Add room management settings

CHANGELOG.md

@@ -1,5 +1,42 @@
 # Changelog
 
+## 1.7.6 - 2026-07-26
+- Einstellungen um eine Raumverwaltung erweitert: Räume zeigen Aktoren,
+  aktive/optionale/nicht nötige Sensoren und lesbare Vorhersage-Regeln in
+  einer gemeinsamen Ansicht.
+- Neue API `/v1/actuators/settings/rooms` liefert kompakte Verwaltungsdaten
+  für Raumkarten, Sensorvorschläge, Aktoren und noch nicht verwaltete
+  Vorschläge.
+- Licht-/Schalter-Zuordnung darf bei eindeutigem Tür-/Öffnungskontext ohne
+  numerischen Helligkeitssensor arbeiten, z. B. Tür auf -> Licht an und Tür zu
+  -> Licht aus.
+
+## 1.7.5 - 2026-07-26
+- Dashboard-Sprachumschaltung übersetzt jetzt auch dynamisch gerenderte
+  Status-, Discovery-, Detail-, Listen-, Button- und Aufklapptexte.
+- Aufklapp-Hinweise (`expand`/`collapse`) kommen nicht mehr fest aus CSS auf
+  Deutsch, sondern werden pro Sprache gesetzt.
+- Detail-Cache wird beim Sprachwechsel geleert, damit keine alten deutschen
+  HTML-Fragmente in der englischen Oberfläche sichtbar bleiben.
+
+## 1.7.4 - 2026-07-26
+- Dashboard-Sprachumschaltung aktualisiert statische Texte, Labels,
+  Platzhalter und wichtige Laufzeittexte direkt beim Wechsel.
+- Verhaltenslernen speichert Lichtattribute wie Helligkeit und Farbwerte aus
+  der Home-Assistant-Historie und gibt sie bei Lichtvorhersagen an den
+  `light.turn_on` Service weiter.
+- Kontext-Discovery erkennt Umlaute/Raumvarianten robuster, ignoriert
+  Markenwörter wie `lidl` als falsche Gemeinsamkeit und bevorzugt Raum-
+  Präsenzsensoren für Lidl-/Treppenlichter.
+- Lüftungen bevorzugen Luftfeuchte und Belegungs-/Präsenzkontext; gelernte
+  Sensorwechsel dürfen jetzt eine Verzögerung haben, z. B. WC besetzt -> nach
+  2-3 Minuten Lüftung an.
+- Briefkasten-Reset-Buttons können Schrank-/Entnahme-Türen als Kontext
+  erkennen; Button-Aktoren können im aktiven Modus per `press` ausgeführt
+  werden.
+- PV-/Akku-/Verbrauchssensoren werden als Energiemanagement-Kontext stärker
+  einsortiert.
+
 ## 1.7.0 - 2026-06-18
 - Produktiv-Ausbau fuer Schaltvertrauen: persistente Entscheidungs-Timeline,
   Event-Latenzmessungen und Dry-run pro Aktor.

addon/config.yaml

@@ -1,5 +1,5 @@
 name: SillyHome Next
-version: "1.7.0"
+version: "1.7.6"
 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/lifecycle.py

@@ -46,6 +46,8 @@ _STOPWORDS = frozenset(
         "entity",
         "humidity",
         "illuminance",
+        "led",
+        "lidl",
         "light",
         "licht",
         "lichtschalter",
@@ -138,6 +140,33 @@ _AUTO_CONTEXT_CLASSES = frozenset({
     "presence",
     "window",
 })
+_PRESENCE_TOKENS = frozenset({
+    "besetzt",
+    "occupied",
+    "occupancy",
+    "presence",
+    "prasenz",
+    "praesenz",
+    "motion",
+    "bewegung",
+    "bewegungsmelder",
+})
+_MAILBOX_TOKENS = frozenset({"briefkasten", "mailbox", "post"})
+_CABINET_TOKENS = frozenset({"schrank", "cabinet"})
+_PV_TOKENS = frozenset({
+    "pv",
+    "solar",
+    "photovoltaik",
+    "akku",
+    "batterie",
+    "battery",
+    "einspeisung",
+    "wechselrichter",
+    "inverter",
+    "netzbezug",
+    "grid",
+    "verbrauch",
+})
 
