Limit rollback snapshots and relax HA timeouts

CHANGELOG.md

@@ -1,5 +1,41 @@
 # Changelog
 
+## 1.2.0 - 2026-06-17
+- Automatische Sensor-Gewichtungsanpassung aus Nutzerfeedback:
+  korrektes Feedback staerkt aktuelle Kontextsignale leicht, falsches Feedback
+  wertet sie vorsichtig ab.
+- Modell-Snapshots mit aktivem Modellstand und Rollback-API ergaenzt.
+- Dashboard zeigt Modell-Snapshots, Rollback, Zeitprofile,
+  adaptive Gewichtungsupdates und Automation-Konflikte.
+- Automation-Refresh markiert Konflikte, wenn SillyHome aktiv ist und passende
+  HA-Automationen parallel aktiv bleiben.
+- Zeitprofile fuer Nacht, Morgen, Tag, Abend und Wochenende werden aus
+  gelernten Handlungen gebildet.
+
+## 1.1.0 - 2026-06-17
+- Dashboard als Einrichtungs- und Visualisierungszentrale erweitert:
+  Job-Queue, Sicherheitsprofil, Entscheidungsakte, Wissen/Annahmen/
+  Unsicherheiten und Beitragsfaktoren pro Aktor.
+- Lokales Safety-Profil pro Aktor eingefuehrt: manuelle Sperre,
+  Freigabestufe, Mindest-Confidence und optionaler Cooldown werden vor
+  autonomem Schalten ausgewertet.
+- Sofort-Schaltpfad bleibt schnell: Safety prueft nur lokale Daten; der
+  Home-Assistant-Serviceaufruf wird nicht durch Discovery, Training oder
+  Statistik blockiert.
+- Sichtbare Job-Queue fuer Discovery, Reconciliation, Training, Evaluation
+  und Automation-Refresh mit Status, Dauer, Fehler und Zusammenfassung.
+- Entscheidungsstatistik erweitert: Sensor-/Kontextfaktoren, aktive
+  Gewichtungen, Sample-/Confidence-Trends und Feedbackzaehler werden
+  persistiert.
+
+## 1.0.5 - 2026-06-17
+- Lange Friendly Names, Entity-IDs, Chips, Tabellenwerte und Metriken brechen
+  im Dashboard responsiv um und laufen nicht mehr aus Karten oder Boxen.
+- Automatisierter Performance-Budget-Test fuer Root-HTML und
+  `/v1/actuators/dashboard` gegen das 5-Sekunden-Limit ergaenzt.
+- HA-/Ingress-Verifikation mit Supervisor-Status, Backup, Watchdog,
+  Hard-Reload und Rollback im Operating Guide dokumentiert.
+
 ## 1.0.4 - 2026-06-17
 - Sensor-Relevanz ist in der Aktor-Detailansicht sichtbar: automatische
   Relevanz, aktive Gewichtung und Score werden pro verwendetem Sensor/Zustand

README.md

@@ -15,6 +15,20 @@ nach einer ausdrücklichen Freigabe ausführen.
   [`docs/V1_0_0_OPERATING_GUIDE.md`](docs/V1_0_0_OPERATING_GUIDE.md)
 - Version 1.0.x Abnahme und offene Punkte:
   [`docs/V1_0_ACCEPTANCE.md`](docs/V1_0_ACCEPTANCE.md)
+- Version 1.1.0 Safety, Transparenz und Job-Queue:
+  [`docs/V1_1_0_OPERATING_GUIDE.md`](docs/V1_1_0_OPERATING_GUIDE.md)
+- Version 1.2.0 adaptive Gewichtung, Rollback und Profile:
+  [`docs/V1_2_0_OPERATING_GUIDE.md`](docs/V1_2_0_OPERATING_GUIDE.md)
+- Version 1.3.0 Anomalie- und Performance-Überwachung:
+  [`docs/V1_3_0_OPERATING_GUIDE.md`](docs/V1_3_0_OPERATING_GUIDE.md)
+- Version 1.4.0 deutsches Dashboard und gestufter Datenabruf:
+  [`docs/V1_4_0_OPERATING_GUIDE.md`](docs/V1_4_0_OPERATING_GUIDE.md)
+- Version 1.5.0 Menü-Dashboard und kompakte Detaildaten:
+  [`docs/V1_5_0_OPERATING_GUIDE.md`](docs/V1_5_0_OPERATING_GUIDE.md)
+- Version 1.5.1 Stabilisierung der Dashboard-Ladepfade:
+  [`docs/V1_5_1_OPERATING_GUIDE.md`](docs/V1_5_1_OPERATING_GUIDE.md)
+- Version 1.5.2 Rollback-Speicher und HA-Timeouts:
+  [`docs/V1_5_2_OPERATING_GUIDE.md`](docs/V1_5_2_OPERATING_GUIDE.md)
 - Arbeitsregeln für Coding-Agenten: [`AGENTS.md`](AGENTS.md)
 
 ## Reifegrad

addon/config.yaml

@@ -1,5 +1,5 @@
 name: SillyHome Next
-version: "1.0.4"
+version: "1.5.2"
 slug: sillyhome_next
 description: Lernt automatisch aus deinem Verhalten und steuert freigegebene Aktoren
 url: http://192.168.6.31:3000/pino/sillyhome-next

app/actuators/models.py

@@ -37,6 +37,21 @@ class BehaviorStatus(StrEnum):
     BLOCKED = "blocked"
 
 
+class SafetyStage(StrEnum):
+    OBSERVE = "observe"
+    SUGGEST = "suggest"
+    SHADOW = "shadow"
+    PARTIAL = "partial"
+    ACTIVE = "active"
+
+
+class JobStatus(StrEnum):
+    PENDING = "pending"
+    RUNNING = "running"
+    COMPLETED = "completed"
+    FAILED = "failed"
+
+
 class AssignmentCandidate(BaseModel):
     entity_id: str
     domain: str
@@ -121,11 +136,111 @@ class BehaviorPrediction(BaseModel):
     execution_reason: str = "Vorhersage wurde noch nicht ausgeführt."
 
