Add production diagnostics and planning features

CHANGELOG.md

@@ -1,5 +1,17 @@
 # Changelog
 
+## 1.7.0 - 2026-06-18
+- Produktiv-Ausbau fuer Schaltvertrauen: persistente Entscheidungs-Timeline,
+  Event-Latenzmessungen und Dry-run pro Aktor.
+- Backup-/Restore-API fuer Aktor-Konfigurationen, Reconciliation-Status und
+  sichtbare Job-Historie.
+- Feedback kann jetzt konkrete Korrekturtypen wie `too_early`, `too_late` und
+  `never_automate` speichern; `never_automate` setzt eine manuelle Sperre.
+- Planungs-Refresh erzeugt Raum-/Aktorgruppen, einfache Szenenvorschlaege und
+  lokale Agent-Insights aus vorhandenen Daten.
+- Event-Verarbeitung laedt Aktor-Konfigurationen nur noch einmal pro
+  Home-Assistant-State-Change.
+
 ## 1.6.1 - 2026-06-18
 - Home-Assistant-WebSocket nutzt wieder keinen clientseitigen Keepalive-Ping.
   Damit bleibt das Event-Verhalten aus 0.7.8 stabil und Sensorwechsel fuehren

README.md

@@ -31,6 +31,8 @@ nach einer ausdrücklichen Freigabe ausführen.
   [`docs/V1_5_2_OPERATING_GUIDE.md`](docs/V1_5_2_OPERATING_GUIDE.md)
 - Version 1.5.3 SQLite-Cache fuer Ingress-Dashboard:
   [`docs/V1_5_3_OPERATING_GUIDE.md`](docs/V1_5_3_OPERATING_GUIDE.md)
+- Version 1.7.0 Diagnose, Backup, Dry-run und Planung:
+  [`docs/V1_7_0_OPERATING_GUIDE.md`](docs/V1_7_0_OPERATING_GUIDE.md)
 - Arbeitsregeln für Coding-Agenten: [`AGENTS.md`](AGENTS.md)
 
 ## Reifegrad

addon/config.yaml

@@ -1,5 +1,5 @@
 name: SillyHome Next
-version: "1.6.1"
+version: "1.7.0"
 slug: sillyhome_next
 description: Lernt automatisch aus deinem Verhalten und steuert freigegebene Aktoren
 url: http://192.168.6.31:3000/pino/sillyhome-next

app/actuators/models.py

@@ -52,6 +52,14 @@ class JobStatus(StrEnum):
     FAILED = "failed"
 
 
+class FeedbackKind(StrEnum):
+    CORRECT = "correct"
+    WRONG = "wrong"
+    TOO_EARLY = "too_early"
+    TOO_LATE = "too_late"
+    NEVER_AUTOMATE = "never_automate"
+
+
 class AssignmentCandidate(BaseModel):
     entity_id: str
     domain: str
@@ -146,6 +154,30 @@ class DecisionFactor(BaseModel):
     evidence: list[str] = Field(default_factory=list)
 
 
+class DecisionTrace(BaseModel):
+    trace_id: str = Field(pattern=r"^[a-z0-9_.-]{1,120}$")
+    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+    trigger_entity_id: str | None = None
+    trigger_state: str | None = None
+    target_state: str | None = None
+    confidence: float | None = Field(default=None, ge=0.0, le=1.0)
+    executed: bool = False
+    blocked: bool = False
+    reason: str = Field(default="", max_length=700)
+    blockers: list[str] = Field(default_factory=list)
+    duration_ms: int | None = Field(default=None, ge=0)
+
+
+class LatencyMeasurement(BaseModel):
+    measured_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+    trigger_entity_id: str | None = None
+    event_to_decision_ms: int | None = Field(default=None, ge=0)
+    decision_to_service_ms: int | None = Field(default=None, ge=0)
+    event_to_done_ms: int | None = Field(default=None, ge=0)
+    executed: bool = False
+    source: str = Field(default="manual", max_length=40)
+
+
 class AdaptiveWeightUpdate(BaseModel):
     entity_id: str
     previous_weight: float = Field(ge=0.0, le=1.0)
@@ -248,6 +280,32 @@ class RelatedAutomation(BaseModel):
     enabled: bool
 