 
 class ActuatorReconciliationService:
@@ -517,6 +546,25 @@ class ActuatorReconciliationService:
             if candidate.auto_accepted
         ][: _MAX_CONTEXT_SELECTIONS]
         top_contexts = [candidate.entity_id for candidate in accepted_contexts]
+        if (
+            top_numeric is not None
+            and actuator.domain in {"light", "switch"}
+            and any(
+                (candidate.device_class or "") in {"door", "garage_door", "opening", "window"}
+                for candidate in accepted_contexts
+            )
+        ):
+            return AssignmentSelection(
+                selected_numeric_entity_id=None,
+                selected_context_entity_ids=top_contexts,
+                source=AssignmentSource.AUTOMATIC,
+                confidence=max(candidate.confidence for candidate in accepted_contexts),
+                review_required=False,
+                reason=(
+                    "Tür-/Öffnungskontext automatisch erkannt. Für diese "
+                    "direkte Schaltlogik ist kein Helligkeitssensor erforderlich."
+                ),
+            )
         if top_numeric is None:
             if accepted_contexts:
                 return AssignmentSelection(
@@ -864,6 +912,18 @@ def _has_context_relationship(actuator: HaEntitySummary, entity: HaEntitySummary
         return True
     if _metadata_tokens(actuator).intersection(_metadata_tokens(entity)):
         return True
+    actuator_tokens = _metadata_tokens(actuator, include_stopwords=True)
+    entity_tokens = _metadata_tokens(entity, include_stopwords=True)
+    if _is_mailbox_reset_candidate(actuator_tokens, entity_tokens, entity):
+        return True
+    if actuator.domain in {"fan", "humidifier"} and (
+        _is_presence_context(entity) or entity.device_class in {"humidity", "moisture"}
+    ):
+        return True
+    if actuator.domain in {"climate", "cover", "fan", "humidifier", "light", "switch"} and (
+        entity_tokens.intersection(_PV_TOKENS)
+    ):
+        return True
     entity_tokens = _metadata_tokens(entity, include_stopwords=True)
     return bool(
         entity_tokens.intersection(_OUTDOOR_TOKENS)
@@ -878,6 +938,18 @@ def _eligible_for_auto_context(
     device_class = candidate.device_class or ""
     if device_class in _AUTO_CONTEXT_CLASSES:
         return True
+    if actuator.domain in {"fan", "humidifier"} and device_class in {
+        "humidity",
+        "moisture",
+        "temperature",
+    }:
+        return True
+    if actuator.domain in {"fan", "humidifier", "light", "switch"} and _is_presence_candidate(candidate):
+        return True
+    actuator_tokens = _metadata_tokens(actuator, include_stopwords=True)
+    candidate_tokens = _candidate_tokens(candidate, include_stopwords=True)
+    if _is_mailbox_reset_candidate(actuator_tokens, candidate_tokens, candidate):
+        return True
     if (
         actuator.device_name
         and candidate.device_name
@@ -899,6 +971,7 @@ def _score_candidate(
     score = 0.0
     actuator_tokens = _metadata_tokens(actuator)
     entity_tokens = _metadata_tokens(entity)
+    full_entity_tokens = _metadata_tokens(entity, include_stopwords=True)
     overlap = sorted(actuator_tokens.intersection(entity_tokens))
     if overlap:
         score += min(0.4, 0.1 * len(overlap))
@@ -924,6 +997,31 @@ def _score_candidate(
     if entity.device_class in preferred_device_classes:
         score += 0.2
         evidence.append(f"Passende device_class: {entity.device_class}")
+    if context and actuator.domain in {"fan", "humidifier"} and entity.device_class in {
+        "humidity",
+        "moisture",
+    }:
+        score += 0.3
+        evidence.append("Luftfeuchtigkeit ist primärer Kontext für Lüftung.")
+    if not context and actuator.domain in {"fan", "humidifier"} and entity.device_class in {
+        "humidity",
+        "moisture",
+    }:
+        score += 0.3
+        evidence.append("Luftfeuchtigkeit ist primärer Messwert für Lüftung.")
+    if context and actuator.domain in {"fan", "humidifier", "light", "switch"} and _is_presence_context(entity):
+        score += 0.3
+        evidence.append("Anwesenheit/Belegung ist primärer Schaltkontext.")
+    if context and _is_mailbox_reset_candidate(
+        _metadata_tokens(actuator, include_stopwords=True),
+        _metadata_tokens(entity, include_stopwords=True),
+        entity,
+    ):
+        score += 0.45
+        evidence.append("Briefkasten-Reset passt zur Schrank-/Entnahme-Tür.")
+    if full_entity_tokens.intersection(_PV_TOKENS):
+        score += 0.12 if context else 0.18
+        evidence.append("PV-/Akku-/Verbrauchswert ist als Energiemanagement-Kontext relevant.")
     if not context and actuator.domain == "light" and entity.device_class == "illuminance":
         score += 0.2
         evidence.append("Beleuchtungsstärke wird für Lichtaktoren bevorzugt.")
@@ -1068,11 +1166,83 @@ def _metadata_tokens(entity: HaEntitySummary, *, include_stopwords: bool = False
     for value in raw_values:
         if value is None:
             continue
-        for token in _TOKEN_PATTERN.findall(value.lower().replace("_", " ")):
-            if len(token) < 3 or (not include_stopwords and token in _STOPWORDS):
+        for token in _TOKEN_PATTERN.findall(_normalize_text(value)):
+            if (len(token) < 3 and token != "wc") or (not include_stopwords and token in _STOPWORDS):
                 continue
             tokens.add(token)
-    return tokens
+    return _expand_room_tokens(tokens)
+
+
+def _candidate_tokens(
+    candidate: AssignmentCandidate,
+    *,
+    include_stopwords: bool = False,
+) -> set[str]:
+    raw_values = [
+        candidate.entity_id,
+        candidate.friendly_name,
+        candidate.area_name,
+        candidate.device_name,
+    ]
+    tokens: set[str] = set()
+    for value in raw_values:
+        if value is None:
+            continue
+        for token in _TOKEN_PATTERN.findall(_normalize_text(value)):
+            if (len(token) < 3 and token != "wc") or (not include_stopwords and token in _STOPWORDS):
+                continue
+            tokens.add(token)
+    return _expand_room_tokens(tokens)
+
+
+def _expand_room_tokens(tokens: set[str]) -> set[str]:
+    expanded = set(tokens)
+    if "gaste" in expanded:
+        expanded.add("gaeste")
+    if {"gaste", "wc"}.issubset(expanded) or {"gaeste", "wc"}.issubset(expanded):
+        expanded.add("gaestewc")
+    if {"gaeste", "zimmer"}.issubset(expanded):
+        expanded.add("gaestezimmer")
+    return expanded
+
+
+def _normalize_text(value: str) -> str:
+    return (
+        value.lower()
+        .replace("_", " ")
+        .replace("ä", "ae")
+        .replace("ö", "oe")
+        .replace("ü", "ue")
+        .replace("ß", "ss")
+    )
+
+
+def _is_presence_context(entity: HaEntitySummary) -> bool:
+    if entity.device_class in {"motion", "occupancy", "presence"}:
+        return True
+    return bool(_metadata_tokens(entity, include_stopwords=True).intersection(_PRESENCE_TOKENS))
+
+
+def _is_presence_candidate(candidate: AssignmentCandidate) -> bool:
+    if candidate.device_class in {"motion", "occupancy", "presence"}:
+        return True
+    return bool(_candidate_tokens(candidate, include_stopwords=True).intersection(_PRESENCE_TOKENS))
+
+
+def _is_mailbox_reset_candidate(
+    actuator_tokens: set[str],
+    context_tokens: set[str],
+    entity: HaEntitySummary | AssignmentCandidate,
+) -> bool:
+    if not actuator_tokens.intersection(_MAILBOX_TOKENS):
+        return False
+    if not context_tokens.intersection(_CABINET_TOKENS):
+        return False
+    return entity.domain == "binary_sensor" and entity.device_class in {
+        "door",
+        "garage_door",
+        "opening",
+    }
 
 
 def _history_signature(sensor_id: str, points: list[NumericHistoryPoint]) -> str:

app/actuators/models.py

@@ -123,12 +123,14 @@ class ModelLifecycleState(BaseModel):
 
 class BehaviorPattern(BaseModel):
     target_state: str = Field(min_length=1, max_length=100)
+    target_attributes: dict[str, object] = Field(default_factory=dict)
     minute_of_day: int = Field(ge=0, le=1439)
     weekday: int = Field(ge=0, le=6)
     context_states: dict[str, str] = Field(default_factory=dict)
     trigger_entity_id: str | None = None
     trigger_from_state: str | None = None
     trigger_to_state: str | None = None
+    trigger_delay_seconds: int | None = Field(default=None, ge=0)
     source: str = Field(default="observed", max_length=40)
     weight: float = Field(default=1.0, ge=0.1, le=1.0)
     observed_at: datetime
@@ -136,6 +138,7 @@ class BehaviorPattern(BaseModel):
 
 class BehaviorPrediction(BaseModel):
     target_state: str
+    target_attributes: dict[str, object] = Field(default_factory=dict)
     confidence: float = Field(ge=0.0, le=1.0)
     generated_at: datetime
     reason: str
@@ -154,6 +157,19 @@ 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))

app/api/v1/actuators.py

@@ -10,7 +10,16 @@ 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, FeedbackKind, ReconciliationState, SensorWeightGroup
+from app.actuators.models import (
+    ActuatorRecord,
+    AnomalyEvent,
+    AssignmentCandidate,
+    BehaviorPattern,
+    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
@@ -52,6 +61,14 @@ 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)
@@ -162,6 +179,49 @@ class AnomalyOverview(BaseModel):
     anomalies: list[AnomalyEvent] = Field(default_factory=list)
 