 
+class DecisionFactor(BaseModel):
+    entity_id: str | None = None
+    label: str
+    factor_type: str = Field(max_length=40)
+    state: str | None = None
+    weight: float = Field(default=1.0, ge=0.0, le=1.0)
+    contribution: float = Field(default=0.0, ge=0.0, le=1.0)
+    evidence: list[str] = Field(default_factory=list)
+
+
+class AdaptiveWeightUpdate(BaseModel):
+    entity_id: str
+    previous_weight: float = Field(ge=0.0, le=1.0)
+    new_weight: float = Field(ge=0.0, le=1.0)
+    reason: str = Field(max_length=300)
+    updated_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+
+
+class SafetyRule(BaseModel):
+    rule_id: str = Field(pattern=r"^[a-z0-9_-]{1,64}$")
+    label: str = Field(min_length=1, max_length=160)
+    enabled: bool = True
+    blocking: bool = True
+    reason: str = Field(default="", max_length=300)
+
+
+def default_safety_rules() -> list[SafetyRule]:
+    return [
+        SafetyRule(
+            rule_id="activation_ready",
+            label="Nur nach Lernfreigabe aktiv schalten",
+            reason="Der Aktor muss genug eindeutiges Verhalten gelernt haben.",
+        ),
+        SafetyRule(
+            rule_id="confidence_threshold",
+            label="Mindest-Sicherheit einhalten",
+            reason="Vorhersagen unter der Schaltschwelle bleiben im Shadow-Modus.",
+        ),
+        SafetyRule(
+            rule_id="cooldown",
+            label="Sicherheits-Cooldown gegen Hin-und-her-Schalten",
+            reason="Gleiche Zielzustände werden nicht zu schnell wiederholt.",
+        ),
+        SafetyRule(
+            rule_id="manual_block",
+            label="Manuelle Sperre respektieren",
+            reason="Nutzer können jeden Aktor sofort blockieren.",
+        ),
+    ]
+
+
+class SafetyProfile(BaseModel):
+    stage: SafetyStage = SafetyStage.SHADOW
+    manual_block: bool = False
+    min_confidence: float = Field(default=0.82, ge=0.0, le=1.0)
+    min_confidence_on: float | None = Field(default=None, ge=0.0, le=1.0)
+    min_confidence_off: float | None = Field(default=None, ge=0.0, le=1.0)
+    cooldown_seconds: int | None = Field(default=None, ge=0)
+    rules: list[SafetyRule] = Field(default_factory=default_safety_rules)
+    updated_at: datetime | None = None
+    note: str | None = Field(default=None, max_length=500)
+
+
 class ExecutionEvent(BaseModel):
     target_state: str
     executed_at: datetime
 
 
+class ModelSnapshot(BaseModel):
+    version_id: str
+    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+    sample_count: int = Field(default=0, ge=0)
+    high_confidence_sample_count: int = Field(default=0, ge=0)
+    average_confidence: float = Field(default=0.0, ge=0.0, le=1.0)
+    incorrect_feedback_count: int = Field(default=0, ge=0)
+    patterns: list[BehaviorPattern] = Field(default_factory=list)
+    reason: str = Field(default="", max_length=500)
+
+
+class AutomationConflict(BaseModel):
+    automation_entity_id: str = Field(pattern=r"^automation\.[a-z0-9_]+$")
+    severity: str = Field(default="info", max_length=20)
+    status: str = Field(default="open", max_length=40)
+    reason: str = Field(max_length=500)
+    updated_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+
+
+class AnomalyEvent(BaseModel):
+    anomaly_id: str = Field(pattern=r"^[a-z0-9_.-]{1,120}$")
+    severity: str = Field(default="info", max_length=20)
+    category: str = Field(max_length=40)
+    title: str = Field(min_length=1, max_length=160)
+    detail: str = Field(min_length=1, max_length=500)
+    detected_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+    resolved: bool = False
+
+
+class TimeProfile(BaseModel):
+    profile_id: str = Field(pattern=r"^[a-z0-9_-]{1,64}$")
+    label: str = Field(min_length=1, max_length=80)
+    sample_count: int = Field(default=0, ge=0)
+    dominant_state: str | None = None
+    confidence: float = Field(default=0.0, ge=0.0, le=1.0)
+
+
 class RelatedAutomation(BaseModel):
     entity_id: str = Field(pattern=r"^automation\.[a-z0-9_]+$")
     config_id: str = Field(min_length=1, max_length=120)
@@ -150,6 +265,22 @@ class BehaviorState(BaseModel):
     related_automations: list[RelatedAutomation] = Field(default_factory=list)
     paused_automation_entity_ids: list[str] = Field(default_factory=list)
     reason: str = "Historische Aktorhandlungen werden analysiert."
+    safety: SafetyProfile = Field(default_factory=SafetyProfile)
+    decision_factors: list[DecisionFactor] = Field(default_factory=list)
+    knowledge: list[str] = Field(default_factory=list)
+    assumptions: list[str] = Field(default_factory=list)
+    uncertainties: list[str] = Field(default_factory=list)
+    safety_blockers: list[str] = Field(default_factory=list)
+    sample_trend: list[int] = Field(default_factory=list)
+    confidence_trend: list[float] = Field(default_factory=list)
+    correct_feedback_count: int = Field(default=0, ge=0)
+    incorrect_feedback_count: int = Field(default=0, ge=0)
+    model_snapshots: list[ModelSnapshot] = Field(default_factory=list)
+    active_model_version: str | None = None
+    adaptive_weight_updates: list[AdaptiveWeightUpdate] = Field(default_factory=list)
+    automation_conflicts: list[AutomationConflict] = Field(default_factory=list)
+    time_profiles: list[TimeProfile] = Field(default_factory=list)
+    anomalies: list[AnomalyEvent] = Field(default_factory=list)
 
 
 class ActuatorRecord(BaseModel):
@@ -176,5 +307,22 @@ class ReconciliationState(BaseModel):
     last_summary: str = "Noch keine Reconciliation ausgeführt."
 