 
+class ActuatorGroup(BaseModel):
+    group_id: str = Field(pattern=r"^[a-z0-9_-]{1,64}$")
+    name: str = Field(min_length=1, max_length=120)
+    area_name: str | None = Field(default=None, max_length=120)
+    member_entity_ids: list[str] = Field(default_factory=list)
+    reason: str = Field(default="", max_length=300)
+
+
+class SceneSuggestion(BaseModel):
+    scene_id: str = Field(pattern=r"^[a-z0-9_-]{1,64}$")
+    label: str = Field(min_length=1, max_length=120)
+    member_entity_ids: list[str] = Field(default_factory=list)
+    confidence: float = Field(default=0.0, ge=0.0, le=1.0)
+    reason: str = Field(default="", max_length=500)
+    last_seen_at: datetime | None = None
+
+
+class AgentInsight(BaseModel):
+    insight_id: str = Field(pattern=r"^[a-z0-9_.-]{1,120}$")
+    severity: str = Field(default="info", max_length=20)
+    title: str = Field(min_length=1, max_length=160)
+    detail: str = Field(min_length=1, max_length=700)
+    action: str | None = Field(default=None, max_length=300)
+    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+
+
 class BehaviorState(BaseModel):
     mode: BehaviorMode = BehaviorMode.SHADOW
     status: BehaviorStatus = BehaviorStatus.COLLECTING
@@ -281,6 +339,16 @@ class BehaviorState(BaseModel):
     automation_conflicts: list[AutomationConflict] = Field(default_factory=list)
     time_profiles: list[TimeProfile] = Field(default_factory=list)
     anomalies: list[AnomalyEvent] = Field(default_factory=list)
+    decision_timeline: list[DecisionTrace] = Field(default_factory=list)
+    latency_measurements: list[LatencyMeasurement] = Field(default_factory=list)
+    feedback_log: list[FeedbackKind] = Field(default_factory=list)
+    dry_run_enabled: bool = False
+    dry_run_started_at: datetime | None = None
+    dry_run_sample_count: int = Field(default=0, ge=0)
+    dry_run_hit_count: int = Field(default=0, ge=0)
+    actuator_groups: list[ActuatorGroup] = Field(default_factory=list)
+    scene_suggestions: list[SceneSuggestion] = Field(default_factory=list)
+    agent_insights: list[AgentInsight] = Field(default_factory=list)
 
 
 class ActuatorRecord(BaseModel):

app/actuators/store.py

@@ -100,6 +100,11 @@ class ActuatorStore:
             except ValueError as exc:
                 raise ValueError("Ungültiger Job-Queue-Status.") from exc
 
+    def save_job_queue(self, queue: JobQueueState) -> JobQueueState:
+        with self._lock:
+            self._persist_job_queue(queue)
+            return queue
+
     def start_job(
         self,
         *,

app/api/v1/actuators.py

@@ -10,7 +10,7 @@ from pydantic import BaseModel, Field
 
 from app.actuators.cache_db import DashboardCache
 from app.actuators.lifecycle import ActuatorReconciliationService
-from app.actuators.models import ActuatorRecord, AnomalyEvent, ReconciliationState, SensorWeightGroup
+from app.actuators.models import ActuatorRecord, AnomalyEvent, FeedbackKind, ReconciliationState, SensorWeightGroup
 from app.actuators.models import JobQueueItem, JobQueueState, JobStatus, SafetyProfile
 from app.actuators.store import ActuatorStore
 from app.behavior.engine import BehaviorEngine
@@ -55,6 +55,29 @@ class WeightOverrideRequest(BaseModel):
 class FeedbackRequest(BaseModel):
     correct: bool
     expected_state: str | None = Field(default=None, max_length=100)
+    kind: FeedbackKind | None = None
+
+
+class DryRunRequest(BaseModel):
+    enabled: bool
+
+
+class BackupPayload(BaseModel):
+    exported_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+    records: list[ActuatorRecord] = Field(default_factory=list)
+    reconciliation: ReconciliationState = Field(default_factory=ReconciliationState)
+    jobs: JobQueueState = Field(default_factory=JobQueueState)
+
+
+class RestoreRequest(BaseModel):
+    backup: BackupPayload
+    replace_existing: bool = False
+
+
+class RestoreResult(BaseModel):
+    restored_records: int = 0
+    skipped_existing: int = 0
+    restored_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
 
 
 class SafetyProfileRequest(BaseModel):
@@ -406,6 +429,48 @@ def list_anomalies(request: Request) -> list[AnomalyOverview]:
     return overview
 
 
+@router.get("/backup/export", response_model=BackupPayload)
+def export_backup(request: Request) -> BackupPayload:
+    store = getattr(request.app.state, "actuator_store", None)
+    if not isinstance(store, ActuatorStore):
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail="Actuator Store nicht initialisiert.",
+        )
+    return BackupPayload(
+        records=store.list(),
+        reconciliation=store.load_reconciliation_state(),
+        jobs=store.load_job_queue(),
+    )
+
+
+@router.post("/backup/restore", response_model=RestoreResult)
+def restore_backup(payload: RestoreRequest, request: Request) -> RestoreResult:
+    store = getattr(request.app.state, "actuator_store", None)
+    if not isinstance(store, ActuatorStore):
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail="Actuator Store nicht initialisiert.",
+        )
+    existing_ids = {record.actuator_entity_id for record in store.list()}
+    restored = 0
+    skipped = 0
+    for record in payload.backup.records:
+        if record.actuator_entity_id in existing_ids and not payload.replace_existing:
+            skipped += 1
+            continue
+        store.upsert(record)
+        restored += 1
+    store.save_reconciliation_state(payload.backup.reconciliation)
+    store.save_job_queue(payload.backup.jobs)
+    return RestoreResult(restored_records=restored, skipped_existing=skipped)
+
+
+@router.post("/planning/refresh", response_model=list[ActuatorRecord])
+def refresh_planning_insights(request: Request) -> list[ActuatorRecord]:
+    return _behavior(request).refresh_planning_insights()
+
+
 @router.get("", response_model=list[ActuatorRecord])
 def list_configured(request: Request) -> list[ActuatorRecord]:
     return _service(request).list_configured()
@@ -513,11 +578,24 @@ def record_feedback(
             actuator_entity_id,
             correct=payload.correct,
             expected_state=payload.expected_state,
+            kind=payload.kind,
         )
     except KeyError as exc:
         raise HTTPException(status_code=404, detail=str(exc)) from exc
 