 
+class RoomManagementSensor(BaseModel):
+    entity_id: str
+    domain: str
+    role: str
+    category: str
+    friendly_name: str | None = None
+    device_class: str | None = None
+    state: str | None = None
+    confidence: float = Field(default=0.0, ge=0.0, le=1.0)
+    active: bool = False
+    optional: bool = False
+    not_required: bool = False
+    reason: str
+
+
+class RoomManagementActuator(BaseModel):
+    actuator_entity_id: str
+    friendly_name: str | None = None
+    domain: str
+    behavior_mode: str
+    behavior_status: str
+    lifecycle_status: str
+    sample_count: int = 0
+    selected_numeric_entity_id: str | None = None
+    selected_context_entity_ids: list[str] = Field(default_factory=list)
+    sensors: list[RoomManagementSensor] = Field(default_factory=list)
+    prediction_rules: list[str] = Field(default_factory=list)
+    management_hint: str
+
+
+class RoomManagementGroup(BaseModel):
+    room: str
+    actuator_count: int
+    sensors: list[RoomManagementSensor] = Field(default_factory=list)
+    actuators: list[RoomManagementActuator] = Field(default_factory=list)
+    prediction_rules: list[str] = Field(default_factory=list)
+
+
+class RoomManagementOverview(BaseModel):
+    rooms: list[RoomManagementGroup] = Field(default_factory=list)
+    unmanaged_actuators: list[ActuatorSuggestion] = Field(default_factory=list)
+
+
 @router.get("/discovery", response_model=list[HaEntitySummary])
 def discover_actuators(
     request: Request,
@@ -429,6 +489,96 @@ def list_anomalies(request: Request) -> list[AnomalyOverview]:
     return overview
 
 
+@router.get("/settings/rooms", response_model=RoomManagementOverview)
+def room_management_overview(request: Request) -> RoomManagementOverview:
+    service = _service(request)
+    records = service.list_configured()
+    try:
+        entities = {entity.entity_id: entity for entity in service._ha_reader.read_entities()}
+    except Exception:
+        entities = _load_cached_entity_map(
+            request,
+            {
+                entity_id
+                for record in records
+                for entity_id in [
+                    record.actuator_entity_id,
+                    record.assignment.selected_numeric_entity_id,
+                    *record.assignment.selected_context_entity_ids,
+                    *[candidate.entity_id for candidate in record.numeric_candidates[:8]],
+                    *[candidate.entity_id for candidate in record.context_candidates[:12]],
+                ]
+                if entity_id
+            },
+        )
+    configured_ids = {record.actuator_entity_id for record in records}
+    rooms: dict[str, RoomManagementGroup] = {}
+    for record in records:
+        actuator = entities.get(record.actuator_entity_id)
+        room = (
+            actuator.area_name
+            if actuator is not None and actuator.area_name
+            else _candidate_room(record)
+        ) or "Ohne Raum"
+        selected_context_ids = set(record.assignment.selected_context_entity_ids)
+        selected_numeric_id = record.assignment.selected_numeric_entity_id
+        selected_ids = {selected_numeric_id, *selected_context_ids} - {None}
+        ranked_candidates = _rank_management_candidates(record)
+        has_opening_context = any(
+            candidate.entity_id in selected_context_ids
+            and (candidate.device_class or "") in {"door", "garage_door", "opening", "window"}
+            for candidate in ranked_candidates
+        )
+        sensors = [
+            _management_sensor(
+                candidate,
+                entities.get(candidate.entity_id),
+                active=candidate.entity_id in selected_ids,
+                optional=(
+                    candidate.role is EntityRole.MEASUREMENT
+                    and candidate.entity_id != selected_numeric_id
+                ),
+                not_required=(
+                    record.actuator_entity_id.startswith(("light.", "switch."))
+                    and has_opening_context
+                    and (candidate.device_class or "") == "illuminance"
+                ),
+            )
+            for candidate in ranked_candidates[:12]
+        ]
+        actuator_group = RoomManagementActuator(
+            actuator_entity_id=record.actuator_entity_id,
+            friendly_name=actuator.friendly_name if actuator is not None else None,
+            domain=record.actuator_entity_id.split(".", 1)[0],
+            behavior_mode=record.behavior.mode.value,
+            behavior_status=record.behavior.status.value,
+            lifecycle_status=record.lifecycle.status.value,
+            sample_count=record.behavior.sample_count,
+            selected_numeric_entity_id=selected_numeric_id,
+            selected_context_entity_ids=record.assignment.selected_context_entity_ids,
+            sensors=sensors,
+            prediction_rules=_prediction_rule_lines(record, ranked_candidates),
+            management_hint=_management_hint(record, has_opening_context),
+        )
+        if room not in rooms:
+            rooms[room] = RoomManagementGroup(room=room, actuator_count=0)
+        rooms[room].actuators.append(actuator_group)
+        rooms[room].actuator_count += 1
+        rooms[room].prediction_rules = _unique_lines([
+            *rooms[room].prediction_rules,
+            *actuator_group.prediction_rules,
+        ])[:8]
+        rooms[room].sensors = _merge_room_sensors(rooms[room].sensors, sensors)
+    unmanaged = [
+        suggestion for suggestion in suggest_actuators(request, service._ha_reader)
+        if suggestion.entity_id not in configured_ids
+    ][:10]
+    return RoomManagementOverview(
+        rooms=sorted(rooms.values(), key=lambda item: item.room.lower()),
+        unmanaged_actuators=unmanaged,
+    )
+
+
 @router.get("/backup/export", response_model=BackupPayload)
 def export_backup(request: Request) -> BackupPayload:
     store = getattr(request.app.state, "actuator_store", None)
@@ -567,6 +717,28 @@ 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,
@@ -885,6 +1057,236 @@ def _validate_weight_payload(payload: WeightOverrideRequest) -> None:
                 raise ValueError(f"Ungültige Entity-ID in Gruppe {group.name}: {entity_id}")
 