 
+class JobQueueItem(BaseModel):
+    job_id: str = Field(min_length=1, max_length=120)
+    kind: str = Field(min_length=1, max_length=40)
+    target: str | None = Field(default=None, max_length=160)
+    trigger: str = Field(default="manual", max_length=40)
+    status: JobStatus = JobStatus.PENDING
+    started_at: datetime | None = None
+    completed_at: datetime | None = None
+    duration_ms: int | None = Field(default=None, ge=0)
+    error: str | None = Field(default=None, max_length=500)
+    summary: str = Field(default="", max_length=500)
+
+
+class JobQueueState(BaseModel):
+    jobs: list[JobQueueItem] = Field(default_factory=list)
+
+
 def model_id_for_actuator(actuator_entity_id: str) -> str:
     return f"actuator.{actuator_entity_id}"

app/actuators/store.py

@@ -8,6 +8,9 @@ from threading import RLock
 
 from app.actuators.models import (
     ActuatorRecord,
+    JobQueueItem,
+    JobQueueState,
+    JobStatus,
     LifecycleStatus,
     ModelLifecycleState,
     ReconciliationState,
@@ -22,6 +25,7 @@ class ActuatorStore:
         self._actuators_root.mkdir(parents=True, exist_ok=True)
         self._lock = RLock()
         self._reconciliation_state_path = self._root / "reconciliation_state.json"
+        self._job_queue_path = self._root / "job_queue.json"
 
     def list(self) -> list[ActuatorRecord]:
         with self._lock:
@@ -85,6 +89,75 @@ class ActuatorStore:
             self._persist_reconciliation_state(state)
             return state
 
+    def load_job_queue(self) -> JobQueueState:
+        with self._lock:
+            if not self._job_queue_path.exists():
+                return JobQueueState()
+            try:
+                return JobQueueState.model_validate_json(
+                    self._job_queue_path.read_text(encoding="utf-8")
+                )
+            except ValueError as exc:
+                raise ValueError("Ungültiger Job-Queue-Status.") from exc
+
+    def start_job(
+        self,
+        *,
+        kind: str,
+        trigger: str,
+        target: str | None = None,
+        summary: str = "",
+    ) -> JobQueueItem:
+        now = datetime.now(timezone.utc)
+        job = JobQueueItem(
+            job_id=f"{now.strftime('%Y%m%d%H%M%S%f')}-{kind}-{target or 'all'}",
+            kind=kind,
+            target=target,
+            trigger=trigger,
+            status=JobStatus.RUNNING,
+            started_at=now,
+            summary=summary,
+        )
+        with self._lock:
+            queue = self.load_job_queue()
+            queue.jobs = [*queue.jobs, job][-50:]
+            self._persist_job_queue(queue)
+        return job
+
+    def finish_job(
+        self,
+        job_id: str,
+        *,
+        status: JobStatus,
+        summary: str = "",
+        error: str | None = None,
+    ) -> JobQueueItem | None:
+        now = datetime.now(timezone.utc)
+        with self._lock:
+            queue = self.load_job_queue()
+            updated_job: JobQueueItem | None = None
+            jobs: list[JobQueueItem] = []
+            for job in queue.jobs:
+                if job.job_id != job_id:
+                    jobs.append(job)
+                    continue
+                duration_ms = None
+                if job.started_at is not None:
+                    duration_ms = max(0, int((now - job.started_at).total_seconds() * 1000))
+                updated_job = job.model_copy(
+                    update={
+                        "status": status,
+                        "completed_at": now,
+                        "duration_ms": duration_ms,
+                        "summary": summary or job.summary,
+                        "error": error,
+                    }
+                )
+                jobs.append(updated_job)
+            queue.jobs = jobs[-50:]
+            self._persist_job_queue(queue)
+            return updated_job
+
     def _target(self, actuator_entity_id: str) -> Path:
         if "." not in actuator_entity_id:
             raise ValueError("Ungültige actuator_entity_id.")
@@ -108,6 +181,14 @@ class ActuatorStore:
         )
         os.replace(temporary, self._reconciliation_state_path)
 
+    def _persist_job_queue(self, state: JobQueueState) -> None:
+        temporary = self._job_queue_path.with_suffix(".json.tmp")
+        temporary.write_text(
+            json.dumps(state.model_dump(mode="json"), ensure_ascii=True, sort_keys=True) + "\n",
+            encoding="utf-8",
+        )
+        os.replace(temporary, self._job_queue_path)
+
     @staticmethod
     def _load(path: Path) -> ActuatorRecord:
         try:

app/api/v1/actuators.py

@@ -9,7 +9,8 @@ from fastapi import APIRouter, Depends, HTTPException, Query, Request, status
 from pydantic import BaseModel, Field
 
 from app.actuators.lifecycle import ActuatorReconciliationService
-from app.actuators.models import ActuatorRecord, ReconciliationState, SensorWeightGroup
+from app.actuators.models import ActuatorRecord, AnomalyEvent, ReconciliationState, SensorWeightGroup
+from app.actuators.models import JobQueueItem, JobQueueState, JobStatus, SafetyProfile
 from app.actuators.store import ActuatorStore
 from app.behavior.engine import BehaviorEngine
 from app.config import Settings
@@ -55,6 +56,14 @@ class FeedbackRequest(BaseModel):
     expected_state: str | None = Field(default=None, max_length=100)
 
 
+class SafetyProfileRequest(BaseModel):
+    safety: SafetyProfile
+
+
+class ModelRollbackRequest(BaseModel):
+    version_id: str = Field(min_length=1, max_length=120)
+
+
 class ActuatorSuggestion(BaseModel):
     entity_id: str
     domain: str
@@ -80,6 +89,8 @@ class ActuatorSummary(BaseModel):
     activation_ready: bool
     activation_reason: str
     sample_count: int
+    anomaly_count: int = 0
+    critical_anomaly_count: int = 0
     prediction_target_state: str | None = None
     prediction_confidence: float | None = None
     updated_at: str
@@ -99,6 +110,12 @@ class DashboardSystemStatus(BaseModel):
     configured_actuators: int = 0
     trained_models: int = 0
     review_required: int = 0
+    performance_budget_ms: int = 3000
+    job_p95_duration_ms: int | None = None
+    slow_job_count: int = 0
+    performance_status: str = "unknown"
+    anomaly_count: int = 0
+    critical_anomaly_count: int = 0
 
 
 class DashboardDiscoveryGroup(BaseModel):
@@ -112,6 +129,13 @@ class DashboardOverview(BaseModel):
     cache: EntityCacheStatus
     actuators: list[ActuatorSummary]
     discovery_groups: list[DashboardDiscoveryGroup]
+    jobs: JobQueueState = Field(default_factory=JobQueueState)
+
+
+class AnomalyOverview(BaseModel):
+    actuator_entity_id: str
+    friendly_name: str | None = None
+    anomalies: list[AnomalyEvent] = Field(default_factory=list)
 
