Add actuator simulation tuning

Add production diagnostics and planning features
Fix HA websocket keepalive regression
2026-06-18 19:06:47 +02:00 · 2026-06-18 11:53:53 +02:00 · 2026-06-18 07:48:46 +02:00
14 changed files with 1190 additions and 32 deletions
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,22 @@
 # 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
+  nicht erst ueber Fallback oder manuelle Statusabfrage zu Schaltungen.
+
 ## 1.6.0 - 2026-06-18
 - `/v1/actuators/dashboard/system` und `/dashboard/start` lesen fuer
  Cache-Status nur noch SQLite-Metadaten statt den kompletten Entity-Cache zu
--- a/README.md
+++ b/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
--- a/addon/config.yaml
+++ b/addon/config.yaml
@@ -1,5 +1,5 @@
 name: SillyHome Next
-version: "1.6.0"
+version: "1.7.1"
 slug: sillyhome_next
 description: Lernt automatisch aus deinem Verhalten und steuert freigegebene Aktoren
 url: http://192.168.6.31:3000/pino/sillyhome-next
--- a/app/actuators/models.py
+++ b/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 +154,43 @@ class DecisionFactor(BaseModel):
    evidence: list[str] = Field(default_factory=list)


+class SimulationOutcome(BaseModel):
+    scenario_id: str = Field(pattern=r"^[a-z0-9_.-]{1,120}$")
+    actuator_entity_id: str
+    sensor_states: dict[str, str] = Field(default_factory=dict)
+    sensor_weights: dict[str, float] = Field(default_factory=dict)
+    prediction: BehaviorPrediction | None = None
+    decision_factors: list[DecisionFactor] = Field(default_factory=list)
+    would_execute: bool = False
+    blockers: list[str] = Field(default_factory=list)
+    score: float = Field(default=0.0, ge=0.0, le=1.0)
+    recommendation: str = Field(default="", max_length=700)
+
+
+class DecisionTrace(BaseModel):
+    trace_id: str = Field(pattern=r"^[a-z0-9_.-]{1,120}$")
+    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 +293,32 @@ 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 +352,16 @@ 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):
--- a/app/actuators/store.py
+++ b/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,
        *,
--- a/app/api/v1/actuators.py
+++ b/app/api/v1/actuators.py
@@ -10,7 +10,14 @@ 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,
+    SimulationOutcome,
+)
 from app.actuators.models import JobQueueItem, JobQueueState, JobStatus, SafetyProfile
 from app.actuators.store import ActuatorStore
 from app.behavior.engine import BehaviorEngine
@@ -52,9 +59,40 @@ class WeightOverrideRequest(BaseModel):
    note: str | None = Field(default=None, max_length=500)


+class SimulationRequest(BaseModel):
+    sensor_states: dict[str, str] = Field(default_factory=dict)
+    sensor_weights: dict[str, float] = Field(default_factory=dict)
+    state_options: dict[str, list[str]] = Field(default_factory=dict)
+    include_current: bool = True
+    max_results: int = Field(default=8, ge=1, le=20)
+
+
 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 +444,48 @@ 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()
@@ -502,6 +582,28 @@ def evaluate_actuator(
        raise HTTPException(status_code=404, detail=str(exc)) from exc


+@router.post("/{actuator_entity_id}/simulate", response_model=list[SimulationOutcome])
+def simulate_actuator(
+    actuator_entity_id: str,
+    payload: SimulationRequest,
+    request: Request,
+) -> list[SimulationOutcome]:
+    try:
+        _validate_simulation_payload(payload)
+        return _behavior(request).simulate(
+            actuator_entity_id,
+            sensor_states=payload.sensor_states,
+            sensor_weights=payload.sensor_weights,
+            state_options=payload.state_options,
+            include_current=payload.include_current,
+            max_results=payload.max_results,
+        )
+    except KeyError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+    except ValueError as exc:
+        raise HTTPException(status_code=422, detail=str(exc)) from exc
+
+
@router.post("/{actuator_entity_id}/feedback", response_model=ActuatorRecord)
 def record_feedback(
    actuator_entity_id: str,
@@ -513,11 +615,24 @@ 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,
@@ -807,6 +922,22 @@ def _validate_weight_payload(payload: WeightOverrideRequest) -> None:
                raise ValueError(f"Ungültige Entity-ID in Gruppe {group.name}: {entity_id}")