 
+def _candidate_room(record: ActuatorRecord) -> str | None:
+    for candidate in [*record.context_candidates, *record.numeric_candidates]:
+        if candidate.area_name:
+            return candidate.area_name
+    return None
+
+
+def _rank_management_candidates(record: ActuatorRecord) -> list[AssignmentCandidate]:
+    selected_ids = {
+        entity_id
+        for entity_id in [
+            record.assignment.selected_numeric_entity_id,
+            *record.assignment.selected_context_entity_ids,
+        ]
+        if entity_id
+    }
+    candidates = {
+        candidate.entity_id: candidate
+        for candidate in [*record.context_candidates, *record.numeric_candidates]
+    }
+    ranked = sorted(
+        candidates.values(),
+        key=lambda item: (
+            item.entity_id not in selected_ids,
+            _management_sort_group(item),
+            -item.confidence,
+            -item.score,
+            item.entity_id,
+        ),
+    )
+    return ranked
+
+
+def _management_sort_group(candidate: AssignmentCandidate) -> str:
+    device_class = candidate.device_class or ""
+    if device_class in {"door", "garage_door", "opening", "window"}:
+        return "01_opening"
+    if device_class in {"motion", "occupancy", "presence"}:
+        return "02_presence"
+    if device_class == "illuminance":
+        return "03_brightness"
+    if device_class in {"humidity", "moisture"}:
+        return "04_humidity"
+    if candidate.role is EntityRole.MEASUREMENT:
+        return "08_measurement"
+    return f"20_{candidate.domain}_{device_class}"
+
+
+def _management_sensor(
+    candidate: AssignmentCandidate,
+    entity: HaEntitySummary | None,
+    *,
+    active: bool,
+    optional: bool,
+    not_required: bool,
+) -> RoomManagementSensor:
+    if not_required:
+        reason = "Nicht nötig, weil ein Tür-/Öffnungskontakt die Lichtlogik direkt erklärt."
+    elif active:
+        reason = "Wird aktuell für Lernen und Vorhersage verwendet."
+    elif optional:
+        reason = "Optionaler Messwert; nur verwenden, wenn Helligkeit oder Verbrauch wirklich steuern soll."
+    else:
+        reason = ", ".join(candidate.evidence[:2]) or "Naheliegender Kontext aus Raum, Gerät oder Namen."
+    return RoomManagementSensor(
+        entity_id=candidate.entity_id,
+        domain=candidate.domain,
+        role=candidate.role.value,
+        category=_sensor_category_label(candidate),
+        friendly_name=candidate.friendly_name,
+        device_class=candidate.device_class,
+        state=entity.state if entity is not None else None,
+        confidence=candidate.confidence,
+        active=active,
+        optional=optional,
+        not_required=not_required,
+        reason=reason,
+    )
+
+
+def _sensor_category_label(candidate: AssignmentCandidate) -> str:
+    device_class = candidate.device_class or ""
+    if device_class in {"door", "garage_door", "opening", "window"}:
+        return "Tür/Fenster"
+    if device_class in {"motion", "occupancy", "presence"}:
+        return "Präsenz"
+    if device_class == "illuminance":
+        return "Helligkeit"
+    if device_class in {"humidity", "moisture"}:
+        return "Luftfeuchtigkeit"
+    if device_class in {"power", "energy", "current", "voltage"}:
+        return "Energie"
+    if candidate.domain in {"cover"}:
+        return "Rollo/Cover"
+    if candidate.domain in {"zone", "person", "device_tracker"}:
+        return "Zone/Person"
+    return "Kontext"
+
+
+def _merge_room_sensors(
+    existing: list[RoomManagementSensor],
+    incoming: list[RoomManagementSensor],
+) -> list[RoomManagementSensor]:
+    by_id = {sensor.entity_id: sensor for sensor in existing}
+    for sensor in incoming:
+        current = by_id.get(sensor.entity_id)
+        if current is None:
+            by_id[sensor.entity_id] = sensor
+            continue
+        by_id[sensor.entity_id] = current.model_copy(
+            update={
+                "active": current.active or sensor.active,
+                "optional": current.optional and sensor.optional,
+                "not_required": current.not_required and sensor.not_required,
+                "confidence": max(current.confidence, sensor.confidence),
+            }
+        )
+    return sorted(
+        by_id.values(),
+        key=lambda item: (
+            not item.active,
+            item.not_required,
+            item.category,
+            item.friendly_name or item.entity_id,
+        ),
+    )[:18]
+
+
+def _prediction_rule_lines(
+    record: ActuatorRecord,
+    candidates: list[AssignmentCandidate],
+) -> list[str]:
+    lines = _pattern_rule_lines(record.behavior.patterns)
+    if lines:
+        return lines[:8]
+    selected_contexts = [
+        candidate
+        for candidate in candidates
+        if candidate.entity_id in set(record.assignment.selected_context_entity_ids)
+    ]
+    result: list[str] = []
+    for candidate in selected_contexts:
+        label = candidate.friendly_name or candidate.entity_id
+        device_class = candidate.device_class or ""
+        if device_class in {"door", "garage_door", "opening", "window"}:
+            result.extend([
+                f"{label} geöffnet -> {record.actuator_entity_id} an.",
+                f"{label} geschlossen -> {record.actuator_entity_id} aus.",
+            ])
+        elif device_class in {"motion", "occupancy", "presence"}:
+            result.extend([
+                f"{label} erkannt -> {record.actuator_entity_id} an, bei Licht bevorzugt gedimmt.",
+                f"{label} aus -> {record.actuator_entity_id} verzögert ausschalten.",
+            ])
+        elif device_class in {"humidity", "moisture"}:
+            result.append(f"{label} hoch -> {record.actuator_entity_id} einschalten, bis Feuchte wieder normal ist.")
+    if record.assignment.selected_numeric_entity_id:
+        result.append(
+            f"{record.assignment.selected_numeric_entity_id} nur als Messwert verwenden, nicht als Pflichtsensor."
+        )
+    return _unique_lines(result)[:8] or ["Noch keine stabile Vorhersage; erst Kontext prüfen und weiter beobachten."]
+
+
+def _pattern_rule_lines(patterns: list[BehaviorPattern]) -> list[str]:
+    buckets: dict[tuple[str, tuple[tuple[str, str], ...]], int] = {}
+    attrs: dict[tuple[str, tuple[tuple[str, str], ...]], dict[str, object]] = {}
+    for pattern in patterns[-120:]:
+        context = tuple(sorted(pattern.context_states.items()))
+        key = (pattern.target_state, context)
+        buckets[key] = buckets.get(key, 0) + 1
+        attrs[key] = pattern.target_attributes
+    ordered = sorted(buckets.items(), key=lambda item: (-item[1], item[0]))
+    lines: list[str] = []
+    for (target_state, context), count in ordered[:8]:
+        conditions = ", ".join(f"{entity}={state}" for entity, state in context[:3])
+        if not conditions:
+            conditions = "aktueller Zeit-/Nutzungskontext passt"
+        attr_text = _attribute_text(attrs.get((target_state, context), {}))
+        lines.append(f"{conditions} -> {target_state}{attr_text} ({count}x gelernt).")
+    return lines
+
+
+def _attribute_text(attributes: dict[str, object]) -> str:
+    if not attributes:
+        return ""
+    brightness = attributes.get("brightness")
+    if isinstance(brightness, int | float):
+        percent = round(max(0, min(255, float(brightness))) / 255 * 100)
+        return f", Helligkeit {percent} %"
+    return ""
+
+
+def _management_hint(record: ActuatorRecord, has_opening_context: bool) -> str:
+    if has_opening_context and record.actuator_entity_id.startswith(("light.", "switch.")):
+        return "Direkte Türlogik: kein Helligkeitssensor nötig, Sensor und Aktor reichen."
+    if record.behavior.activation_ready:
+        return "Regeln sind lernbereit; vor Aktivierung Vorhersagen prüfen."
+    if record.assignment.review_required:
+        return "Kontext prüfen: Vorschläge übernehmen oder unpassende Sensoren entfernen."
+    return "Weiter beobachten, bis genug eindeutige Schaltbeispiele vorhanden sind."
+
+
+def _unique_lines(lines: list[str]) -> list[str]:
+    seen: set[str] = set()
+    result: list[str] = []
+    for line in lines:
+        normalized = line.strip()
+        if not normalized or normalized in seen:
+            continue
+        seen.add(normalized)
+        result.append(normalized)
+    return result
+
+
+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):

app/behavior/engine.py

@@ -1,6 +1,7 @@
 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
@@ -29,6 +30,7 @@ from app.actuators.models import (
     SafetyProfile,
     SafetyStage,
     SceneSuggestion,
+    SimulationOutcome,
     TimeProfile,
 )
 from app.actuators.store import ActuatorStore
@@ -46,10 +48,27 @@ _MAX_DECISION_TRACES = 30
 _MAX_LATENCY_MEASUREMENTS = 50
 _MAX_FEEDBACK_LOG = 50
 _ACTION_LOGBOOK_TOLERANCE = timedelta(seconds=10)
-_CONTEXT_TRIGGER_TOLERANCE = timedelta(seconds=3)
+_CONTEXT_TRIGGER_TOLERANCE = timedelta(minutes=4)
 _OWN_ACTION_TOLERANCE = timedelta(seconds=20)
-_SAFE_ACTIVE_DOMAINS = frozenset({"cover", "fan", "humidifier", "light", "switch"})
+_SAFE_ACTIVE_DOMAINS = frozenset({
+    "button",
+    "cover",
+    "fan",
+    "humidifier",
+    "input_button",
+    "light",
+    "switch",
+})
 _AUTOMATION_CONTEXT_DOMAINS = frozenset({"automation", "script"})
+_LIGHT_TARGET_ATTRIBUTES = frozenset({
+    "brightness",
+    "color_temp",
+    "color_temp_kelvin",
+    "effect",
+    "hs_color",
+    "rgb_color",
+    "xy_color",
+})
 logger = logging.getLogger(__name__)
 