 
 @router.get("/discovery", response_model=list[HaEntitySummary])
@@ -124,8 +148,19 @@ def discover_actuators(
     if cached_entities:
         entities = {entity.entity_id: entity for entity in cached_entities}
     else:
-        fresh_entities = list(ha_reader.read_entities())
-        _save_cached_entities(request, fresh_entities)
+        job = _start_job(
+            request,
+            kind="discovery",
+            trigger="manual" if refresh else "cache-miss",
+            summary="Home-Assistant-Entities werden gelesen und klassifiziert.",
+        )
+        try:
+            fresh_entities = list(ha_reader.read_entities())
+            _save_cached_entities(request, fresh_entities)
+        except Exception as exc:
+            _finish_job(job, request, status=JobStatus.FAILED, summary="Discovery fehlgeschlagen.", error=str(exc))
+            raise
+        _finish_job(job, request, status=JobStatus.COMPLETED, summary=f"{len(fresh_entities)} Entities klassifiziert.")
         entities = {entity.entity_id: entity for entity in fresh_entities}
     discovered = discover_entities(list(entities.values()))
     actuator_ids = _deduplicate_actuator_ids(
@@ -245,6 +280,14 @@ def list_configured_summary(request: Request) -> list[ActuatorSummary]:
             activation_ready=record.behavior.activation_ready,
             activation_reason=record.behavior.activation_reason,
             sample_count=record.behavior.sample_count,
+            anomaly_count=len([item for item in record.behavior.anomalies if not item.resolved]),
+            critical_anomaly_count=len(
+                [
+                    item
+                    for item in record.behavior.anomalies
+                    if not item.resolved and item.severity == "critical"
+                ]
+            ),
             prediction_target_state=(
                 record.behavior.prediction.target_state
                 if record.behavior.prediction is not None
@@ -263,6 +306,25 @@ def list_configured_summary(request: Request) -> list[ActuatorSummary]:
 
 @router.get("/dashboard", response_model=DashboardOverview)
 def dashboard_overview(request: Request) -> DashboardOverview:
+    return _dashboard_overview(request, include_background=True, include_actuators=True)
+
+
+@router.get("/dashboard/start", response_model=DashboardOverview)
+def dashboard_start(request: Request) -> DashboardOverview:
+    return _dashboard_overview(request, include_background=False, include_actuators=True)
+
+
+@router.get("/dashboard/system", response_model=DashboardOverview)
+def dashboard_system(request: Request) -> DashboardOverview:
+    return _dashboard_overview(request, include_background=False, include_actuators=False)
+
+
+def _dashboard_overview(
+    request: Request,
+    *,
+    include_background: bool,
+    include_actuators: bool,
+) -> DashboardOverview:
     cache_payload = _load_entity_cache_payload(request)
     raw_entities = cache_payload.get("entities", [])
     if not isinstance(raw_entities, list):
@@ -274,10 +336,19 @@ def dashboard_overview(request: Request) -> DashboardOverview:
         DashboardDiscoveryGroup.model_validate(group)
         for group in raw_groups
         if isinstance(group, dict)
-    ] if isinstance(raw_groups, list) else []
+    ] if include_background and isinstance(raw_groups, list) else []
     reconciliation = _reconciliation_state_or_default(request)
     ws_status = getattr(request.app.state, "ws_status", None)
-    actuators = list_configured_summary(request)
+    actuators = list_configured_summary(request) if include_actuators else []
+    store = getattr(request.app.state, "actuator_store", None)
+    jobs = (
+        store.load_job_queue()
+        if include_background and isinstance(store, ActuatorStore)
+        else JobQueueState()
+    )
+    job_p95_duration_ms, slow_job_count, performance_status = _performance_status(jobs)
+    anomaly_count = sum(record.anomaly_count for record in actuators)
+    critical_anomaly_count = sum(record.critical_anomaly_count for record in actuators)
     return DashboardOverview(
         system=DashboardSystemStatus(
             websocket_status=getattr(ws_status, "status", "unavailable"),
@@ -287,9 +358,18 @@ def dashboard_overview(request: Request) -> DashboardOverview:
                 if reconciliation.last_completed_at is not None
                 else None
             ),
-            configured_actuators=len(actuators),
+            configured_actuators=(
+                len(actuators)
+                if include_actuators
+                else reconciliation.configured_actuators
+            ),
             trained_models=reconciliation.trained_models,
             review_required=reconciliation.review_required,
+            job_p95_duration_ms=job_p95_duration_ms,
+            slow_job_count=slow_job_count,
+            performance_status=performance_status,
+            anomaly_count=anomaly_count,
+            critical_anomaly_count=critical_anomaly_count,
         ),
         cache=EntityCacheStatus(
             available=bool(raw_entities),
@@ -298,9 +378,33 @@ def dashboard_overview(request: Request) -> DashboardOverview:
         ),
         actuators=actuators,
         discovery_groups=cached_groups,
+        jobs=jobs,
     )
 
 
+@router.get("/anomalies", response_model=list[AnomalyOverview])
+def list_anomalies(request: Request) -> list[AnomalyOverview]:
+    records = _service(request).list_configured()
+    entity_map = _load_cached_entity_map(
+        request,
+        {record.actuator_entity_id for record in records},
+    )
+    overview: list[AnomalyOverview] = []
+    for record in records:
+        active = [item for item in record.behavior.anomalies if not item.resolved]
+        if not active:
+            continue
+        entity = entity_map.get(record.actuator_entity_id)
+        overview.append(
+            AnomalyOverview(
+                actuator_entity_id=record.actuator_entity_id,
+                friendly_name=entity.friendly_name if entity is not None else None,
+                anomalies=active,
+            )
+        )
+    return overview
+
+
 @router.get("", response_model=list[ActuatorRecord])
 def list_configured(request: Request) -> list[ActuatorRecord]:
     return _service(request).list_configured()
@@ -327,6 +431,47 @@ def get_actuator(actuator_entity_id: str, request: Request) -> ActuatorRecord:
         raise HTTPException(status_code=404, detail=str(exc)) from exc
 
