App-Kommunikation

Dieses Diagramm zeigt die Kommunikation zwischen Browser, FastAPI-Backend, Job-Ablage und der Python-Optimierungspipeline.

flowchart LR
    user[Benutzer im Browser]
    frontend[React Frontend<br/>Vite Build / App.jsx]
    backend[FastAPI Backend<br/>webapp/backend/main.py]
    jobs[(var/jobs<br/>Job-Status, Uploads, Logs, Outputs)]
    preprocess[Preprocessing<br/>src/preprocessing/exploration_preprocess.py]
    optimization[Optimierung<br/>src/optimization/run_optimization.py]
    model[Pyomo Modell<br/>src/optimization/model_builder.py]
    solver[Solver<br/>HiGHS / Gurobi / SCIP]
    processed[(processed Parquet<br/>Job-Verzeichnis)]
    output[(output.xlsx<br/>warmstart.json)]

    user --> frontend
    frontend -->|POST /api/run<br/>Excel + Parameter + optional Warmstart| backend
    backend -->|Job-Verzeichnis anlegen| jobs
    backend -->|Subprozess starten| preprocess
    preprocess -->|liest Excel<br/>schreibt vorbereitete Tabellen| processed
    backend -->|Subprozess starten| optimization
    optimization -->|lädt Tabellen| processed
    optimization --> model
    model --> solver
    solver -->|Lösung| model
    optimization -->|Excel + Warmstart exportieren| output
    backend -->|Status completed / failed / cancelled| jobs

    frontend -->|GET /api/jobs/{job_id}| backend
    frontend -->|GET /api/jobs/{job_id}/logs/{name}| backend
    frontend -->|GET /api/jobs/{job_id}/monthly-flows| backend
    frontend -->|GET /api/jobs/{job_id}/capacity-timeseries| backend
    frontend -->|GET /api/jobs/{job_id}/output| backend
    frontend -->|GET /api/jobs/{job_id}/warmstart| backend
    frontend -->|POST /api/jobs/{job_id}/cancel| backend

    backend -->|liest job.json / Logs| jobs
    backend -->|liest output.xlsx| output
    backend -->|liest processed Parquet| processed

Ablauf eines Optimierungslaufs

sequenceDiagram
    participant U as Benutzer
    participant F as React Frontend
    participant B as FastAPI Backend
    participant P as Preprocessing
    participant O as Optimierung
    participant S as Solver
    participant V as var/jobs

    U->>F: Excel-Datei und Solver-Parameter auswaehlen
    F->>B: POST /api/run
    B->>V: Job-Verzeichnis, input.xlsx, job.json anlegen
    B-->>F: job_id zurueckgeben
    B->>P: exploration_preprocess.py als Subprozess
    P->>V: processed/*.parquet und preprocess.log schreiben
    B->>O: run_optimization.py als Subprozess
    O->>S: Pyomo-Modell loesen
    S-->>O: Loesung / Status
    O->>V: output.xlsx, warmstart.json, optimization.log schreiben
    B->>V: job.json auf completed/failed/cancelled setzen

    loop Polling waehrend der Laufzeit
        F->>B: GET /api/jobs/{job_id}
        B-->>F: Status
        F->>B: GET /api/jobs/{job_id}/logs/optimization
        B-->>F: Logtext
    end

    F->>B: GET /api/jobs/{job_id}/monthly-flows
    B-->>F: Monatsfluesse fuer Plotly
    F->>B: GET /api/jobs/{job_id}/capacity-timeseries
    B-->>F: Kapazitaets-Zeitreihen fuer Plotly
    F->>B: GET /api/jobs/{job_id}/output
    B-->>F: output.xlsx Download

Deployment-Sicht

flowchart TB
    subgraph docker[Docker Container leag-coallog]
        nginxless[FastAPI / Uvicorn<br/>Port 8080]
        static[React Static Build<br/>webapp/frontend/dist]
        python[Python Runtime<br/>Preprocessing + Optimierung]
        gurobi[Gurobi / HiGHS / SCIP]
    end

    browser[Browser] -->|HTTP :8080| nginxless
    nginxless -->|liefert statische Dateien| static
    nginxless -->|API-Aufrufe /api/*| python
    python --> gurobi
    python --> volume[(Host Volume ./var:/app/var)]
    license[(Host ./licenses:/app/licenses:ro<br/>oder GRB_WLS*)] --> gurobi

3.6 KiB Raw Blame History

App-Kommunikation

Ablauf eines Optimierungslaufs

Deployment-Sicht

3.6 KiB

Raw Blame History