 
@@ -333,10 +352,11 @@ 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,
-            causal_window_seconds=self._settings.prediction_interval_seconds * 2,
+            causal_window_seconds=max(self._settings.prediction_interval_seconds * 2, 240),
             timezone_name=self._settings.timezone,
         )
         if prediction is not None:
@@ -436,7 +456,11 @@ class BehaviorEngine:
                     self._ha_reader.call_service(
                         domain,
                         service,
-                        {"entity_id": actuator_entity_id},
+                        _service_data_for_prediction(
+                            actuator_entity_id,
+                            domain,
+                            prediction,
+                        ),
                     )
                     decision_to_service_ms = _elapsed_ms(service_started_perf)
                 except (HaClientError, ValueError) as exc:
@@ -514,6 +538,116 @@ class BehaviorEngine:
         )
         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,
@@ -994,7 +1128,7 @@ class BehaviorEngine:
             blockers.append(
                 f"Sicherheit {prediction.confidence:.0%} liegt unter der Schwelle {threshold:.0%}."
             )
-        if current_state == prediction.target_state:
+        if _target_reached(record.actuator_entity_id, current_state, prediction):
             blockers.append("Zielzustand ist bereits erreicht.")
         if not self._cooldown_elapsed(
             record.behavior,
@@ -1037,12 +1171,16 @@ class BehaviorEngine:
             patterns.append(
                 BehaviorPattern(
                     target_state=point.state,
+                    target_attributes=_target_attributes_for(point),
                     minute_of_day=local.hour * 60 + local.minute,
                     weekday=local.weekday(),
                     context_states=contexts,
-                    trigger_entity_id=trigger[0] if trigger else None,
-                    trigger_from_state=trigger[1] if trigger else None,
-                    trigger_to_state=trigger[2] if trigger else None,
+                    trigger_entity_id=trigger[1] if trigger else None,
+                    trigger_from_state=trigger[2] if trigger else None,
+                    trigger_to_state=trigger[3] if trigger else None,
+                    trigger_delay_seconds=(
+                        int(trigger[0].total_seconds()) if trigger else None
+                    ),
                     source=source,
                     weight=weight,
                     observed_at=point.timestamp,
@@ -1379,15 +1517,21 @@ 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(
@@ -1423,6 +1567,62 @@ 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,
@@ -1756,6 +1956,7 @@ 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:
@@ -1765,6 +1966,7 @@ def predict_behavior(
     minute_of_day = local.hour * 60 + local.minute
     changed_at = current_context_changed_at or {}
     by_state: dict[str, list[float]] = {}
+    attributes_by_state: dict[str, list[tuple[float, dict[str, object]]]] = {}
     causal_support_by_state: dict[str, int] = {}
     for pattern in patterns:
         if pattern.trigger_entity_id and pattern.trigger_to_state:
@@ -1778,7 +1980,11 @@ def predict_behavior(
                 current_context.get(pattern.trigger_entity_id)
                 == pattern.trigger_to_state
                 and trigger_age is not None
-                and 0 <= trigger_age <= causal_window_seconds
+                and _trigger_age_matches(
+                    trigger_age,
+                    pattern.trigger_delay_seconds,
+                    causal_window_seconds,
+                )
             ):
                 continue
             comparable = [
@@ -1786,17 +1992,16 @@ 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)
+            attributes_by_state.setdefault(pattern.target_state, []).append(
+                (score, pattern.target_attributes)
+            )
             causal_support_by_state[pattern.target_state] = (
                 causal_support_by_state.get(pattern.target_state, 0) + 1
             )
@@ -1817,16 +2022,18 @@ 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
         )
         by_state.setdefault(pattern.target_state, []).append(score)
+        attributes_by_state.setdefault(pattern.target_state, []).append(
+            (score, pattern.target_attributes)
+        )
     if not by_state:
         return None
     target_state, scores = max(
@@ -1840,6 +2047,9 @@ def predict_behavior(
         return None
     return BehaviorPrediction(
         target_state=target_state,
+        target_attributes=_aggregate_target_attributes(
+            attributes_by_state.get(target_state, [])
+        ),
         confidence=round(confidence, 4),
         generated_at=now,
         matching_patterns=support,
@@ -1854,9 +2064,106 @@ 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 _trigger_age_matches(
+    trigger_age_seconds: float,
+    expected_delay_seconds: int | None,
+    causal_window_seconds: int,
+) -> bool:
+    if trigger_age_seconds < 0:
+        return False
+    if expected_delay_seconds is None or expected_delay_seconds <= 10:
+        return trigger_age_seconds <= causal_window_seconds
+    tolerance = max(30, min(90, causal_window_seconds // 2))
+    return abs(trigger_age_seconds - expected_delay_seconds) <= tolerance
+
+
+def _aggregate_target_attributes(
+    weighted_attributes: list[tuple[float, dict[str, object]]],
+) -> dict[str, object]:
+    if not weighted_attributes:
+        return {}
+    result: dict[str, object] = {}
+    numeric_values: dict[str, list[tuple[float, float]]] = {}
+    categorical_values: dict[str, dict[str, float]] = {}
+    for score, attributes in weighted_attributes:
+        for key, value in attributes.items():
+            if key not in _LIGHT_TARGET_ATTRIBUTES:
+                continue
+            if isinstance(value, bool) or value is None:
+                continue
+            if isinstance(value, (int, float)):
+                numeric_values.setdefault(key, []).append((score, float(value)))
+            else:
+                categorical_values.setdefault(key, {}).setdefault(str(value), 0.0)
+                categorical_values[key][str(value)] += score
+    for key, values in numeric_values.items():
+        total_weight = sum(score for score, _ in values)
+        if total_weight <= 0:
+            continue
+        result[key] = round(sum(score * value for score, value in values) / total_weight)
+    for key, values in categorical_values.items():
+        if key in result:
+            continue
+        result[key] = max(values.items(), key=lambda item: (item[1], item[0]))[0]
+    return result
+
+
+def _target_attributes_for(point: StateHistoryPoint) -> dict[str, object]:
+    if point.state != "on":
+        return {}
+    return {
+        key: value
+        for key, value in point.attributes.items()
+        if key in _LIGHT_TARGET_ATTRIBUTES and value is not None
+    }
+
+
+def _service_data_for_prediction(
+    actuator_entity_id: str,
+    domain: str,
+    prediction: BehaviorPrediction,
+) -> dict[str, object]:
+    data: dict[str, object] = {"entity_id": actuator_entity_id}
+    if domain == "light" and prediction.target_state == "on":
+        data.update(prediction.target_attributes)
+    return data
+
+
+def _target_reached(
+    actuator_entity_id: str,
+    current_state: str,
+    prediction: BehaviorPrediction,
+) -> bool:
+    domain = actuator_entity_id.split(".", 1)[0]
+    if domain == "light" and prediction.target_state == "on" and prediction.target_attributes:
+        return False
+    return current_state == prediction.target_state
+
+
 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)
+    if domain in {"button", "input_button"}:
+        return "press"
     if domain == "scene":
         return "turn_on" if target_state == "on" else None
     if domain == "cover":
@@ -1922,7 +2229,7 @@ def _recent_context_transition(
     history: dict[str, StateHistorySeries],
     context_ids: list[str],
     timestamp: datetime,
-) -> tuple[str, str, str] | None:
+) -> tuple[timedelta, str, str, str] | None:
     nearest: tuple[timedelta, str, str, str] | None = None
     for entity_id in context_ids:
         series = history.get(entity_id)
@@ -1941,7 +2248,7 @@ def _recent_context_transition(
             previous_state = point.state
     if nearest is None:
         return None
-    return nearest[1], nearest[2], nearest[3]
+    return nearest
 