 
+@router.post("/{actuator_entity_id}/dry-run", response_model=ActuatorRecord)
+def set_dry_run(
+    actuator_entity_id: str,
+    payload: DryRunRequest,
+    request: Request,
+) -> ActuatorRecord:
+    try:
+        return _behavior(request).set_dry_run(actuator_entity_id, enabled=payload.enabled)
+    except KeyError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+
+
 @router.post("/{actuator_entity_id}/safety", response_model=ActuatorRecord)
 def set_safety_profile(
     actuator_entity_id: str,

app/behavior/engine.py

@@ -3,12 +3,15 @@ from __future__ import annotations
 import logging
 from collections.abc import Sequence
 from datetime import datetime, timedelta, timezone
+from time import perf_counter
 from zoneinfo import ZoneInfo
 
 from app.actuators.models import (
     ActuatorRecord,
     AdaptiveWeightUpdate,
+    AgentInsight,
     AnomalyEvent,
+    ActuatorGroup,
     AutomationConflict,
     BehaviorMode,
     BehaviorPattern,
@@ -16,12 +19,16 @@ from app.actuators.models import (
     BehaviorState,
     BehaviorStatus,
     DecisionFactor,
+    DecisionTrace,
     ExecutionEvent,
+    FeedbackKind,
+    LatencyMeasurement,
     ManualOverride,
     ModelSnapshot,
     RelatedAutomation,
     SafetyProfile,
     SafetyStage,
+    SceneSuggestion,
     TimeProfile,
 )
 from app.actuators.store import ActuatorStore
@@ -35,6 +42,9 @@ _MAX_PATTERNS = 500
 _MAX_MODEL_SNAPSHOTS = 3
 _MAX_SNAPSHOT_PATTERNS = 120
 _MAX_EXECUTION_EVENTS = 100
+_MAX_DECISION_TRACES = 30
+_MAX_LATENCY_MEASUREMENTS = 50
+_MAX_FEEDBACK_LOG = 50
 _ACTION_LOGBOOK_TOLERANCE = timedelta(seconds=10)
 _CONTEXT_TRIGGER_TOLERANCE = timedelta(seconds=3)
 _OWN_ACTION_TOLERANCE = timedelta(seconds=20)
@@ -254,7 +264,11 @@ class BehaviorEngine:
         context_state_overrides: dict[str, str | None] | None = None,
         context_changed_at_overrides: dict[str, datetime | None] | None = None,
         current_entities: Sequence[HaEntitySummary] | None = None,
+        trigger_entity_id: str | None = None,
+        trigger_state: str | None = None,
+        event_received_at: datetime | None = None,
     ) -> ActuatorRecord:
+        started_perf = perf_counter()
         record = self._store.get(actuator_entity_id)
         now = datetime.now(timezone.utc)
         if current_entities is None:
@@ -344,6 +358,7 @@ class BehaviorEngine:
         else:
             safety_allowed = False
             safety_blockers = ["Keine fällige Vorhersage."]
+        decision_to_service_ms: int | None = None
         decision_factors = _decision_factors_for(record, current_context, prediction)
         behavior = record.behavior.model_copy(
             update={
@@ -383,12 +398,47 @@ class BehaviorEngine:
             domain = actuator_entity_id.split(".", 1)[0]
             service = service_for_state(domain, prediction.target_state)
             if service is not None:
+                if record.behavior.dry_run_enabled:
+                    behavior = behavior.model_copy(
+                        update={
+                            "prediction": prediction.model_copy(
+                                update={
+                                    "executed": False,
+                                    "execution_reason": (
+                                        "Dry-run: Aktion wäre ausgeführt worden."
+                                    ),
+                                }
+                            ),
+                            "dry_run_sample_count": record.behavior.dry_run_sample_count + 1,
+                            "reason": (
+                                f"Dry-run hätte {prediction.target_state!r} mit "
+                                f"{prediction.confidence:.0%} Sicherheit ausgeführt."
+                            ),
+                        }
+                    )
+                    return self._save_behavior(
+                        record,
+                        _append_decision_trace(
+                            behavior,
+                            trigger_entity_id=trigger_entity_id,
+                            trigger_state=trigger_state,
+                            prediction=prediction,
+                            safety_blockers=safety_blockers,
+                            duration_ms=_elapsed_ms(started_perf),
+                            event_received_at=event_received_at,
+                            decision_to_service_ms=None,
+                            executed=False,
+                            source="event" if event_received_at is not None else "manual",
+                        ),
+                    )
                 try:
+                    service_started_perf = perf_counter()
                     self._ha_reader.call_service(
                         domain,
                         service,
                         {"entity_id": actuator_entity_id},
                     )
+                    decision_to_service_ms = _elapsed_ms(service_started_perf)
                 except (HaClientError, ValueError) as exc:
                     logger.error(
                         "Predicted action failed for %s: %s",
@@ -400,7 +450,21 @@ class BehaviorEngine:
                             "reason": f"Vorhersage wurde aus Sicherheitsgründen nicht ausgeführt: {exc}"
                         }
                     )
-                    return self._save_behavior(record, behavior)
+                    return self._save_behavior(
+                        record,
+                        _append_decision_trace(
+                            behavior,
+                            trigger_entity_id=trigger_entity_id,
+                            trigger_state=trigger_state,
+                            prediction=prediction,
+                            safety_blockers=[str(exc)],
+                            duration_ms=_elapsed_ms(started_perf),
+                            event_received_at=event_received_at,
+                            decision_to_service_ms=None,
+                            executed=False,
+                            source="event" if event_received_at is not None else "manual",
+                        ),
+                    )
                 event = ExecutionEvent(
                     target_state=prediction.target_state,
                     executed_at=now,
@@ -434,6 +498,20 @@ class BehaviorEngine:
                         )
                     }
                 )
+        behavior = _append_decision_trace(
+            behavior,
+            trigger_entity_id=trigger_entity_id,
+            trigger_state=trigger_state,
+            prediction=prediction,
+            safety_blockers=safety_blockers,
+            duration_ms=_elapsed_ms(started_perf),
+            event_received_at=event_received_at,
+            decision_to_service_ms=(
+                decision_to_service_ms
+            ),
+            executed=bool(prediction is not None and behavior.prediction is not None and behavior.prediction.executed),
+            source="event" if event_received_at is not None else "manual",
+        )
         return self._save_behavior(record, behavior)
 
