Add generate_campaign_report: a pure aggregator over a campaign's child Runs
producing a time-trend axis (Runs bucketed by service-window position) and a
capability-summary axis (grouped by scenario), each carrying pass_rate /
availability / latency. pass_rate keeps the single-Run case-level meaning and
counts execution failures as 0.0 (ADR-0002); time_scale only places Runs into
window-time buckets and never alters any figure. Engine summary now records
avg_latency_ms to feed the latency axis.
Expose GET /api/campaigns/{id}/report (structured) and .../report/markdown
(reusing the existing Markdown export path). Adds "可用性/Availability" to the
domain glossary.
Add a thin async loop (run_campaign_loop) that ticks on real wall-clock time,
maps elapsed×time_scale to a window offset via the pure decide_schedule, spawns
due child Runs, and marks the campaign COMPLETED at window end. All authority
lives in the DB (started_at, spawned_indices, status), so the app lifespan can
resume every RUNNING campaign on startup without double-spawning and stop all
loops gracefully on shutdown. A failing plan entry is skipped and recorded
rather than wedging the campaign.
Creating a campaign now starts its loop; POST /api/campaigns/{id}/cancel stops
further spawning (completed child Runs are kept); GET /api/campaigns/{id}
reports live progress (window offset, spawned/completed Run counts).
Add the pure scheduling seam (campaign_scheduler.decide_schedule) that, given a
static plan and window-clock offset, decides which plan entries are due and
whether the window ended — mirroring judgement.combine_case_outcome, with
time_scale confined to the clock mapping so it never touches judgement/report.
The campaign_runner shell maps injected elapsed time to a window offset, spawns
due child Runs through the existing EvalEngine.run(existing_run=...) path with
campaign_id + RunTrigger.CAMPAIGN, and persists spawned-entry indices per entry
for idempotent, restart-recoverable progress. No auto loop yet (ticket 03).