 
 def _circular_minute_distance(left: int, right: int) -> int:

app/ha/client.py

@@ -78,7 +78,6 @@ class HaClient:
                 "filter_entity_id": ",".join(entity_ids),
                 "end_time": end_time.isoformat(),
                 "minimal_response": "1",
-                "no_attributes": "1",
             },
         )
         if not isinstance(payload, list):

app/ha/history.py

@@ -21,6 +21,7 @@ class EntityHistorySeries(BaseModel):
 class StateHistoryPoint(BaseModel):
     timestamp: datetime
     state: str
+    attributes: dict[str, object] = {}
 
 
 class StateHistorySeries(BaseModel):
@@ -81,8 +82,22 @@ def normalize_state_history_payload(payload: object) -> list[StateHistorySeries]
             timestamp = _parse_timestamp(
                 raw_entry.get("last_changed") or raw_entry.get("last_updated")
             )
-            if not points or points[-1].state != raw_state:
-                points.append(StateHistoryPoint(timestamp=timestamp, state=raw_state))
+            attributes = raw_entry.get("attributes")
+            if not isinstance(attributes, dict):
+                attributes = {}
+            if (
+                not points
+                or points[-1].state != raw_state
+                or _relevant_state_attributes(points[-1].attributes)
+                != _relevant_state_attributes(attributes)
+            ):
+                points.append(
+                    StateHistoryPoint(
+                        timestamp=timestamp,
+                        state=raw_state,
+                        attributes=_relevant_state_attributes(attributes),
+                    )
+                )
         if entity_id is not None and points:
             points.sort(key=lambda point: point.timestamp)
             normalized.append(StateHistorySeries(entity_id=entity_id, points=points))
@@ -176,3 +191,16 @@ def _optional_string(value: object) -> str | None:
     if value is None or value == "":
         return None
     return str(value)
+
+
+def _relevant_state_attributes(attributes: dict[str, object]) -> dict[str, object]:
+    keys = {
+        "brightness",
+        "color_temp",
+        "color_temp_kelvin",
+        "effect",
+        "hs_color",
+        "rgb_color",
+        "xy_color",
+    }
+    return {key: attributes[key] for key in keys if key in attributes}

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="1.7.0",
+    version="1.7.4",
     lifespan=lifespan,
 )
 app.state.settings = load_settings()
@@ -255,6 +255,9 @@ async def _ha_event_listener(app: FastAPI, client: HaClient) -> None:
     ws_url = ha_url.replace("http://", "ws://").replace("https://", "wss://") + "/api/websocket"
     auth_token = cast(str, settings.ha_token)
     ws_status = getattr(app.state, "ws_status", None)
+    reconnect_delay = 1.0
+    relevant_entity_ids: set[str] = set()
+    relevant_loaded_at = 0.0
     while True:
         if ws_status is not None:
             ws_status.status = "connecting"
@@ -283,6 +286,9 @@ async def _ha_event_listener(app: FastAPI, client: HaClient) -> None:
 
                 logger.info("WebSocket-Verbindung zu Home Assistant hergestellt")
                 state_cache = await asyncio.to_thread(_load_ha_state_cache, ha_reader)
+                relevant_entity_ids = await asyncio.to_thread(_relevant_entity_ids, store)
+                relevant_loaded_at = asyncio.get_running_loop().time()
+                reconnect_delay = 1.0
                 if ws_status is not None:
                     ws_status.status = "connected"
                     ws_status.error = None
@@ -309,6 +315,12 @@ async def _ha_event_listener(app: FastAPI, client: HaClient) -> None:
                         entity_id = event_data.get("entity_id")
                         if not entity_id:
                             continue
+                        loop_time = asyncio.get_running_loop().time()
+                        if loop_time - relevant_loaded_at >= 10:
+                            relevant_entity_ids = await asyncio.to_thread(_relevant_entity_ids, store)
+                            relevant_loaded_at = loop_time
+                        if entity_id not in relevant_entity_ids:
+                            continue
                         new_state = event_data.get("new_state")
                         _update_ha_state_cache(state_cache, entity_id, new_state)
                         # Prüfe, ob Entity ein Aktor oder relevanter Kontext ist
@@ -328,17 +340,24 @@ async def _ha_event_listener(app: FastAPI, client: HaClient) -> None:
             websockets.exceptions.InvalidStatus,
             OSError,
         ) as exc:
-            logger.warning("WebSocket-Verbindung unterbrochen: %s. Wiederholung in 1s...", exc)
+            delay = reconnect_delay
+            logger.warning(
+                "WebSocket-Verbindung unterbrochen: %s. Wiederholung in %.0fs...",
+                exc,
+                delay,
+            )
             if ws_status is not None:
                 ws_status.status = "reconnecting"
                 ws_status.error = str(exc)
-            await asyncio.sleep(1)
+            await asyncio.sleep(delay)
+            reconnect_delay = min(reconnect_delay * 2, 60.0)
         except Exception as exc:
             logger.exception("Unerwarteter Fehler im Event-Listener: %s", exc)
             if ws_status is not None:
                 ws_status.status = "error"
                 ws_status.error = str(exc)
-            await asyncio.sleep(1)
+            await asyncio.sleep(reconnect_delay)
+            reconnect_delay = min(reconnect_delay * 2, 60.0)
 
 
 # Fallback: periodische Vorhersage falls Event-Stream ausfällt
@@ -353,7 +372,7 @@ async def _fallback_prediction(app: FastAPI) -> None:
         await asyncio.sleep(
             app.state.settings.prediction_interval_seconds
             if websocket_connected
-            else min(5, app.state.settings.prediction_interval_seconds)
+            else max(30, app.state.settings.prediction_interval_seconds)
         )
         # Nur ausführen, wenn WebSocket nicht verbunden ist
         ws_status = getattr(app.state, "ws_status", None)
@@ -389,15 +408,14 @@ def _update_ha_state_cache(
     )
 