 
+@router.get("/{actuator_entity_id}/detail", response_model=ActuatorRecord)
+def get_actuator_detail(actuator_entity_id: str, request: Request) -> ActuatorRecord:
+    try:
+        record = _service(request).get_actuator(actuator_entity_id)
+    except KeyError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+    selected_ids = {
+        entity_id
+        for entity_id in [
+            record.assignment.selected_numeric_entity_id,
+            *record.assignment.selected_context_entity_ids,
+        ]
+        if entity_id
+    }
+    compact_snapshots = [
+        snapshot.model_copy(update={"patterns": []})
+        for snapshot in record.behavior.model_snapshots[-3:]
+    ]
+    compact_behavior = record.behavior.model_copy(
+        update={
+            "patterns": [],
+            "model_snapshots": compact_snapshots,
+        }
+    )
+    return record.model_copy(
+        update={
+            "behavior": compact_behavior,
+            "numeric_candidates": [
+                candidate
+                for candidate in record.numeric_candidates
+                if candidate.entity_id in selected_ids
+            ],
+            "context_candidates": [
+                candidate
+                for candidate in record.context_candidates
+                if candidate.entity_id in selected_ids
+            ],
+        }
+    )
+
+
 @router.delete("/{actuator_entity_id}", status_code=204)
 def delete_actuator(actuator_entity_id: str, request: Request) -> None:
     _service(request).delete_actuator(actuator_entity_id)
@@ -372,6 +517,32 @@ def record_feedback(
         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,
+    payload: SafetyProfileRequest,
+    request: Request,
+) -> ActuatorRecord:
+    try:
+        return _behavior(request).set_safety_profile(actuator_entity_id, profile=payload.safety)
+    except KeyError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+
+
+@router.post("/{actuator_entity_id}/model/rollback", response_model=ActuatorRecord)
+def rollback_model(
+    actuator_entity_id: str,
+    payload: ModelRollbackRequest,
+    request: Request,
+) -> ActuatorRecord:
+    try:
+        return _behavior(request).rollback_model(actuator_entity_id, version_id=payload.version_id)
+    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}/activation", response_model=ActuatorRecord)
 def set_activation(
     actuator_entity_id: str,
@@ -441,11 +612,27 @@ def refresh_related_automations(
     actuator_entity_id: str,
     request: Request,
 ) -> ActuatorRecord:
+    job = _start_job(
+        request,
+        kind="automation_refresh",
+        trigger="manual",
+        target=actuator_entity_id,
+        summary="Passende HA-Automationen werden gesucht.",
+    )
     try:
-        return _behavior(request).refresh_related_automations(actuator_entity_id)
+        record = _behavior(request).refresh_related_automations(actuator_entity_id)
+        _finish_job(
+            job,
+            request,
+            status=JobStatus.COMPLETED,
+            summary=f"{len(record.behavior.related_automations)} Automationen gefunden.",
+        )
+        return record
     except KeyError as exc:
+        _finish_job(job, request, status=JobStatus.FAILED, summary="Automation-Refresh fehlgeschlagen.", error=str(exc))
         raise HTTPException(status_code=404, detail=str(exc)) from exc
     except (ValueError, HaClientError) as exc:
+        _finish_job(job, request, status=JobStatus.FAILED, summary="Automation-Refresh fehlgeschlagen.", error=str(exc))
         raise HTTPException(status_code=409, detail=str(exc)) from exc
 
 
@@ -486,12 +673,105 @@ def run_reconciliation(
     request: Request,
     trigger: str = Query(default="manual", pattern=r"^[a-z0-9_-]{1,32}$"),
 ) -> ReconciliationState:
-    state = _service(request).reconcile_all(trigger=trigger)
-    _behavior(request).train_all()
-    _behavior(request).evaluate_all()
+    reconciliation_job = _start_job(
+        request,
+        kind="reconciliation",
+        trigger=trigger,
+        summary="Kontext, Zuordnung und Modelle werden abgeglichen.",
+    )
+    training_job: JobQueueItem | None = None
+    evaluation_job: JobQueueItem | None = None
+    try:
+        state = _service(request).reconcile_all(trigger=trigger)
+        _finish_job(reconciliation_job, request, status=JobStatus.COMPLETED, summary=state.last_summary)
+        reconciliation_job = None
+        training_job = _start_job(
+            request,
+            kind="training",
+            trigger=trigger,
+            summary="Gelernte Aktorhandlungen werden aktualisiert.",
+        )
+        _behavior(request).train_all()
+        _finish_job(training_job, request, status=JobStatus.COMPLETED, summary="Training abgeschlossen.")
+        training_job = None
+        evaluation_job = _start_job(
+            request,
+            kind="evaluation",
+            trigger=trigger,
+            summary="Aktuelle Vorhersagen werden neu berechnet.",
+        )
+        _behavior(request).evaluate_all()
+        _finish_job(evaluation_job, request, status=JobStatus.COMPLETED, summary="Evaluation abgeschlossen.")
+        evaluation_job = None
+    except Exception as exc:
+        for job in [reconciliation_job, training_job, evaluation_job]:
+            if isinstance(job, JobQueueItem) and job.status is JobStatus.RUNNING:
+                _finish_job(job, request, status=JobStatus.FAILED, summary="Job fehlgeschlagen.", error=str(exc))
+        raise
     return state
 
 
+@router.get("/job-queue/state", response_model=JobQueueState)
+def get_job_queue(request: Request) -> JobQueueState:
+    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 store.load_job_queue()
+
+
+def _start_job(
+    request: Request,
+    *,
+    kind: str,
+    trigger: str,
+    target: str | None = None,
+    summary: str = "",
+) -> JobQueueItem | None:
+    store = getattr(request.app.state, "actuator_store", None)
+    if not isinstance(store, ActuatorStore):
+        return None
+    return store.start_job(kind=kind, trigger=trigger, target=target, summary=summary)
+
+
+def _finish_job(
+    job: JobQueueItem | None,
+    request: Request,
+    *,
+    status: JobStatus,
+    summary: str,
+    error: str | None = None,
+) -> None:
+    if job is None:
+        return
+    store = getattr(request.app.state, "actuator_store", None)
+    if not isinstance(store, ActuatorStore):
+        return
+    store.finish_job(job.job_id, status=status, summary=summary, error=error)
+
+
+def _performance_status(jobs: JobQueueState) -> tuple[int | None, int, str]:
+    budget_ms = 3000
+    durations = sorted(
+        job.duration_ms
+        for job in jobs.jobs
+        if job.status is JobStatus.COMPLETED and job.duration_ms is not None
+    )
+    slow_count = sum(1 for duration in durations if duration >= budget_ms)
+    if durations:
+        index = min(len(durations) - 1, int(round((len(durations) - 1) * 0.95)))
+        p95: int | None = durations[index]
+        status_value = "slow" if slow_count else "ok"
+    else:
+        p95 = None
+        status_value = "unknown"
+    if any(job.status is JobStatus.RUNNING for job in jobs.jobs):
+        status_value = "running" if status_value == "unknown" else status_value
+    return p95, slow_count, status_value
+
+
 def _service(request: Request) -> ActuatorReconciliationService:
     service = getattr(request.app.state, "actuator_service", None)
     if not isinstance(service, ActuatorReconciliationService):

