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admin 231e18082c Generalize live material consumption architecture 2026-09-04 16:43:24 +02:00
admin e144935439 Add ENLYZE exploration fixtures 2026-09-04 16:33:41 +02:00
10 changed files with 39987 additions and 32 deletions
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PROJECT_KNOWLEDGE.md
+82 -11
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@@ -3,8 +3,12 @@
## Purpose
This service is a calculation and persistence layer between ENLYZE and
Grafana. It must not become a second historian: raw process data stays in
ENLYZE and is retrieved again when historical calculations need reproduction.
Grafana. Its primary use case is to continuously integrate material consumption
for the currently active production order and expose its cumulative value in
Grafana while production is running. K7 fiber is the first validated
application, not the scope of the product. It must not become a second
historian: raw process data stays in ENLYZE and is retrieved again when
historical calculations need reproduction.
## Core architectural decisions
@@ -16,6 +20,17 @@ ENLYZE and is retrieved again when historical calculations need reproduction.
configuration are committed.
- Calculations are modular, testable Python implementations. Configuration
declares instances; it is not a generic low-code language.
- Historical production-order analysis/backfill is secondary. It uses the same
generic integration engine as live processing: one integration engine with
live incremental and historical replay/backfill operating modes.
## Material-consumption integration
The core calculation is generic rate integration: cumulative material
consumption increases by `material_rate * elapsed_time` when the configured
machine/process-specific validity or gating conditions are satisfied. It is
reusable across machines, materials, rate variables, units, and gates. The
project deliberately does not define a broader configuration schema here.
## Peak-cycle detector
@@ -60,18 +75,72 @@ fixtures only.
## Central domain context
Production orders connect machine, article/material, source interval, and
derived results. Metrics and events must retain calculation type/version and
source-time-range provenance.
derived results. An ENLYZE Production Run is a time segment; one
`production_order` can have one or more Production Runs. Integrate each run
separately, then aggregate derived values on the production-order level. Never
implicitly bridge gaps between runs. Metrics and events must retain calculation
type/version and source-time-range provenance.
## K7 fiber integration
The current K7 candidate fiber mass-flow signal is `Stundenleistung Anlage`
in kg/h. Observed behaviour supports treating it as a process-responsive
band-scale signal, but it can remain frozen at a non-zero value during a
machine stop. It must therefore be integrated only while the validated gate
`Geschwindigkeit Gesamtanlage > 0.5 m/min` is active. The ENLYZE boolean
`Anlage läuft` is not the primary integration gate.
The process-derived result is `fiber_feed_kg` for a Production Run. It is not
automatically a finished-product mass or a per-order material yield. Material
measured at the band scale may still be in the machine at an order transition,
and ERP feedback can arrive asynchronously or periodically. Differences from
reported finished-product quantities must not automatically be labelled scrap;
an exact per-order yield needs WIP/transport-delay accounting.
## Bento 1 bentonite-powder integration
Bento 1 bentonite-powder consumption is the next planned application of the
same generic integration mechanism. Its ENLYZE source variable and its
validity/gating logic have not yet been selected or validated; both must be
determined from Bento 1 process data before implementation. Bento 1 does not
need a separate subsystem or calculation formula.
## Quantity-total interpretation
For this customer setup, `production_run.quantity_total` is populated from
ERP/BI production feedback received by ENLYZE, rather than necessarily being
calculated from machine signals. The tested source CSV field is
`PCO_FEEDB_QUANTITY`. For inspected machines/articles it represents reported
finished-product area in m² (for example, 5.00 m × 50 m = 250.00 m²; 4.75 m ×
50 m = 237.50 m²; 5.80 m × 50 m = 290.00 m²). The unit displayed by ENLYZE is
separately configured and must not be considered authoritative until its
configuration is validated.
## Known versus unknown
Known from the ENLYZE UI: machine identity, operational/downtime state,
current production order, article/material number, and process signals exist.
It is **not yet verified** how, or whether, each is exposed by the ENLYZE API.