 
-def _is_relevant_state_change(store: ActuatorStore, entity_id: str) -> bool:
+def _relevant_entity_ids(store: ActuatorStore) -> set[str]:
+    result: set[str] = set()
     for record in store.list():
-        if record.actuator_entity_id == entity_id:
-            return True
-        if record.assignment.selected_numeric_entity_id == entity_id:
-            return True
-        if entity_id in record.assignment.selected_context_entity_ids:
-            return True
-    return False
+        result.add(record.actuator_entity_id)
+        if record.assignment.selected_numeric_entity_id:
+            result.add(record.assignment.selected_numeric_entity_id)
+        result.update(record.assignment.selected_context_entity_ids)
+    return result
 
 
 def _ha_entity_from_event(

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "sillyhome-next"
-version = "1.7.0"
+version = "1.7.6"
 description = "Lokales Smart-Home-Intelligenzsystem für Home Assistant"
 requires-python = ">=3.11"
 dependencies = [

tests/actuators/test_lifecycle.py

@@ -87,7 +87,7 @@ def _service(
 
 
 def test_reconciliation_auto_assigns_and_trains_numeric_model(tmp_path: Path) -> None:
-    start = datetime(2026, 6, 1, tzinfo=timezone.utc)
+    start = datetime.now(timezone.utc) - timedelta(days=1)
     entities = [
         HaEntitySummary(
             entity_id="light.abstellkammer",
@@ -141,6 +141,46 @@ def test_reconciliation_auto_assigns_and_trains_numeric_model(tmp_path: Path) ->
     assert "binary_sensor.abstellkammer_motion" not in artifact.supported_sensors
 
 
+def test_light_with_opening_context_does_not_require_brightness_sensor(tmp_path: Path) -> None:
+    start = datetime.now(timezone.utc) - timedelta(days=1)
+    entities = [
+        HaEntitySummary(
+            entity_id="light.abstellkammer",
+            domain="light",
+            friendly_name="Abstellkammer Licht",
+            area_name="Abstellkammer",
+        ),
+        HaEntitySummary(
+            entity_id="sensor.abstellkammer_illuminance",
+            domain="sensor",
+            device_class="illuminance",
+            state_class="measurement",
+            unit_of_measurement="lx",
+            friendly_name="Abstellkammer Helligkeit",
+            area_name="Abstellkammer",
+        ),
+        HaEntitySummary(
+            entity_id="binary_sensor.abstellkammer_tuer",
+            domain="binary_sensor",
+            device_class="door",
+            friendly_name="Tür Abstellkammer",
+            area_name="Abstellkammer",
+        ),
+    ]
+    service = _service(
+        tmp_path,
+        entities,
+        {"sensor.abstellkammer_illuminance": _points(8, start, 10.0)},
+    )
+
+    record = service.configure_actuator("light.abstellkammer")
+
+    assert record.assignment.selected_numeric_entity_id is None
+    assert record.assignment.selected_context_entity_ids == ["binary_sensor.abstellkammer_tuer"]
+    assert record.assignment.review_required is False
+    assert "kein Helligkeitssensor erforderlich" in record.assignment.reason
+
+
 def test_reconciliation_rejects_ambiguous_numeric_mapping(tmp_path: Path) -> None:
     start = datetime(2026, 6, 1, tzinfo=timezone.utc)
     entities = [
@@ -352,6 +392,100 @@ def test_fan_prefers_humidity_over_power_sensor(tmp_path: Path) -> None:
     assert record.assignment.selected_numeric_entity_id == "sensor.bad_luftfeuchtigkeit"
 
 
+def test_lidl_light_uses_room_presence_not_brand_overlap(tmp_path: Path) -> None:
+    entities = [
+        HaEntitySummary(
+            entity_id="light.lidl_kuche",
+            domain="light",
+            friendly_name="Lidl Küche",
+        ),
+        HaEntitySummary(
+            entity_id="light.lidl_wohnzimmer",
+            domain="light",
+            friendly_name="Lidl Wohnzimmer",
+        ),
+        HaEntitySummary(
+            entity_id="binary_sensor.pir_kuche_motion_detection",
+            domain="binary_sensor",
+            device_class="motion",
+            friendly_name="Bewegungsmelder",
+            device_name="PIR_Küche",
+        ),
+        HaEntitySummary(
+            entity_id="binary_sensor.pir_wohnzimmer_sensor_state_any",
+            domain="binary_sensor",
+            device_class="motion",
+            friendly_name="Bewegungsmelder",
+            device_name="PIR_Wohnzimmer",
+        ),
+    ]
+    service = _service(tmp_path, entities, {})
+
+    record = service.configure_actuator("light.lidl_kuche")
+
+    assert record.assignment.selected_context_entity_ids == [
+        "binary_sensor.pir_kuche_motion_detection"
+    ]
+
+
+def test_mailbox_reset_button_uses_cabinet_door_context(tmp_path: Path) -> None:
+    entities = [
+        HaEntitySummary(
+            entity_id="button.smart_mailbox_als_geleert_markieren",
+            domain="button",
+            friendly_name="Smart Mailbox Als geleert markieren",
+        ),
+        HaEntitySummary(
+            entity_id="binary_sensor.schrank_strasse_open",
+            domain="binary_sensor",
+            device_class="door",
+            friendly_name="Schrank Straße",
+        ),
+    ]
+    service = _service(tmp_path, entities, {})
+
+    record = service.configure_actuator("button.smart_mailbox_als_geleert_markieren")
+
+    assert record.assignment.selected_context_entity_ids == [
+        "binary_sensor.schrank_strasse_open"
+    ]
+    assert record.assignment.review_required is False
+
+
+def test_fan_auto_selects_humidity_and_occupancy_context(tmp_path: Path) -> None:
+    start = datetime(2026, 6, 1, tzinfo=timezone.utc)
+    entities = [
+        HaEntitySummary(
+            entity_id="humidifier.gastewc_luftung",
+            domain="humidifier",
+            friendly_name="GästeWC Lüftung",
+        ),
+        HaEntitySummary(
+            entity_id="sensor.pir_gastewc_humidity",
+            domain="sensor",
+            device_class="humidity",
+            state_class="measurement",
+            unit_of_measurement="%",
+            friendly_name="Gäste WC Luftfeuchtigkeit",
+        ),
+        HaEntitySummary(
+            entity_id="input_boolean.gaste_wc_occupied",
+            domain="input_boolean",
+            friendly_name="gaste_wc_occupied",
+        ),
+    ]
+    service = _service(
+        tmp_path,
+        entities,
+        {"sensor.pir_gastewc_humidity": _points(8, start, 55.0)},
+    )
+
+    record = service.configure_actuator("humidifier.gastewc_luftung")
+
+    assert record.assignment.selected_numeric_entity_id == "sensor.pir_gastewc_humidity"
+    assert "input_boolean.gaste_wc_occupied" in record.assignment.selected_context_entity_ids
+
+
 def test_manual_assignment_persists_and_wins_over_automatic_mapping(tmp_path: Path) -> None:
     start = datetime(2026, 6, 1, tzinfo=timezone.utc)
     entities = [

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)
@@ -537,6 +623,25 @@ def test_dashboard_system_and_start_do_not_materialize_entity_cache(
     assert start_response.json()["actuators"][0]["friendly_name"] == "Abstellkammer Licht"
 