app/behavior/engine.py

@@ -7,13 +7,22 @@ from zoneinfo import ZoneInfo
 
 from app.actuators.models import (
     ActuatorRecord,
+    AdaptiveWeightUpdate,
+    AnomalyEvent,
+    AutomationConflict,
     BehaviorMode,
     BehaviorPattern,
     BehaviorPrediction,
     BehaviorState,
     BehaviorStatus,
+    DecisionFactor,
     ExecutionEvent,
+    ManualOverride,
+    ModelSnapshot,
     RelatedAutomation,
+    SafetyProfile,
+    SafetyStage,
+    TimeProfile,
 )
 from app.actuators.store import ActuatorStore
 from app.config import Settings
@@ -23,6 +32,8 @@ from app.ha.models import HaEntitySummary
 from app.ha.reader import HaReader
 
 _MAX_PATTERNS = 500
+_MAX_MODEL_SNAPSHOTS = 3
+_MAX_SNAPSHOT_PATTERNS = 120
 _MAX_EXECUTION_EVENTS = 100
 _ACTION_LOGBOOK_TOLERANCE = timedelta(seconds=10)
 _CONTEXT_TRIGGER_TOLERANCE = timedelta(seconds=3)
@@ -80,6 +91,16 @@ class BehaviorEngine:
                         ),
                         "last_trained_at": now,
                         "reason": "Noch kein geeigneter Kontext für Verhaltenslernen vorhanden.",
+                        "anomalies": _detect_anomalies(
+                            record,
+                            now=now,
+                            min_behavior_actions=self._settings.min_behavior_actions,
+                            stale_hours=self._settings.retrain_stale_hours,
+                            sample_count=0,
+                            trusted_actions=0,
+                            prediction=None,
+                            safety_blockers=[],
+                        ),
                     }
                 ),
             )
@@ -120,6 +141,16 @@ class BehaviorEngine:
                         "patterns": [],
                         "last_trained_at": now,
                         "reason": "Noch keine historischen Aktorhandlungen gefunden.",
+                        "anomalies": _detect_anomalies(
+                            record,
+                            now=now,
+                            min_behavior_actions=self._settings.min_behavior_actions,
+                            stale_hours=self._settings.retrain_stale_hours,
+                            sample_count=0,
+                            trusted_actions=0,
+                            prediction=None,
+                            safety_blockers=[],
+                        ),
                     }
                 ),
             )
@@ -165,6 +196,7 @@ class BehaviorEngine:
                 "eindeutig zugeordnete Handlungen fehlen."
             )
         )
+        model_version_id = f"model-{now.strftime('%Y%m%d%H%M%S')}"
         behavior = record.behavior.model_copy(
             update={
                 "status": status,
@@ -175,6 +207,32 @@ class BehaviorEngine:
                 "patterns": patterns[-_MAX_PATTERNS:],
                 "last_trained_at": now,
                 "reason": reason,
+                "sample_trend": [*record.behavior.sample_trend, len(patterns)][-30:],
+                "knowledge": _knowledge_lines(record, len(patterns), trusted_actions),
+                "assumptions": _assumption_lines(record),
+                "uncertainties": _uncertainty_lines(record, len(patterns), trusted_actions),
+                "time_profiles": _time_profiles(patterns),
+                "model_snapshots": _next_model_snapshots(
+                    record.behavior.model_snapshots,
+                    model_version_id,
+                    patterns[-_MAX_PATTERNS:],
+                    len(patterns),
+                    trusted_actions,
+                    _average(record.behavior.confidence_trend),
+                    record.behavior.incorrect_feedback_count,
+                    reason,
+                ),
+                "active_model_version": model_version_id,
+                "anomalies": _detect_anomalies(
+                    record,
+                    now=now,
+                    min_behavior_actions=self._settings.min_behavior_actions,
+                    stale_hours=self._settings.retrain_stale_hours,
+                    sample_count=len(patterns),
+                    trusted_actions=trusted_actions,
+                    prediction=record.behavior.prediction,
+                    safety_blockers=record.behavior.safety_blockers,
+                ),
             }
         )
         return self._save_behavior(record, behavior)
@@ -268,16 +326,25 @@ class BehaviorEngine:
             timezone_name=self._settings.timezone,
         )
         if prediction is not None:
+            safety_allowed, safety_blockers = self._assess_safety(
+                record,
+                actuator.state,
+                prediction,
+                now,
+            )
             prediction = prediction.model_copy(
                 update={
-                    "execution_reason": self._prediction_execution_reason(
-                        record,
-                        actuator.state,
-                        prediction,
-                        now,
+                    "execution_reason": (
+                        "Ausführung ist freigegeben."
+                        if safety_allowed
+                        else "Nicht ausgeführt: " + " ".join(safety_blockers)
                     )
                 }
             )
+        else:
+            safety_allowed = False
+            safety_blockers = ["Keine fällige Vorhersage."]
+        decision_factors = _decision_factors_for(record, current_context, prediction)
         behavior = record.behavior.model_copy(
             update={
                 "last_evaluated_at": now,
@@ -287,18 +354,31 @@ class BehaviorEngine:
                     if prediction is not None
                     else "Aktuell ist kein gelerntes Handlungsmuster fällig."
                 ),
+                "decision_factors": decision_factors,
+                "knowledge": _knowledge_lines(record, record.behavior.sample_count, record.behavior.high_confidence_sample_count),
+                "assumptions": _assumption_lines(record),