     def record_feedback(
@@ -442,6 +520,7 @@ class BehaviorEngine:
         *,
         correct: bool,
         expected_state: str | None = None,
+        kind: FeedbackKind | None = None,
     ) -> ActuatorRecord:
         record = self._store.get(actuator_entity_id)
         now = datetime.now(timezone.utc)
@@ -485,6 +564,7 @@ class BehaviorEngine:
             reason = "Vorhersage wurde vom Nutzer als korrekt bestätigt."
             correct_count = record.behavior.correct_feedback_count + 1
             incorrect_count = record.behavior.incorrect_feedback_count
+            feedback_kind = kind or FeedbackKind.CORRECT
         else:
             target = prediction.target_state if prediction is not None else None
             if target:
@@ -515,11 +595,24 @@ class BehaviorEngine:
             reason = "Vorhersage wurde vom Nutzer als falsch markiert."
             correct_count = record.behavior.correct_feedback_count
             incorrect_count = record.behavior.incorrect_feedback_count + 1
+            feedback_kind = kind or FeedbackKind.WRONG
+            if feedback_kind is FeedbackKind.NEVER_AUTOMATE:
+                safety = record.behavior.safety.model_copy(
+                    update={
+                        "manual_block": True,
+                        "updated_at": now,
+                        "note": "Durch Nutzerfeedback dauerhaft blockiert.",
+                    }
+                )
+            else:
+                safety = record.behavior.safety
         adaptive_updates, manual_override = _adapt_sensor_weights(
             record,
             current_context,
             correct=correct,
         )
+        if correct and prediction is not None:
+            safety = record.behavior.safety
         behavior = record.behavior.model_copy(
             update={
                 "patterns": patterns[-_MAX_PATTERNS:],
@@ -532,6 +625,11 @@ class BehaviorEngine:
                 "last_trained_at": now,
                 "correct_feedback_count": correct_count,
                 "incorrect_feedback_count": incorrect_count,
+                "feedback_log": [
+                    *record.behavior.feedback_log,
+                    feedback_kind,
+                ][-_MAX_FEEDBACK_LOG:],
+                "safety": safety,
                 "adaptive_weight_updates": [
                     *record.behavior.adaptive_weight_updates,
                     *adaptive_updates,
@@ -557,6 +655,43 @@ class BehaviorEngine:
         )
         return self._save_behavior(record_for_save, behavior)
 