Do not infer endpoint paths, authentication mechanisms, identifiers, paging,
timestamp semantics, or signal payloads. The active open-question list is in
Validated K7 historical tests show physically plausible fiber-feed totals when
the band-scale kg/h signal is speed-gated. For production order
`K 7-12026000842`, its run from 2026-08-31T10:57:54Z to
2026-08-31T18:23:11Z had ERP `quantity_total` of 12,420 m², approximately
5.206 active hours, approximately 5,180.1 kg integrated fiber feed,
approximately 2,174.2 m integrated machine length, and approximately
995.1 kg/h average active throughput. Article external ID `213408` has width
6.0 m and static mean basis weight 390.16 g/m²; the corresponding approximate
reported finished-product mass is 4,845.8 kg. This is a plausibility validation
case, not a precise material-yield calculation.
Known from the ENLYZE UI/API exploration: machine and product identity,
Production Runs, and relevant K7 process signals are available. Some API and
time-series semantics remain unverified; do not infer them beyond documented
evidence. The active evidence and open-question list is in
[docs/enlyze-api.md](docs/enlyze-api.md).
## Future refinement
An optional future historical mass-balance refinement is to obtain actual
QA/QS basis-weight measurements from SQL instead of using static article basis
weight. It is not a blocker for the live material-consumption MVP.
## Exploration workflow
A deliberately generic, read-only CLI is available as
@@ -85,5 +154,7 @@ authentication header; its value is never printed.
Unmodified captures are local in ignored `data/raw/enlyze/`. Fixtures written
to `fixtures/enlyze/` are sanitized, but must still be reviewed before commit.
The deliverable is verified API observations and sanitized fixtures, not
production calculations or database ingestion.
The exploration deliverable is verified API observations and sanitized
fixtures. The next product deliverable is live incremental material
integration, using a generic material-rate integrator rather than a separate
historical-only calculation path.
docs/architecture.md
+60 -3
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@@ -7,6 +7,19 @@ only to calculate derived results and persists the results, their context, and
the minimum state needed for incremental calculation. It does not mirror raw
time series into PostgreSQL/TimescaleDB.
## Product direction
The primary product path is live integration of material consumption for the
currently active production order, with a continuously updated cumulative value
for Grafana. The core mechanism is generic rate integration: cumulative
material consumption increases by `material_rate * elapsed_time` subject to
machine/process-specific validity or gating conditions. It is reusable for
different machines, materials, rate variables, units, and gates. Historical
production-order analysis and backfill are secondary. They must reuse this
generic integration engine rather than introduce separate formulas: one
integration engine, two operating modes (live incremental processing and
historical replay/backfill).
## Components
| Component | Responsibility | Must not do |
@@ -18,6 +31,48 @@ time series into PostgreSQL/TimescaleDB.
| `cli` | Operator commands; first use is API exploration | Contain business calculations |
| `service` | Future FastAPI composition/API layer | Require endpoints during bootstrap |
## Production Run boundaries and aggregation
ENLYZE Production Runs are time segments, not necessarily one-to-one with a
production order. A `production_order` may contain multiple runs. The
integrator processes every Production Run independently; production-order
totals aggregate the resulting derived values. It must not implicitly integrate
across a gap between runs.
## Material-consumption integrator
Live mode detects the active Production Run/order, ingests new ENLYZE samples,
incrementally integrates material consumption while its configured validity
conditions are active, and persists calculation state and the cumulative
result. Replay mode fetches a bounded completed run and applies that same
stateful operator from its initial state. Results retain the run boundary and
source interval; aggregation across runs is a distinct production-order-level
step.
The generic engine does not prescribe a material, source variable, unit, or
gate. Those are specific to the supported machine/process use case.
### K7 fiber (first validated implementation)
For K7, the current validated calculation integrates `Stundenleistung Anlage`
(kg/h) only for intervals where `Geschwindigkeit Gesamtanlage > 0.5 m/min`.
The mass-flow signal can remain non-zero while the machine is stopped, so it
must not be integrated without this gate. `Anlage läuft` is not the primary
gate.