+                "uncertainties": _uncertainty_lines(record, record.behavior.sample_count, record.behavior.high_confidence_sample_count),
+                "safety_blockers": safety_blockers if prediction is not None else [],
+                "anomalies": _detect_anomalies(
+                    record,
+                    now=now,
+                    min_behavior_actions=self._settings.min_behavior_actions,
+                    stale_hours=self._settings.retrain_stale_hours,
+                    sample_count=record.behavior.sample_count,
+                    trusted_actions=record.behavior.high_confidence_sample_count,
+                    prediction=prediction,
+                    safety_blockers=safety_blockers if prediction is not None else [],
+                ),
+                "confidence_trend": (
+                    [*record.behavior.confidence_trend, round(prediction.confidence, 4)][-30:]
+                    if prediction is not None
+                    else record.behavior.confidence_trend
+                ),
             }
         )
         if (
             prediction is not None
-            and behavior.mode is BehaviorMode.ACTIVE
-            and prediction.confidence >= self._settings.prediction_confidence
-            and actuator.state != prediction.target_state
-            and self._cooldown_elapsed(
-                behavior,
-                now,
-                prediction.target_state,
-            )
+            and safety_allowed
         ):
             domain = actuator_entity_id.split(".", 1)[0]
             service = service_for_state(domain, prediction.target_state)
@@ -403,6 +483,8 @@ 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
         else:
             target = prediction.target_state if prediction is not None else None
             if target:
@@ -431,6 +513,13 @@ 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
+        adaptive_updates, manual_override = _adapt_sensor_weights(
+            record,
+            current_context,
+            correct=correct,
+        )
         behavior = record.behavior.model_copy(
             update={
                 "patterns": patterns[-_MAX_PATTERNS:],
@@ -441,6 +530,68 @@ class BehaviorEngine:
                 ),
                 "reason": reason,
                 "last_trained_at": now,
+                "correct_feedback_count": correct_count,
+                "incorrect_feedback_count": incorrect_count,
+                "adaptive_weight_updates": [
+                    *record.behavior.adaptive_weight_updates,
+                    *adaptive_updates,
+                ][-50:],
+                "anomalies": _detect_anomalies(
+                    record,
+                    now=now,
+                    min_behavior_actions=self._settings.min_behavior_actions,
+                    stale_hours=self._settings.retrain_stale_hours,
+                    sample_count=len(patterns),
+                    trusted_actions=record.behavior.high_confidence_sample_count,
+                    prediction=prediction,
+                    safety_blockers=record.behavior.safety_blockers,
+                    correct_feedback_count=correct_count,
+                    incorrect_feedback_count=incorrect_count,
+                ),
+            }
+        )
+        record_for_save = (
+            record.model_copy(update={"manual_override": manual_override})
+            if manual_override is not None
+            else record
+        )
+        return self._save_behavior(record_for_save, behavior)
+
+    def rollback_model(
+        self,
+        actuator_entity_id: str,
+        *,
+        version_id: str,
+    ) -> ActuatorRecord:
+        record = self._store.get(actuator_entity_id)
+        snapshot = next(
+            (item for item in record.behavior.model_snapshots if item.version_id == version_id),
+            None,
+        )
+        if snapshot is None:
+            raise ValueError("Modell-Snapshot nicht gefunden.")
+        behavior = record.behavior.model_copy(
+            update={
+                "patterns": snapshot.patterns,
+                "sample_count": snapshot.sample_count,
+                "high_confidence_sample_count": snapshot.high_confidence_sample_count,
+                "active_model_version": snapshot.version_id,
+                "reason": f"Rollback auf Modell-Snapshot {snapshot.version_id}.",
+            }
+        )
+        return self._save_behavior(record, behavior)
+
+    def set_safety_profile(
+        self,
+        actuator_entity_id: str,
+        *,
+        profile: SafetyProfile,
+    ) -> ActuatorRecord:
+        record = self._store.get(actuator_entity_id)
+        behavior = record.behavior.model_copy(
+            update={
+                "safety": profile.model_copy(update={"updated_at": datetime.now(timezone.utc)}),
+                "reason": "Sicherheitsprofil wurde manuell aktualisiert.",
             }
         )
         return self._save_behavior(record, behavior)
@@ -459,7 +610,24 @@ class BehaviorEngine:
             )
         ]
         behavior = record.behavior.model_copy(
-            update={"related_automations": related}
+            update={
+                "related_automations": related,
+                "automation_conflicts": _automation_conflicts(record, related),
+            }
+        )
+        behavior = behavior.model_copy(
+            update={
+                "anomalies": _detect_anomalies(
+                    record.model_copy(update={"behavior": behavior}),
+                    now=datetime.now(timezone.utc),
+                    min_behavior_actions=self._settings.min_behavior_actions,
+                    stale_hours=self._settings.retrain_stale_hours,
+                    sample_count=behavior.sample_count,
+                    trusted_actions=behavior.high_confidence_sample_count,
+                    prediction=behavior.prediction,
+                    safety_blockers=behavior.safety_blockers,
+                )
+            }
         )
         return self._save_behavior(record, behavior)
 
