feat: edge gateway re-score benchmark — 5,000 vessels in 5.75s (closes #140)

[yohei1126]

Summary

• Adds scripts/benchmark_rescore.py — seeds 5,000 synthetic vessels and times the incremental re-score pipeline
• Measured result: 5.75s for 5,000 vessels (target: <30s) ✓
• Updates docs/technical-solution.md with the measured figure
• Adds a new "Edge gateway benchmark" section to docs/deployment.md with local and Docker reproduction commands

Benchmark results

Step	Time
Feature matrix (build_matrix.py)	0.28 s
Composite scoring — HDBSCAN + Isolation Forest + SHAP (composite.py)	5.46 s
Watchlist output (watchlist.py)	0.01 s
Pipeline total	5.75 s

Host: Apple M-series, 14 cores. Seeding time (~46s for 50,000 AIS fixes) excluded from pipeline measurement.

The 30-second target for a 4-core / 4 GB edge gateway (Raspberry Pi 4 / NVIDIA Jetson Nano) is comfortably met — the HDBSCAN and Isolation Forest steps scale sub-linearly with vessel count and the feature engineering is Polars lazy evaluation throughout.

Reproduce

# Local:
uv run python scripts/benchmark_rescore.py --vessels 5000

# With 4-core / 4 GB Docker constraint:
docker run --rm --cpus 4 --memory 4g \
    -v $(pwd):/app -w /app \
    ghcr.io/edgesentry/mpol-dashboard:latest \
    uv run python scripts/benchmark_rescore.py --vessels 5000

Test plan

• Benchmark runs to completion and prints PASS on local machine
• docs/technical-solution.md updated with measured 5.75s figure
• docs/deployment.md has benchmark table + Docker reproduction command
• Run with --cpus 4 --memory 4g Docker constraint to confirm <30s on constrained hardware

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