+def _validate_simulation_payload(payload: SimulationRequest) -> None:
+    for entity_id in [
+        *payload.sensor_states.keys(),
+        *payload.sensor_weights.keys(),
+        *payload.state_options.keys(),
+    ]:
+        if "." not in entity_id:
+            raise ValueError(f"Ungültige Entity-ID: {entity_id}")
+    for entity_id, weight in payload.sensor_weights.items():
+        if not 0.0 <= weight <= 1.0:
+            raise ValueError(f"Ungültige Gewichtung für {entity_id}: {weight}")
+    for entity_id, states in payload.state_options.items():
+        if not states:
+            raise ValueError(f"Keine Zustände für {entity_id} angegeben.")
+
+
 def _reconciliation_state_or_default(request: Request) -> ReconciliationState:
    store = getattr(request.app.state, "actuator_store", None)
    if not isinstance(store, ActuatorStore):
--- a/app/behavior/engine.py
+++ b/app/behavior/engine.py
@@ -1,14 +1,18 @@
 from __future__ import annotations

 import logging
+from itertools import product
 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 +20,17 @@ from app.actuators.models import (
    BehaviorState,
    BehaviorStatus,
    DecisionFactor,
+    DecisionTrace,
    ExecutionEvent,
+    FeedbackKind,
+    LatencyMeasurement,
    ManualOverride,
    ModelSnapshot,
    RelatedAutomation,
    SafetyProfile,
    SafetyStage,
+    SceneSuggestion,
+    SimulationOutcome,
    TimeProfile,
 )
 from app.actuators.store import ActuatorStore
@@ -35,6 +44,9 @@ _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 +266,11 @@ 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:
@@ -319,6 +335,7 @@ class BehaviorEngine:
            record.behavior.patterns,
            current_context=current_context,
            current_context_changed_at=current_context_changed_at,
+            context_weights=_context_weights_for(record),
            now=now,
            min_support=self._settings.min_behavior_actions,
            window_minutes=self._settings.prediction_window_minutes,
@@ -344,6 +361,7 @@ 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 +401,47 @@ 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 +453,21 @@ 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,
@@ -434,14 +501,139 @@ 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 simulate(
+        self,
+        actuator_entity_id: str,
+        *,
+        sensor_states: dict[str, str],
+        sensor_weights: dict[str, float],
+        state_options: dict[str, list[str]],
+        max_results: int,
+        include_current: bool = True,
+    ) -> list[SimulationOutcome]:
+        record = self._store.get(actuator_entity_id)
+        now = datetime.now(timezone.utc)
+        current_entities = self._ha_reader.read_entities()
+        entities = {entity.entity_id: entity for entity in current_entities}
+        actuator = entities.get(actuator_entity_id)
+        if actuator is None:
+            raise KeyError("Aktor ist aktuell nicht in Home Assistant verfügbar.")
+        selected_context_ids = [
+            entity_id
+            for entity_id in [
+                record.assignment.selected_numeric_entity_id,
+                *record.assignment.selected_context_entity_ids,
+            ]
+            if entity_id
+        ]
+        if not selected_context_ids:
+            return []
+        base_context = {
+            entity_id: entities[entity_id].state
+            for entity_id in selected_context_ids
+            if entity_id in entities and entities[entity_id].state is not None
+        }
+        base_changed_at = {
+            entity_id: entities[entity_id].last_changed
+            for entity_id in base_context
+        }
+        context_weights = _context_weights_for(record)
+        for entity_id, weight in sensor_weights.items():
+            if entity_id in selected_context_ids:
+                context_weights[entity_id] = max(0.0, min(1.0, weight))
+        scenarios = _simulation_contexts(
+            base_context,
+            sensor_states=sensor_states,
+            state_options=state_options,
+            selected_context_ids=selected_context_ids,
+            include_current=include_current,
+        )
+        outcomes: list[SimulationOutcome] = []
+        for index, context in enumerate(scenarios[:64], start=1):
+            prediction_context: dict[str, str | None] = dict(context)
+            changed_at = dict(base_changed_at)