+    def set_dry_run(self, actuator_entity_id: str, *, enabled: bool) -> ActuatorRecord:
+        record = self._store.get(actuator_entity_id)
+        now = datetime.now(timezone.utc)
+        behavior = record.behavior.model_copy(
+            update={
+                "dry_run_enabled": enabled,
+                "dry_run_started_at": now if enabled else record.behavior.dry_run_started_at,
+                "reason": (
+                    "Dry-run aktiv; freigegebene Aktionen werden protokolliert, aber nicht geschaltet."
+                    if enabled
+                    else "Dry-run beendet."
+                ),
+            }
+        )
+        return self._save_behavior(record, behavior)
+
+    def refresh_planning_insights(self) -> list[ActuatorRecord]:
+        records = self._store.list()
+        groups = _derive_actuator_groups(records)
+        scenes = _derive_scene_suggestions(records)
+        insights_by_actuator = _derive_agent_insights(records)
+        updated: list[ActuatorRecord] = []
+        for record in records:
+            behavior = record.behavior.model_copy(
+                update={
+                    "actuator_groups": [
+                        group for group in groups if record.actuator_entity_id in group.member_entity_ids
+                    ],
+                    "scene_suggestions": [
+                        scene for scene in scenes if record.actuator_entity_id in scene.member_entity_ids
+                    ],
+                    "agent_insights": insights_by_actuator.get(record.actuator_entity_id, []),
+                }
+            )
+            updated.append(self._save_behavior(record, behavior))
+        return updated
+
     def rollback_model(
         self,
         actuator_entity_id: str,
@@ -966,11 +1101,19 @@ class BehaviorEngine:
         - Wenn current_entities gesetzt ist, kommt die Auswertung direkt aus dem
           WebSocket-State-Cache statt aus einer frischen REST-Abfrage.
         """
+        event_received_at = datetime.now(timezone.utc)
+        records = self._store.list()
         # Aktor direkt evaluieren
-        for record in self._store.list():
+        for record in records:
             if record.actuator_entity_id == entity_id:
                 try:
-                    self.evaluate(record.actuator_entity_id, current_entities=current_entities)
+                    self.evaluate(
+                        record.actuator_entity_id,
+                        current_entities=current_entities,
+                        trigger_entity_id=entity_id,
+                        trigger_state=_event_state(new_state),
+                        event_received_at=event_received_at,
+                    )
                 except Exception:
                     logger.exception("Event-basierte Vorhersage fehlgeschlagen für %s", record.actuator_entity_id)
                 return
@@ -979,7 +1122,7 @@ class BehaviorEngine:
         # Kontext-Entity: alle Aktoren finden, die diesen Kontext nutzen
         affected_actuators = [
             record.actuator_entity_id
-            for record in self._store.list()
+            for record in records
             if (
                 record.assignment.selected_numeric_entity_id == entity_id
                 or entity_id in record.assignment.selected_context_entity_ids
@@ -992,6 +1135,9 @@ class BehaviorEngine:
                     context_state_overrides={entity_id: event_state},
                     context_changed_at_overrides={entity_id: event_changed_at},
                     current_entities=current_entities,
+                    trigger_entity_id=entity_id,
+                    trigger_state=event_state,
+                    event_received_at=event_received_at,
                 )
             except Exception:
                 logger.exception("Event-basierte Vorhersage fehlgeschlagen für %s", actuator_entity_id)
@@ -1019,6 +1165,208 @@ def _event_changed_at(new_state: dict[str, object] | None) -> datetime | None:
     return parsed
 