 
+def test_room_management_overview_groups_actuators_with_sensors_and_rules(tmp_path: Path) -> None:
+    with TestClient(app) as client:
+        _install_service(tmp_path)
+        client.post("/v1/actuators", json={"actuator_entity_id": "light.abstellkammer"})
+
+        response = client.get("/v1/actuators/settings/rooms")
+
+    assert response.status_code == 200
+    payload = response.json()
+    room = payload["rooms"][0]
+    assert room["room"] == "Abstellkammer"
+    assert room["actuator_count"] == 1
+    actuator = room["actuators"][0]
+    assert actuator["actuator_entity_id"] == "light.abstellkammer"
+    assert actuator["sensors"]
+    assert actuator["prediction_rules"]
+    assert any(sensor["entity_id"] == "binary_sensor.abstellkammer_motion" for sensor in room["sensors"])
+
+
 def test_actuator_detail_uses_compact_payload(tmp_path: Path) -> None:
     with TestClient(app) as client:
         _install_service(tmp_path)

tests/behavior/test_engine.py

@@ -646,6 +646,81 @@ def test_prediction_ignores_stale_causal_context_state() -> None:
     ) is None
 
 
+def test_prediction_respects_learned_context_delay() -> None:
+    now = datetime.now(timezone.utc).replace(second=0, microsecond=0)
+    patterns = [
+        BehaviorPattern(
+            target_state="on",
+            minute_of_day=60,
+            weekday=0,
+            context_states={"input_boolean.gaste_wc_occupied": "on"},
+            trigger_entity_id="input_boolean.gaste_wc_occupied",
+            trigger_from_state="off",
+            trigger_to_state="on",
+            trigger_delay_seconds=180,
+            source="automation",
+            weight=1.0,
+            observed_at=now - timedelta(days=days_ago),
+        )
+        for days_ago in (3, 2, 1)
+    ]
+
+    early = predict_behavior(
+        patterns,
+        current_context={"input_boolean.gaste_wc_occupied": "on"},
+        current_context_changed_at={
+            "input_boolean.gaste_wc_occupied": now - timedelta(seconds=30)
+        },
+        now=now,
+        min_support=3,
+        window_minutes=30,
+        causal_window_seconds=240,
+    )
+    due = predict_behavior(
+        patterns,
+        current_context={"input_boolean.gaste_wc_occupied": "on"},
+        current_context_changed_at={
+            "input_boolean.gaste_wc_occupied": now - timedelta(seconds=185)
+        },
+        now=now,
+        min_support=3,
+        window_minutes=30,
+        causal_window_seconds=240,
+    )
+
+    assert early is None
+    assert due is not None
+    assert due.target_state == "on"
+
+
+def test_light_prediction_carries_brightness_attributes() -> None:
+    now = datetime.now(timezone.utc).replace(second=0, microsecond=0)
+    patterns = [
+        BehaviorPattern(
+            target_state="on",
+            target_attributes={"brightness": brightness},
+            minute_of_day=now.astimezone().hour * 60 + now.astimezone().minute,
+            weekday=now.astimezone().weekday(),
+            context_states={"binary_sensor.pir_kuche_motion_detection": "on"},
+            source="automation",
+            weight=1.0,
+            observed_at=now - timedelta(days=days_ago),
+        )
+        for days_ago, brightness in zip((3, 2, 1), (80, 90, 100), strict=True)
+    ]
+
+    prediction = predict_behavior(
+        patterns,
+        current_context={"binary_sensor.pir_kuche_motion_detection": "on"},
+        now=now,
+        min_support=3,
+        window_minutes=30,
+    )
+
+    assert prediction is not None
+    assert prediction.target_attributes["brightness"] == 90
+
+
 def test_state_change_uses_websocket_context_state_for_immediate_action(
     tmp_path: Path,
 ) -> None:

tests/ha/test_history.py

@@ -120,6 +120,28 @@ def test_normalize_state_history_keeps_categorical_changes() -> None:
     assert [point.state for point in result[0].points] == ["off", "on"]
 
 
+def test_normalize_state_history_keeps_light_attribute_changes() -> None:
+    result = normalize_state_history_payload(
+        [
+            [
+                {
+                    "entity_id": "light.office",
+                    "state": "on",
+                    "attributes": {"brightness": 80, "friendly_name": "Office"},
+                    "last_changed": "2026-06-01T08:00:00+00:00",
+                },
+                {
+                    "state": "on",
+                    "attributes": {"brightness": 120, "friendly_name": "Office"},
+                    "last_changed": "2026-06-01T08:05:00+00:00",
+                },
+            ]
+        ]
+    )
+
+    assert [point.attributes["brightness"] for point in result[0].points] == [80, 120]
+
+
 def test_normalize_logbook_preserves_action_origin() -> None:
     result = normalize_logbook_payload(
         [

tests/test_dashboard.py

@@ -22,6 +22,10 @@ def test_dashboard_is_served_at_root() -> None:
     assert "Ohne deine spätere Freigabe wird nichts geschaltet" not in response.text
     assert "Du wählst keine Sensoren und erstellst keine Regeln" not in response.text
     assert "Freigabestatus" in response.text
+    assert "Sprache, Räume, Sensoren, Aktoren und Vorhersagen an einem Ort." in response.text
+    assert "room-management" in response.text
+    assert 'api("v1/actuators/settings/rooms")' in response.text
+    assert "Auswahl speichern" in response.text
     assert "SillyHome übernehmen lassen" in response.text
     assert "Passende Home-Assistant-Automationen" in response.text
     assert "Pausieren" in response.text

tests/test_main.py

@@ -126,6 +126,43 @@ def test_ha_event_listener_processes_state_change(tmp_path: Path) -> None:
     assert mock_app.state.ws_status.error is None
 
 
+def test_ha_event_listener_skips_unrelated_state_change(tmp_path: Path) -> None:
+    async def run_test() -> None:
+        fake_ws = _FakeWebSocket(
+            [
+                '{"type":"auth_required"}',
+                '{"type":"auth_ok"}',
+                (
+                    '{"type":"event","event":{"event_type":"state_changed",'
+                    '"data":{"entity_id":"sensor.unused","new_state":{"state":"on"}}}}'
+                ),
+                asyncio.CancelledError(),
+            ]
+        )
+
+        with patch("websockets.connect", return_value=fake_ws):
+            try:
+                await _ha_event_listener(mock_app, mock_client)
+            except asyncio.CancelledError:
+                pass
+
+    mock_app = MagicMock()
+    mock_app.state.settings = MagicMock()
+    mock_app.state.settings.ha_url = "http://homeassistant:8123"
+    mock_app.state.settings.ha_token = "test-token"
+    mock_app.state.ws_status = MagicMock()
+    mock_engine = _RecordingBehaviorEngine(tmp_path)
+    mock_app.state.behavior_engine = mock_engine
+    mock_app.state.ha_reader = _FakeHaReader()
+    mock_store = ActuatorStore(tmp_path / "store")
+    mock_store.configure("light.test")
+    mock_app.state.actuator_store = mock_store
+    mock_client = MagicMock()
+
+    anyio.run(run_test)
+    assert mock_engine.state_changes == []
+
+
 def test_lifespan_skips_event_listener_without_ha_config() -> None:
     app = FastAPI()
     app.state.settings = MagicMock()