`fiber_feed_kg` is an objective process-derived feed quantity, not by itself a
finished-product mass or yield. Band-scale material and asynchronously reported
ERP production can be shifted by WIP/transport delay, especially around order
transitions. Any exact per-order yield calculation therefore needs additional
WIP accounting.
### Bento 1 bentonite powder (planned)
Bento 1 is the next planned use case for the same material-consumption
integrator. Its bentonite-powder source variable and validity/gating conditions
are not yet selected or validated and must be determined from ENLYZE process
data before implementation. It does not require a separate subsystem.
## Result provenance
Persisted metrics/events need machine and production-order context where
@@ -36,9 +91,11 @@ avoids a generic expression language.
## Incremental execution
Future calculation state is stored per calculation instance and relevant
context partition. It enables safe continuation (for example an open peak
cycle), while the source interval recorded on results keeps a historical run
reproducible by fetching ENLYZE data again.
context partition. For live material-consumption integration it includes the
latest processed source position, any interval/carry state needed for correct
integration, and the cumulative run value. It enables safe continuation (for
example an open peak cycle), while the source interval recorded on results
keeps a historical run reproducible by fetching ENLYZE data again.
## Peak-cycle semantics
docs/enlyze-api.md
+35 -4
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@@ -73,18 +73,49 @@ Each requested variable contains required UUID and optional `resampling_method`:
`Downtime` requires UUID, machine UUID, type (`THRESHOLD` or `NO_DATA`), timezone-aware ISO 8601 start, nullable end, and nullable comment/reason/update information. A null end denotes an ongoing downtime. It supports historical downtime retrieval but does not establish a complete machine-state timeline.
## VERIFIED WITH LIVE CUSTOMER API
## VERIFIED WITH CUSTOMER EXPLORATION
None. This execution environment has no ENLYZE DNS/network access, so no customer operation has been sent in this milestone.
The following are customer-specific observations, not general ENLYZE API
semantics:
- K7 candidate fiber mass-flow variable: `Stundenleistung Anlage` (kg/h).
Historical behaviour supports interpreting it as a process-responsive
band-scale signal. It can remain frozen at a non-zero value during stops.
- Validated K7 integration gate: `Geschwindigkeit Gesamtanlage > 0.5 m/min`.
Do not use the boolean `Anlage läuft` as the primary gate for this
calculation.
- `production_run.quantity_total` is populated from ERP/BI production feedback
received by ENLYZE for this setup, not necessarily from machine signals. The
tested CSV source field is `PCO_FEEDB_QUANTITY`. For inspected
machines/articles it is reported finished-product area in m²: 5.00 m × 50 m
= 250.00 m², 4.75 m × 50 m = 237.50 m², and 5.80 m × 50 m = 290.00 m².
`quantity_total.unit` is separately configured and is not authoritative
without configuration validation.
Historical validation case (plausibility only): production order
`K 7-12026000842`, Production Run 2026-08-31T10:57:54Z to
2026-08-31T18:23:11Z, had ERP `quantity_total` 12,420 m². Speed-gated
integration produced approximately 5.206 active hours, 5,180.1 kg fiber feed,
2,174.2 m machine length, and 995.1 kg/h average active throughput. Article
external ID `213408` (width 6.0 m; static mean basis weight 390.16 g/m²) gives
an approximate reported finished-product mass of 4,845.8 kg. This supports the
physical plausibility of the feed integration; it is not a precise material
yield calculation.
## STILL UNKNOWN
- Customer response values, permissions, authentication success/failure behavior, default page size, and rate/retention limits.
- Full customer response coverage, permissions, authentication
success/failure behavior, default page size, and rate/retention limits.
- Whether production-run filters have the desired interval-overlap behavior; completed-run `start`/`end` are candidate integration boundaries but require live confirmation.
- Which customer variable is a consumption signal and whether its unit/scaling factor makes it usable for integration.
- Whether the validated K7 signal/gate generalize to other machines, articles,
production conditions, or future signal configuration.
- Whether omitted `resampling_interval` yields native/raw resolution; its unit and exact temporal meaning of resampling methods.