 
+def _elapsed_ms(started_perf: float) -> int:
+    return max(0, int((perf_counter() - started_perf) * 1000))
+
+
+def _append_decision_trace(
+    behavior: BehaviorState,
+    *,
+    trigger_entity_id: str | None,
+    trigger_state: str | None,
+    prediction: BehaviorPrediction | None,
+    safety_blockers: list[str],
+    duration_ms: int,
+    event_received_at: datetime | None,
+    decision_to_service_ms: int | None,
+    executed: bool,
+    source: str,
+) -> BehaviorState:
+    now = datetime.now(timezone.utc)
+    blocked = prediction is None or bool(safety_blockers)
+    trace = DecisionTrace(
+        trace_id=f"{now.strftime('%Y%m%d%H%M%S%f')}.{trigger_entity_id or 'manual'}",
+        created_at=now,
+        trigger_entity_id=trigger_entity_id,
+        trigger_state=trigger_state,
+        target_state=prediction.target_state if prediction is not None else None,
+        confidence=prediction.confidence if prediction is not None else None,
+        executed=executed,
+        blocked=blocked,
+        reason=(
+            prediction.execution_reason
+            if prediction is not None
+            else behavior.reason
+        ),
+        blockers=safety_blockers if prediction is not None else ["Keine fällige Vorhersage."],
+        duration_ms=duration_ms,
+    )
+    updated = behavior.model_copy(
+        update={
+            "decision_timeline": [
+                *behavior.decision_timeline,
+                trace,
+            ][-_MAX_DECISION_TRACES:],
+        }
+    )
+    if event_received_at is None:
+        return updated
+    return _append_latency_measurement(
+        updated,
+        trigger_entity_id=trigger_entity_id,
+        event_received_at=event_received_at,
+        event_to_decision_ms=duration_ms,
+        decision_to_service_ms=decision_to_service_ms,
+        executed=executed,
+        source=source,
+    )
+
+
+def _append_latency_measurement(
+    behavior: BehaviorState,
+    *,
+    trigger_entity_id: str | None,
+    event_received_at: datetime | None,
+    event_to_decision_ms: int | None,
+    decision_to_service_ms: int | None,
+    executed: bool,
+    source: str,
+) -> BehaviorState:
+    if event_received_at is None:
+        return behavior
+    now = datetime.now(timezone.utc)
+    event_to_done_ms = max(0, int((now - event_received_at).total_seconds() * 1000))
+    measurement = LatencyMeasurement(
+        measured_at=now,
+        trigger_entity_id=trigger_entity_id,
+        event_to_decision_ms=event_to_decision_ms,
+        decision_to_service_ms=decision_to_service_ms,
+        event_to_done_ms=event_to_done_ms,
+        executed=executed,
+        source=source,
+    )
+    return behavior.model_copy(
+        update={
+            "latency_measurements": [
+                *behavior.latency_measurements,
+                measurement,
+            ][-_MAX_LATENCY_MEASUREMENTS:],
+        }
+    )
+
+
+def _derive_actuator_groups(records: list[ActuatorRecord]) -> list[ActuatorGroup]:
+    by_area: dict[str, list[str]] = {}
+    for record in records:
+        area = _area_hint(record)
+        if area:
+            by_area.setdefault(area, []).append(record.actuator_entity_id)
+    return [
+        ActuatorGroup(
+            group_id=_slug(f"area_{area}"),
+            name=f"Raum {area}",
+            area_name=area,
+            member_entity_ids=sorted(entity_ids),
+            reason="Aktor-Gruppe aus gemeinsamer Raum-/Kontextzuordnung abgeleitet.",
+        )
+        for area, entity_ids in sorted(by_area.items())
+        if len(entity_ids) >= 2
+    ]
+
+
+def _derive_scene_suggestions(records: list[ActuatorRecord]) -> list[SceneSuggestion]:
+    scenes: list[SceneSuggestion] = []
+    by_context: dict[tuple[str, str], list[str]] = {}
+    for record in records:
+        for pattern in record.behavior.patterns:
+            for entity_id, state in pattern.context_states.items():
+                by_context.setdefault((entity_id, state), []).append(record.actuator_entity_id)
+    for (entity_id, state), members in sorted(by_context.items()):
+        unique_members = sorted(set(members))
+        if len(unique_members) < 2:
+            continue
+        scenes.append(
+            SceneSuggestion(
+                scene_id=_slug(f"{entity_id}_{state}"),
+                label=f"{entity_id} ist {state}",
+                member_entity_ids=unique_members,
+                confidence=min(1.0, len(members) / max(3, len(unique_members) * 2)),
+                reason="Mehrere Aktoren reagieren historisch auf denselben Kontext.",
+                last_seen_at=max(
+                    (
+                        pattern.observed_at
+                        for record in records
+                        for pattern in record.behavior.patterns
+                        if pattern.context_states.get(entity_id) == state
+                    ),
+                    default=None,
+                ),
+            )
+        )
+    return scenes[-20:]
+
+
+def _derive_agent_insights(records: list[ActuatorRecord]) -> dict[str, list[AgentInsight]]:
+    result: dict[str, list[AgentInsight]] = {}
+    for record in records:
+        insights: list[AgentInsight] = []
+        if record.behavior.automation_conflicts:
+            insights.append(
+                AgentInsight(
+                    insight_id=f"{record.actuator_entity_id}.automation_conflict",
+                    severity="warning",
+                    title="Automation-Konflikt prüfen",
+                    detail="Eine passende HA-Automation kann parallel zu SillyHome schalten.",
+                    action="Automation pausieren oder SillyHome im Shadow-Modus lassen.",
+                )
+            )
+        if record.behavior.latency_measurements:
+            durations = [
+                item.event_to_done_ms
+                for item in record.behavior.latency_measurements
+                if item.event_to_done_ms is not None
+            ]
+            if durations and max(durations) > 1500:
+                insights.append(
+                    AgentInsight(
+                        insight_id=f"{record.actuator_entity_id}.latency",
+                        severity="warning",
+                        title="Schalt-Latenz beobachten",
+                        detail=f"Letzte maximale Event-Latenz: {max(durations)} ms.",
+                        action="WebSocket-Status, HA-Servicezeit und Sensor-Routing pruefen.",
+                    )
+                )
+        if record.behavior.incorrect_feedback_count > record.behavior.correct_feedback_count:
+            insights.append(
+                AgentInsight(
+                    insight_id=f"{record.actuator_entity_id}.feedback",
+                    severity="warning",
+                    title="Viele negative Feedbacks",
+                    detail="Das Modell trifft aktuell mehr falsche als richtige Entscheidungen.",
+                    action="Kontextzuordnung, Gewichtung oder Modell-Rollback pruefen.",
+                )
+            )
+        result[record.actuator_entity_id] = insights[:5]
+    return result
+
+
+def _area_hint(record: ActuatorRecord) -> str | None:
+    for candidate in [*record.context_candidates, *record.numeric_candidates]:
+        if candidate.area_name:
+            return candidate.area_name
+    return None
+
+
+def _slug(value: str) -> str:
+    result = []
+    for char in value.lower():
+        if char.isalnum():
+            result.append(char)
+        elif char in {".", "_", "-", " "}:
+            result.append("_")
+    return "".join(result).strip("_")[:64] or "item"
+
+
 def _confidence_threshold_for(profile: SafetyProfile, target_state: str) -> float:
     if target_state == "on" and profile.min_confidence_on is not None:
         return profile.min_confidence_on