+            for entity_id, state in context.items():
+                if base_context.get(entity_id) != state:
+                    changed_at[entity_id] = now
+            prediction = predict_behavior(
+                record.behavior.patterns,
+                current_context=prediction_context,
+                current_context_changed_at=changed_at,
+                context_weights=context_weights,
+                now=now,
+                min_support=self._settings.min_behavior_actions,
+                window_minutes=self._settings.prediction_window_minutes,
+                causal_window_seconds=self._settings.prediction_interval_seconds * 2,
+                timezone_name=self._settings.timezone,
+            )
+            if prediction is not None:
+                would_execute, blockers = self._assess_safety(record, actuator.state, prediction, now)
+                recommendation = (
+                    f"Bestes Szenario: {prediction.target_state} mit {prediction.confidence:.0%}."
+                    if would_execute
+                    else (
+                        f"Vorhersage {prediction.target_state} mit {prediction.confidence:.0%}, "
+                        "aber blockiert: " + " ".join(blockers)
+                    )
+                )
+            else:
+                would_execute = False
+                blockers = ["Keine fällige Vorhersage."]
+                recommendation = "Dieses Szenario erzeugt keine fällige Vorhersage."
+            outcomes.append(
+                SimulationOutcome(
+                    scenario_id=f"scenario-{index}",
+                    actuator_entity_id=actuator_entity_id,
+                    sensor_states=context,
+                    sensor_weights={
+                        entity_id: round(context_weights.get(entity_id, 1.0), 4)
+                        for entity_id in context
+                    },
+                    prediction=prediction,
+                    decision_factors=_decision_factors_for(
+                        record,
+                        prediction_context,
+                        prediction,
+                        context_weights=context_weights,
+                    ),
+                    would_execute=would_execute,
+                    blockers=blockers,
+                    score=round(prediction.confidence if prediction is not None else 0.0, 4),
+                    recommendation=recommendation,
+                )
+            )
+        return sorted(
+            outcomes,
+            key=lambda item: (
+                item.prediction is None,
+                -item.score,
+                item.scenario_id,
+            ),
+        )[:max_results]
+
    def record_feedback(
        self,
        actuator_entity_id: str,
        *,
        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 +677,7 @@ 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 +708,24 @@ 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 +738,11 @@ 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 +768,43 @@ 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 +1214,19 @@ 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 +1235,7 @@ 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 +1248,9 @@ 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 +1278,208 @@ 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
@@ -1031,15 +1492,21 @@ def _decision_factors_for(
    record: ActuatorRecord,
    current_context: dict[str, str | None],
    prediction: BehaviorPrediction | None,
+    *,
+    context_weights: dict[str, float] | None = None,
 ) -> list[DecisionFactor]:
    factors: list[DecisionFactor] = []
+    weights = context_weights or {}
    candidates = {
        candidate.entity_id: candidate
        for candidate in [*record.numeric_candidates, *record.context_candidates]
    }
    for entity_id, state in current_context.items():
        candidate = candidates.get(entity_id)
-        weight = candidate.effective_weight if candidate is not None else 1.0
+        weight = weights.get(
+            entity_id,
+            candidate.effective_weight if candidate is not None else 1.0,
+        )
        relevance = candidate.confidence if candidate is not None else 0.5
        contribution = round(min(1.0, weight * relevance), 4)
        factors.append(
@@ -1075,6 +1542,62 @@ def _decision_factors_for(
    return sorted(factors, key=lambda item: (-item.contribution, item.label))[:12]