- Sample ordering, boundary inclusion, native sampling behavior, gaps, nulls, quality/bad samples, late arrivals, corrections, and backfills.
- A complete historical machine operational-state resource beyond downtimes.
- Exact per-order material yield: band-scale material may remain in the machine
at an order transition and ERP feedback can arrive asynchronously or
periodically. WIP/transport-delay accounting is required before interpreting
feed-versus-finished-product differences as yield or scrap.
## Exact bounded manual exploration sequence
docs/roadmap.md
+32 -14
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@@ -6,27 +6,43 @@ Establish package boundaries, architecture documentation, example calculation
configuration, basic domain/protocol models, tests, and an optional local
TimescaleDB Compose design.
## 1. ENLYZE API exploration client/CLI (recommended exact next milestone)
## 1. ENLYZE API exploration client/CLI (completed baseline)
A read-only, GET-only raw inspection CLI and fixture sanitizer are implemented.
The remaining work is live, authorized exploration of machines, signal catalog,
bounded samples, state history, production-order history, and article/material
context. Record sanitized fixtures and update `docs/enlyze-api.md` with
evidence. Do not implement ingestion or a database schema before this is
complete.
Exploration established a K7 candidate mass-flow signal and speed gate; the
evidence and remaining uncertainties are recorded in `docs/enlyze-api.md`.
Continue to record sanitized fixtures for newly verified semantics.
## 2. Persistence foundation
## 2. Live material-consumption MVP (next implementation milestone)
Detect the currently active Production Run and production order, continuously
ingest new ENLYZE samples, maintain persistent incremental integration state,
and store/expose cumulative material consumption for Grafana. Implement this
as generic rate integration (`material_rate * elapsed_time`) subject to
configured machine/process-specific validity conditions, so it can serve more
than one machine/material pair. The first validated implementation is K7:
integrate `Stundenleistung Anlage` (kg/h) only while `Geschwindigkeit
Gesamtanlage > 0.5 m/min`; do not use `Anlage läuft` as the primary gate.
Bento 1 bentonite-powder consumption is the next analogous use case. Select
and validate its ENLYZE source variable and gating logic from process data
before implementing it; neither is defined yet.
## 3. Persistence foundation
Design and migrate a TimescaleDB schema for derived metrics/events, calculation
runs, context, and incremental state. Add Grafana-oriented query examples.
runs, Production Run/order context, and incremental state. Add Grafana-oriented
query examples.
## 3. First vertical slice
## 4. Historical replay/backfill
Implement one configured integration calculation over a verified signal and
production-order context. Include backfill, rerun/provenance behavior, and
Enable bounded historical Production Run replay and production-order analysis
using the same generic integration engine as live processing. Integrate each
Production Run separately and aggregate derived values at the production-order
level; never bridge gaps between runs. Include rerun/provenance behavior and
tests against sanitized fixtures.
## 4. Peak detection (first detector completed)
## 5. Peak detection (first detector completed)
The generic configurable sawtooth detector is implemented: it retains the
current maximum, closes a cycle after a configurable below-peak fraction has
@@ -38,5 +54,7 @@ non-zero resets, and varying peak heights without detector special cases.
## Later
State/cycle durations, aggregates by order, rolling statistics, threshold
events, derivatives, and specific-consumption calculations.
State/cycle durations, rolling statistics, threshold events, derivatives, and
specific-consumption calculations. An optional historical mass-balance
refinement is actual QA/QS basis-weight data from SQL, replacing static article
basis weight; it is not required for the live material-consumption MVP.
fixtures/enlyze/bento2-variables.json
+531
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@@ -0,0 +1,531 @@
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fixtures/enlyze/k1-variables.json
+416
View File
@@ -0,0 +1,416 @@
{
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fixtures/enlyze/k6-variables.json
+623
View File
@@ -0,0 +1,623 @@
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}
fixtures/enlyze/k7-production-runs.json
+36928
View File
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fixtures/enlyze/k7-variables.json
+1159
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fixtures/enlyze/machines-page-1.json
+121
View File
@@ -0,0 +1,121 @@
{
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},
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}