app/main.py

@@ -117,7 +117,7 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
 app = FastAPI(
     title="SillyHome Next API",
     description="Lokales Smart-Home-Intelligenzsystem für Home Assistant.",
-    version="1.6.1",
+    version="1.7.0",
     lifespan=lifespan,
 )
 app.state.settings = load_settings()
@@ -311,8 +311,6 @@ async def _ha_event_listener(app: FastAPI, client: HaClient) -> None:
                             continue
                         new_state = event_data.get("new_state")
                         _update_ha_state_cache(state_cache, entity_id, new_state)
-                        if not _is_relevant_state_change(store, str(entity_id)):
-                            continue
                         # Prüfe, ob Entity ein Aktor oder relevanter Kontext ist
                         # Sofortige Vorhersage für betroffene Aktoren auslösen
                         await asyncio.to_thread(

docs/V1_7_0_OPERATING_GUIDE.md

@@ -0,0 +1,45 @@
+# SillyHome Next v1.7.0 Operating Guide
+
+v1.7.0 erweitert den Produktivbetrieb um Diagnose, Backup, Dry-run und
+Planungshilfen.
+
+## Diagnose
+
+- Jede Auswertung speichert eine kompakte `decision_timeline` am Aktor.
+- Event-basierte Auswertungen speichern zusaetzlich `latency_measurements`.
+- Die Timeline beantwortet: was war der Ausloeser, welches Ziel wurde
+  vorhergesagt, wurde geschaltet oder blockiert, und warum.
+
+## Backup und Restore
+
+- `GET /v1/actuators/backup/export` exportiert Aktoren, Reconciliation-Status
+  und Job-Historie als JSON.
+- `POST /v1/actuators/backup/restore` spielt diesen Stand wieder ein.
+- Ohne `replace_existing=true` werden vorhandene Aktoren nicht ueberschrieben.
+
+## Dry-run
+
+- `POST /v1/actuators/{entity_id}/dry-run` aktiviert oder beendet den Testmodus.
+- Im Dry-run werden freigegebene Aktionen bewertet und protokolliert, aber nicht
+  an Home Assistant gesendet.
+
+## Feedback
+
+Feedback akzeptiert neben `correct`/`expected_state` nun optionale Typen:
+
+- `correct`
+- `wrong`
+- `too_early`
+- `too_late`
+- `never_automate`
+
+`never_automate` setzt eine manuelle Sicherheitssperre am Aktor.
+
+## Planung
+
+`POST /v1/actuators/planning/refresh` berechnet lokale Hinweise:
+
+- Aktorgruppen aus gemeinsamen Raum-/Kontextdaten
+- einfache Szenenvorschlaege aus gemeinsamem Kontextverhalten
+- Agent-Insights fuer Konflikte, Latenz und auffaelliges Feedback
+

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "sillyhome-next"
-version = "1.6.1"
+version = "1.7.0"
 description = "Lokales Smart-Home-Intelligenzsystem für Home Assistant"
 requires-python = ">=3.11"
 dependencies = [

tests/api/test_actuators.py

@@ -354,6 +354,51 @@ def test_feedback_adapts_sensor_weights_and_model_can_rollback(tmp_path: Path) -
     assert rollback.json()["behavior"]["active_model_version"] == version_id
 
 
+def test_feedback_never_automate_sets_manual_block(tmp_path: Path) -> None:
+    with TestClient(app) as client:
+        _install_service(tmp_path)
+        client.post(
+            "/v1/actuators",
+            json={"actuator_entity_id": "light.abstellkammer"},
+        )
+
+        feedback = client.post(
+            "/v1/actuators/light.abstellkammer/feedback",
+            json={"correct": False, "kind": "never_automate"},
+        )
+
+    assert feedback.status_code == 200
+    payload = feedback.json()
+    assert payload["behavior"]["safety"]["manual_block"] is True
+    assert payload["behavior"]["feedback_log"][-1] == "never_automate"
+
+
+def test_backup_export_restore_and_planning_refresh(tmp_path: Path) -> None:
+    with TestClient(app) as client:
+        _install_service(tmp_path)
+        client.post("/v1/actuators", json={"actuator_entity_id": "light.abstellkammer"})
+
+        backup = client.get("/v1/actuators/backup/export")
+        dry_run = client.post(
+            "/v1/actuators/light.abstellkammer/dry-run",
+            json={"enabled": True},
+        )
+        planning = client.post("/v1/actuators/planning/refresh")
+        restore = client.post(
+            "/v1/actuators/backup/restore",
+            json={"backup": backup.json(), "replace_existing": True},
+        )
+
+    assert backup.status_code == 200
+    assert backup.json()["records"][0]["actuator_entity_id"] == "light.abstellkammer"
+    assert dry_run.status_code == 200
+    assert dry_run.json()["behavior"]["dry_run_enabled"] is True
+    assert planning.status_code == 200
+    assert "agent_insights" in planning.json()[0]["behavior"]
+    assert restore.status_code == 200
+    assert restore.json()["restored_records"] == 1
+
+
 def test_summary_is_lightweight_and_uses_cached_entity_metadata(tmp_path: Path) -> None:
     with TestClient(app) as client:
         _install_service(tmp_path)