+def _context_weights_for(record: ActuatorRecord) -> dict[str, float]:
+    weights = {
+        candidate.entity_id: candidate.effective_weight
+        for candidate in [*record.numeric_candidates, *record.context_candidates]
+    }
+    override = record.manual_override
+    if override is not None:
+        for entity_id, weight in override.sensor_weights.items():
+            weights[entity_id] = max(0.0, min(1.0, weight))
+        for group in override.sensor_weight_groups:
+            for entity_id in group.entity_ids:
+                weights[entity_id] = max(0.0, min(1.0, group.weight))
+    return weights
+
+
+def _simulation_contexts(
+    base_context: dict[str, str | None],
+    *,
+    sensor_states: dict[str, str],
+    state_options: dict[str, list[str]],
+    selected_context_ids: list[str],
+    include_current: bool,
+) -> list[dict[str, str]]:
+    selected = set(selected_context_ids)
+    base = {
+        entity_id: state
+        for entity_id, state in base_context.items()
+        if entity_id in selected and state is not None
+    }
+    for entity_id, state in sensor_states.items():
+        if entity_id in selected:
+            base[entity_id] = state
+    option_items = [
+        (
+            entity_id,
+            list(dict.fromkeys(state for state in states if state))[:6],
+        )
+        for entity_id, states in state_options.items()
+        if entity_id in selected and states
+    ][:6]
+    contexts: list[dict[str, str]] = []
+    if include_current or not option_items:
+        contexts.append(dict(base))
+    if option_items:
+        keys = [item[0] for item in option_items]
+        value_lists = [item[1] for item in option_items]
+        for values in product(*value_lists):
+            context = dict(base)
+            context.update(dict(zip(keys, values, strict=True)))
+            if context not in contexts:
+                contexts.append(context)
+            if len(contexts) >= 64:
+                break
+    return contexts
+
+
 def _knowledge_lines(
    record: ActuatorRecord,
    sample_count: int,
@@ -1408,6 +1931,7 @@ def predict_behavior(
    min_support: int,
    window_minutes: int,
    current_context_changed_at: dict[str, datetime | None] | None = None,
+    context_weights: dict[str, float] | None = None,
    causal_window_seconds: int = 120,
    timezone_name: str = "Europe/Berlin",
 ) -> BehaviorPrediction | None:
@@ -1438,14 +1962,10 @@ def predict_behavior(
                for entity_id, expected in pattern.context_states.items()
                if entity_id in current_context
            ]
-            context_score = (
-                sum(
-                    current_context[entity_id] == expected
-                    for entity_id, expected in comparable
-                )
-                / len(comparable)
-                if comparable
-                else 0.5
+            context_score = _weighted_context_score(
+                comparable,
+                current_context,
+                context_weights or {},
            )
            score = pattern.weight * (0.85 + 0.15 * context_score)
            by_state.setdefault(pattern.target_state, []).append(score)
@@ -1469,11 +1989,10 @@ def predict_behavior(
            for entity_id, expected in pattern.context_states.items()
            if entity_id in current_context
        ]
-        context_score = (
-            sum(current_context[entity_id] == expected for entity_id, expected in comparable)
-            / len(comparable)
-            if comparable
-            else 0.5
+        context_score = _weighted_context_score(
+            comparable,
+            current_context,
+            context_weights or {},
        )
        score = pattern.weight * (
            0.45 * time_score + 0.45 * context_score + 0.10 * weekday_score
@@ -1506,6 +2025,25 @@ def predict_behavior(
    )


