Implement this
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as generic rate integration (`material_rate * elapsed_time`) subject to
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configured machine/process-specific validity conditions, so it can serve more
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than one machine/material pair. The first validated implementation is K7:
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integrate `Stundenleistung Anlage` (kg/h) only while `Geschwindigkeit
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Gesamtanlage > 0.5 m/min`; do not use `Anlage läuft` as the primary gate.
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Bento 1 bentonite-powder consumption is the next analogous use case. Select
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and validate its ENLYZE source variable and gating logic from process data
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before implementing it; neither is defined yet.
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## 3. Persistence foundation
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Design and migrate a TimescaleDB schema for derived metrics/events, calculation
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runs, Production Run/order context, and incremental state. Add Grafana-oriented
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query examples.
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## 4. Historical replay/backfill
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Enable bounded historical Production Run replay and production-order analysis
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using the same generic integration engine as live processing. Integrate each
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Production Run separately and aggregate derived values at the production-order
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level; never bridge gaps between runs. Include rerun/provenance behavior and
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tests against sanitized fixtures.
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## 5. Peak detection (first detector completed)
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The generic configurable sawtooth detector is implemented: it retains the
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current maximum, closes a cycle after a configurable below-peak fraction has
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actual sample support for a configurable elapsed duration, and emits one
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completed peak. Its pure state can later be persisted for incremental use. It
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has been validated with a clean K7 roll-length reference signal and a more
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variable Bento 2 roll-weight robustness signal; the latter includes plateaus,
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non-zero resets, and varying peak heights without detector special cases.
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## Later
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State/cycle durations, rolling statistics, threshold events, derivatives, and
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specific-consumption calculations. An optional historical mass-balance
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refinement is actual QA/QS basis-weight data from SQL, replacing static article
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basis weight; it is not required for the live material-consumption MVP.
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