tests/behavior/test_engine.py

@@ -778,3 +778,129 @@ def test_state_change_uses_event_cache_without_rest_state_query(
     assert reader.service_calls == [
         ("light", "turn_on", {"entity_id": "light.storage"})
     ]
+
+
+def test_event_evaluation_records_decision_timeline_and_latency(
+    tmp_path: Path,
+) -> None:
+    now = datetime.now(timezone.utc).replace(microsecond=0)
+    settings = _settings(tmp_path)
+    store = ActuatorStore(settings.actuator_store)
+    record = store.configure("light.storage")
+    record = record.model_copy(
+        update={
+            "assignment": record.assignment.model_copy(
+                update={"selected_context_entity_ids": ["binary_sensor.storage_door"]}
+            ),
+            "behavior": record.behavior.model_copy(
+                update={
+                    "mode": BehaviorMode.ACTIVE,
+                    "status": BehaviorStatus.TRAINED,
+                    "activation_ready": True,
+                    "patterns": [
+                        BehaviorPattern(
+                            target_state="on",
+                            minute_of_day=60,
+                            weekday=0,
+                            context_states={"binary_sensor.storage_door": "on"},
+                            trigger_entity_id="binary_sensor.storage_door",
+                            trigger_from_state="off",
+                            trigger_to_state="on",
+                            source="automation",
+                            weight=1.0,
+                            observed_at=now - timedelta(days=days_ago),
+                        )
+                        for days_ago in (3, 2, 1)
+                    ],
+                }
+            ),
+        }
+    )
+    store.upsert(record)
+    reader = FakeBehaviorReader(
+        entities=[
+            HaEntitySummary(entity_id="light.storage", domain="light", state="off"),
+            HaEntitySummary(
+                entity_id="binary_sensor.storage_door",
+                domain="binary_sensor",
+                state="on",
+                last_changed=now,
+            ),
+        ],
+        history=[],
+        logbook=[],
+    )
+    engine = BehaviorEngine(ha_reader=reader, store=store, settings=settings)
+
+    result = engine.evaluate(
+        "light.storage",
+        trigger_entity_id="binary_sensor.storage_door",
+        trigger_state="on",
+        event_received_at=now,
+    )
+
+    trace = result.behavior.decision_timeline[-1]
+    latency = result.behavior.latency_measurements[-1]
+    assert trace.trigger_entity_id == "binary_sensor.storage_door"
+    assert trace.target_state == "on"
+    assert trace.executed is True
+    assert latency.trigger_entity_id == "binary_sensor.storage_door"
+    assert latency.executed is True
+
+
+def test_dry_run_records_without_calling_service(tmp_path: Path) -> None:
+    now = datetime.now(timezone.utc).replace(microsecond=0)
+    settings = _settings(tmp_path)
+    store = ActuatorStore(settings.actuator_store)
+    record = store.configure("light.storage")
+    record = record.model_copy(
+        update={
+            "assignment": record.assignment.model_copy(
+                update={"selected_context_entity_ids": ["binary_sensor.storage_door"]}
+            ),
+            "behavior": record.behavior.model_copy(
+                update={
+                    "mode": BehaviorMode.ACTIVE,
+                    "status": BehaviorStatus.TRAINED,
+                    "activation_ready": True,
+                    "dry_run_enabled": True,
+                    "patterns": [
+                        BehaviorPattern(
+                            target_state="on",
+                            minute_of_day=60,
+                            weekday=0,
+                            context_states={"binary_sensor.storage_door": "on"},
+                            trigger_entity_id="binary_sensor.storage_door",
+                            trigger_from_state="off",
+                            trigger_to_state="on",
+                            source="automation",
+                            weight=1.0,
+                            observed_at=now - timedelta(days=days_ago),
+                        )
+                        for days_ago in (3, 2, 1)
+                    ],
+                }
+            ),
+        }
+    )
+    store.upsert(record)
+    reader = FakeBehaviorReader(
+        entities=[
+            HaEntitySummary(entity_id="light.storage", domain="light", state="off"),
+            HaEntitySummary(
+                entity_id="binary_sensor.storage_door",
+                domain="binary_sensor",
+                state="on",
+                last_changed=now,
+            ),
+        ],
+        history=[],
+        logbook=[],
+    )
+    engine = BehaviorEngine(ha_reader=reader, store=store, settings=settings)
+
+    result = engine.evaluate("light.storage")
+
+    assert reader.service_calls == []
+    assert result.behavior.dry_run_sample_count == 1
+    assert result.behavior.decision_timeline[-1].executed is False