+def _weighted_context_score(
+    comparable: list[tuple[str, str]],
+    current_context: dict[str, str | None],
+    context_weights: dict[str, float],
+) -> float:
+    if not comparable:
+        return 0.5
+    total_weight = 0.0
+    matched_weight = 0.0
+    for entity_id, expected in comparable:
+        weight = max(0.0, min(1.0, context_weights.get(entity_id, 1.0)))
+        total_weight += weight
+        if current_context.get(entity_id) == expected:
+            matched_weight += weight
+    if total_weight <= 0:
+        return 0.5
+    return matched_weight / total_weight
+
+
 def service_for_state(domain: str, target_state: str) -> str | None:
    if domain in {"fan", "humidifier", "light", "media_player", "remote", "switch"}:
        return {"on": "turn_on", "off": "turn_off"}.get(target_state)
--- a/app/main.py
+++ b/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="1.6.0",
+    version="1.7.1",
    lifespan=lifespan,
 )
 app.state.settings = load_settings()
@@ -259,11 +259,7 @@ async def _ha_event_listener(app: FastAPI, client: HaClient) -> None:
        if ws_status is not None:
            ws_status.status = "connecting"
        try:
-            async with websockets.connect(
-                ws_url,
-                ping_interval=30,
-                ping_timeout=30,
-            ) as websocket:
+            async with websockets.connect(ws_url, ping_interval=None) as websocket:
                auth_required_msg = await websocket.recv()
                auth_required_data = json.loads(auth_required_msg)
                if auth_required_data.get("type") != "auth_required":
@@ -315,8 +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(
--- a/app/static/index.html
+++ b/app/static/index.html
@@ -1252,6 +1252,42 @@ async function showActuator(actuatorId, evaluationMessage = "") {
        <button class="secondary" onclick="saveWeightOverrides('${escapeHtml(record.actuator_entity_id)}', true)">Als Gruppe speichern</button>
      </details>
    `;
+    const simulationControls = weightedCandidates.length ? `
+      <details class="manual-context" open>
+        <summary>Aktor-Simulation</summary>
+        <p class="muted">Teste Sensorzustände und Gewichtungen, ohne Home Assistant zu schalten.</p>
+        <div class="card-list">
+          ${weightedCandidates.map(candidate => {
+            const effective = Math.round((candidate.effective_weight ?? 1) * 100);
+            const currentState = candidate.state || "";
+            const stateOptions = candidate.domain === "binary_sensor"
+              ? "off,on"
+              : currentState;
+            return `
+              <article class="actuator-card">
+                <div class="card-title">
+                  <div>
+                    <strong>${escapeHtml(candidate.friendly_name || candidate.entity_id)}</strong>
+                    <div class="entity-id">${escapeHtml(candidate.entity_id)}</div>
+                  </div>
+                  <span class="chip">Simulation</span>
+                </div>
+                <label for="sim-state-${escapeHtml(candidate.entity_id)}">Simulierter Zustand</label>
+                <input id="sim-state-${escapeHtml(candidate.entity_id)}" data-sim-state-entity="${escapeHtml(candidate.entity_id)}" value="${escapeHtml(currentState)}" placeholder="on, off, 12 ...">
+                <label for="sim-options-${escapeHtml(candidate.entity_id)}">Zustände vergleichen</label>
+                <input id="sim-options-${escapeHtml(candidate.entity_id)}" data-sim-options-entity="${escapeHtml(candidate.entity_id)}" value="${escapeHtml(stateOptions)}" placeholder="on,off">
+                <label for="sim-weight-${escapeHtml(candidate.entity_id)}">Simulierte Gewichtung in %</label>
+                <input id="sim-weight-${escapeHtml(candidate.entity_id)}" data-sim-weight-entity="${escapeHtml(candidate.entity_id)}" type="number" min="0" max="100" step="5" value="${effective}">
+              </article>
+            `;
+          }).join("")}
+        </div>
+        <div class="actions">
+          <button class="secondary" onclick="simulateActuator('${escapeHtml(record.actuator_entity_id)}')">Bestes Szenario berechnen</button>
+        </div>
+        <div id="simulation-result" class="decision-list"></div>
+      </details>
+    ` : "<p class='muted'>Für die Simulation müssen zuerst Kontextsensoren ausgewählt sein.</p>";
    const currentContextControls = contexts.length
      ? `<ul>${contexts.map(entityId => `
          <li>
@@ -1510,6 +1546,7 @@ async function showActuator(actuatorId, evaluationMessage = "") {
      <h3>Sensor-Gewichtung</h3>
      ${weightControls}
      ${weightGroupControls}
+      ${simulationControls}
      <h3>Verwendete Sensoren/Zustände ändern</h3>
      ${currentContextControls}
      ${manualAssignment}
@@ -1610,6 +1647,58 @@ async function saveWeightOverrides(actuatorId, includeNewGroup = false) {
  }
 }

+async function simulateActuator(actuatorId) {
+  const sensorStates = {};
+  const sensorWeights = {};
+  const stateOptions = {};
+  for (const input of document.querySelectorAll("[data-sim-state-entity]")) {
+    const value = input.value.trim();
+    if (value) sensorStates[input.dataset.simStateEntity] = value;
+  }
+  for (const input of document.querySelectorAll("[data-sim-weight-entity]")) {
+    const value = Number(input.value);
+    if (Number.isFinite(value)) {
+      sensorWeights[input.dataset.simWeightEntity] = Math.max(0, Math.min(100, value)) / 100;
+    }
+  }
+  for (const input of document.querySelectorAll("[data-sim-options-entity]")) {
+    const values = input.value.split(/[,\s]+/).map(value => value.trim()).filter(Boolean);
+    if (values.length) stateOptions[input.dataset.simOptionsEntity] = values;
+  }
+  const box = document.getElementById("simulation-result");
+  box.innerHTML = "<p class='muted'>Simulation läuft ...</p>";
+  try {
+    const results = await api(`v1/actuators/${encodeURIComponent(actuatorId)}/simulate`, {
+      method: "POST",
+      body: JSON.stringify({
+        sensor_states: sensorStates,
+        sensor_weights: sensorWeights,
+        state_options: stateOptions,
+        max_results: 6,
+      }),
+    });
+    box.innerHTML = results.length ? results.map((result, index) => {
+      const prediction = result.prediction;
+      const factors = result.decision_factors || [];
+      return `
+        <div class="decision-row">
+          <header>
+            <strong>${index === 0 ? "Bestes Szenario" : `Szenario ${index + 1}`}</strong>
+            <span class="chip">${prediction ? `${Math.round(prediction.confidence * 100)} % · ${escapeHtml(prediction.target_state)}` : "keine Vorhersage"}</span>
+          </header>
+          <p>${escapeHtml(result.recommendation || "")}</p>
+          <p class="muted">Zustände: ${Object.entries(result.sensor_states || {}).map(([entity, state]) => `${escapeHtml(entity)}=${escapeHtml(state)}`).join(", ") || "keine"}</p>
+          <p class="muted">Gewichtung: ${Object.entries(result.sensor_weights || {}).map(([entity, weight]) => `${escapeHtml(entity)}=${Math.round(weight * 100)} %`).join(", ") || "Standard"}</p>
+          ${result.blockers?.length ? `<p class="warn">${result.blockers.map(escapeHtml).join(" ")}</p>` : "<p class='ok'>Würde nach Sicherheitsprüfung schalten.</p>"}
+          ${factors.length ? `<ul>${factors.slice(0, 4).map(factor => `<li>${escapeHtml(factor.label)}: ${Math.round((factor.contribution || 0) * 100)} % Beitrag</li>`).join("")}</ul>` : ""}
+        </div>
+      `;
+    }).join("") : "<p class='muted'>Keine Simulationsergebnisse.</p>";
+  } catch (error) {
+    box.innerHTML = `<p class="bad">${escapeHtml(error.message)}</p>`;
+  }
+}
+
 async function saveManualAssignment(actuatorId) {
  const numericEntityId = document.getElementById("manual-numeric-select").value || null;
  const selectedContextIds = Array.from(
--- a/docs/V1_7_0_OPERATING_GUIDE.md
+++ b/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
+
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"

 [project]
 name = "sillyhome-next"
-version = "1.6.0"
+version = "1.7.1"
 description = "Lokales Smart-Home-Intelligenzsystem für Home Assistant"
 requires-python = ">=3.11"
 dependencies = [
--- a/tests/api/test_actuators.py
+++ b/tests/api/test_actuators.py
@@ -3,6 +3,7 @@ from __future__ import annotations
 from datetime import datetime, timedelta, timezone
 from pathlib import Path
 from time import perf_counter
+from zoneinfo import ZoneInfo

 import pytest
 from fastapi.testclient import TestClient
@@ -10,7 +11,7 @@ from fastapi.testclient import TestClient
 from app.api.v1.actuators import _deduplicate_actuator_ids
 from app.actuators.cache_db import DashboardCache
 from app.actuators.lifecycle import ActuatorReconciliationService
-from app.actuators.models import JobStatus, ModelSnapshot
+from app.actuators.models import BehaviorPattern, JobStatus, ModelSnapshot
 from app.actuators.store import ActuatorStore
 from app.behavior.engine import BehaviorEngine
 from app.config import Settings
@@ -272,6 +273,91 @@ def test_weight_override_endpoint_updates_sensor_relevance(tmp_path: Path) -> No
    assert numeric["sensor.abstellkammer_illuminance"]["effective_weight"] == 0.75


+def test_actuator_simulation_ranks_sensor_states_without_switching(tmp_path: Path) -> None:
+    with TestClient(app) as client:
+        _install_service(tmp_path)
+        client.post("/v1/actuators", json={"actuator_entity_id": "light.abstellkammer"})
+        client.post(
+            "/v1/actuators/light.abstellkammer/assignment",
+            json={
+                "numeric_entity_id": "sensor.abstellkammer_illuminance",
+                "context_entity_ids": ["binary_sensor.abstellkammer_motion"],
+            },
+        )
+        store = app.state.actuator_store
+        record = store.get("light.abstellkammer")
+        now = datetime.now(timezone.utc)
+        local = now.astimezone(ZoneInfo("Europe/Berlin"))
+        local_minute = local.hour * 60 + local.minute
+        patterns = [
+            BehaviorPattern(
+                target_state="on",
+                minute_of_day=local_minute,
+                weekday=now.weekday(),
+                context_states={
+                    "sensor.abstellkammer_illuminance": "12",
+                    "binary_sensor.abstellkammer_motion": "on",
+                },
+                source="user",
+                weight=1.0,
+                observed_at=now,
+            )
+            for _ in range(3)
+        ]
+        patterns.extend(
+            [
+                BehaviorPattern(
+                    target_state="off",
+                    minute_of_day=local_minute,
+                    weekday=now.weekday(),
+                    context_states={
+                        "sensor.abstellkammer_illuminance": "12",
+                        "binary_sensor.abstellkammer_motion": "off",
+                    },
+                    source="user",
+                    weight=0.5,
+                    observed_at=now,
+                )
+                for _ in range(3)
+            ]
+        )
+        store.upsert(
+            record.model_copy(
+                update={
+                    "behavior": record.behavior.model_copy(
+                        update={
+                            "patterns": patterns,
+                            "sample_count": len(patterns),
+                            "high_confidence_sample_count": len(patterns),
+                            "activation_ready": True,
+                            "activation_reason": "Testfreigabe.",
+                        }
+                    )
+                }
+            )
+        )
+
+        response = client.post(
+            "/v1/actuators/light.abstellkammer/simulate",
+            json={
+                "state_options": {"binary_sensor.abstellkammer_motion": ["off", "on"]},
+                "sensor_weights": {
+                    "binary_sensor.abstellkammer_motion": 1.0,
+                    "sensor.abstellkammer_illuminance": 0.25,
+                },
+                "max_results": 2,
+            },
+        )
+
+    assert response.status_code == 200
+    payload = response.json()
+    assert len(payload) == 2
+    assert payload[0]["prediction"]["target_state"] == "on"
+    assert payload[0]["sensor_states"]["binary_sensor.abstellkammer_motion"] == "on"
+    assert payload[0]["sensor_weights"]["sensor.abstellkammer_illuminance"] == 0.25
+    assert app.state.ha_reader.service_calls == []
+
+
 def test_safety_profile_can_block_actuator_manually(tmp_path: Path) -> None:
    with TestClient(app) as client:
        _install_service(tmp_path)
@@ -354,6 +440,51 @@ 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)
--- a/tests/behavior/test_engine.py
+++ b/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
--- a/tests/test_main.py
+++ b/tests/test_main.py
@@ -94,8 +94,7 @@ def test_ha_event_listener_processes_state_change(tmp_path: Path) -> None:

            connect.assert_called_once_with(
                "ws://homeassistant:8123/api/websocket",
-                ping_interval=30,
-                ping_timeout=30,
+                ping_interval=None,
            )
        assert fake_ws.sent == [
            {"type": "auth", "access_token": "test-token"},
Author	SHA1	Message	Date
Otto	8070a85b52	Add actuator simulation tuning Some checks failed quality / test (3.11) (push) Has been cancelled Details quality / test (3.13) (push) Has been cancelled Details	2026-06-18 19:06:47 +02:00
Otto	575211f0db	Add production diagnostics and planning features Some checks failed quality / test (3.11) (push) Has been cancelled Details quality / test (3.13) (push) Has been cancelled Details	2026-06-18 11:53:53 +02:00
Otto	d9dc186f9b	Fix HA websocket keepalive regression Some checks failed quality / test (3.11) (push) Has been cancelled Details quality / test (3.13) (push) Has been cancelled Details	2026-06-18 07:48:46 +02:00