@@ -527,6 +695,9 @@ class BehaviorEngine:
                 update={
                     "mode": mode,
                     "approved_at": approved_at,
+                    "safety": record.behavior.safety.model_copy(
+                        update={"stage": SafetyStage.ACTIVE, "updated_at": now}
+                    ),
                     "reason": (
                         "Autonomes Lernen und Schalten wurde ausdrücklich freigegeben."
                     ),
@@ -607,6 +778,9 @@ class BehaviorEngine:
             update={
                 "mode": mode,
                 "approved_at": approved_at,
+                "safety": record.behavior.safety.model_copy(
+                    update={"stage": SafetyStage.SHADOW, "updated_at": now}
+                ),
                 "related_automations": [
                     automation.model_copy(update={"enabled": True})
                     if (
@@ -651,6 +825,51 @@ class BehaviorEngine:
             return "Nicht ausgeführt: Sicherheits-Cooldown ist noch aktiv."
         return "Ausführung ist freigegeben."
 
+    def _assess_safety(
+        self,
+        record: ActuatorRecord,
+        current_state: str | None,
+        prediction: BehaviorPrediction,
+        now: datetime,
+    ) -> tuple[bool, list[str]]:
+        profile = record.behavior.safety
+        blockers: list[str] = []
+        domain = record.actuator_entity_id.split(".", 1)[0]
+        if not record.enabled:
+            blockers.append("Aktor ist in SillyHome deaktiviert.")
+        if domain not in _SAFE_ACTIVE_DOMAINS:
+            blockers.append(f"Domain {domain} ist nicht für autonomes Schalten freigegeben.")
+        if profile.manual_block:
+            blockers.append("Manuelle Sicherheitssperre ist aktiv.")
+        stage = profile.stage
+        if (
+            record.behavior.mode is BehaviorMode.ACTIVE
+            and profile.updated_at is None
+            and stage is SafetyStage.SHADOW
+        ):
+            stage = SafetyStage.ACTIVE
+        if stage not in {SafetyStage.ACTIVE, SafetyStage.PARTIAL}:
+            blockers.append(f"Safety-Stufe {stage.value} erlaubt noch kein Schalten.")
+        if record.behavior.mode is not BehaviorMode.ACTIVE:
+            blockers.append("SillyHome ist im Shadow-Modus.")
+        if not record.behavior.activation_ready:
+            blockers.append(record.behavior.activation_reason)
+        threshold = _confidence_threshold_for(profile, prediction.target_state)
+        if prediction.confidence < threshold:
+            blockers.append(
+                f"Sicherheit {prediction.confidence:.0%} liegt unter der Schwelle {threshold:.0%}."
+            )
+        if current_state == prediction.target_state:
+            blockers.append("Zielzustand ist bereits erreicht.")
+        if not self._cooldown_elapsed(
+            record.behavior,
+            now,
+            prediction.target_state,
+            cooldown_seconds=profile.cooldown_seconds,
+        ):
+            blockers.append("Sicherheits-Cooldown ist noch aktiv.")
+        return not blockers, blockers
+
     def _build_patterns(
         self,
         *,
@@ -701,6 +920,8 @@ class BehaviorEngine:
         behavior: BehaviorState,
         now: datetime,
         target_state: str,
+        *,
+        cooldown_seconds: int | None = None,
     ) -> bool:
         if behavior.last_executed_at is None:
             return True
@@ -708,7 +929,9 @@ class BehaviorEngine:
         if last_event is not None and last_event.target_state != target_state:
             return True
         return (now - behavior.last_executed_at) >= timedelta(
-            seconds=self._settings.execution_cooldown_seconds
+            seconds=cooldown_seconds
+            if cooldown_seconds is not None
+            else self._settings.execution_cooldown_seconds
         )
 
     def _save_behavior(
@@ -716,6 +939,11 @@ class BehaviorEngine:
         record: ActuatorRecord,
         behavior: BehaviorState,
     ) -> ActuatorRecord:
+        behavior = behavior.model_copy(
+            update={
+                "model_snapshots": _compact_model_snapshots(behavior.model_snapshots),
+            }
+        )
         updated = record.model_copy(
             update={
                 "behavior": behavior,
@@ -791,6 +1019,387 @@ def _event_changed_at(new_state: dict[str, object] | None) -> datetime | None:
     return parsed
 
 
+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
+    if target_state in {"off", "closed"} and profile.min_confidence_off is not None:
+        return profile.min_confidence_off
+    return profile.min_confidence
+
+
+def _decision_factors_for(
+    record: ActuatorRecord,
+    current_context: dict[str, str | None],
+    prediction: BehaviorPrediction | None,
+) -> list[DecisionFactor]:
+    factors: list[DecisionFactor] = []
+    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
+        relevance = candidate.confidence if candidate is not None else 0.5
+        contribution = round(min(1.0, weight * relevance), 4)
+        factors.append(
+            DecisionFactor(
+                entity_id=entity_id,
+                label=(
+                    candidate.friendly_name
+                    if candidate is not None and candidate.friendly_name
+                    else entity_id
+                ),
+                factor_type="context",
+                state=state,
+                weight=round(weight, 4),
+                contribution=contribution,
+                evidence=(
+                    candidate.evidence[:4]
+                    if candidate is not None
+                    else ["Aktuell ausgewähltes Kontextsignal."]
+                ),
+            )
+        )
+    if prediction is not None:
+        factors.append(
+            DecisionFactor(
+                label=f"Vorhersage {prediction.target_state}",
+                factor_type="prediction",
+                state=prediction.target_state,
+                weight=1.0,
+                contribution=prediction.confidence,
+                evidence=[prediction.reason],
+            )
+        )
+    return sorted(factors, key=lambda item: (-item.contribution, item.label))[:12]
+
+
+def _knowledge_lines(
+    record: ActuatorRecord,
+    sample_count: int,
+    trusted_actions: int,
+) -> list[str]:
+    lines = [
+        f"{sample_count} historische Aktorhandlungen sind ausgewertet.",
+        f"{trusted_actions} Handlungen stammen eindeutig von Nutzer oder HA-Automationen.",
+    ]
+    if record.assignment.selected_numeric_entity_id:
+        lines.append(f"Hauptsensor: {record.assignment.selected_numeric_entity_id}.")
+    if record.assignment.selected_context_entity_ids:
+        lines.append(
+            f"{len(record.assignment.selected_context_entity_ids)} Kontextsignale sind verbunden."
+        )
+    return lines
+
+
+def _assumption_lines(record: ActuatorRecord) -> list[str]:
+    lines = [
+        "Ähnliche Zeitfenster und ähnliche Kontextzustände deuten auf ähnliche Nutzerabsicht hin."
+    ]
+    if record.manual_override is not None:
+        lines.append("Manuelle Sensor-/Kontextkorrekturen werden höher gewichtet.")
+    if record.behavior.related_automations:
+        lines.append("Passende HA-Automationen gelten als starker Hinweis auf vorhandene Logik.")
+    return lines
+
+
+def _uncertainty_lines(
+    record: ActuatorRecord,
+    sample_count: int,
+    trusted_actions: int,
+) -> list[str]:
+    lines: list[str] = []
+    if sample_count < trusted_actions + 3:
+        lines.append("Noch wenig Varianz in den gelernten Handlungen.")
+    if trusted_actions < sample_count:
+        lines.append("Ein Teil der Handlungen ist nicht eindeutig Nutzer oder Automation zugeordnet.")
+    if record.assignment.review_required:
+        lines.append("Die automatische Kontextzuordnung verlangt noch Prüfung.")
+    if record.behavior.incorrect_feedback_count:
+        lines.append(
+            f"{record.behavior.incorrect_feedback_count} negative Feedbacks senken Vertrauen."
+        )
+    return lines or ["Keine kritische Unsicherheit aus den lokalen Daten erkannt."]
+
+
+def _next_model_snapshots(
+    existing: list[ModelSnapshot],
+    version_id: str,
+    patterns: list[BehaviorPattern],
+    sample_count: int,
+    trusted_actions: int,
+    average_confidence: float,
+    incorrect_feedback_count: int,
+    reason: str,
+) -> list[ModelSnapshot]:
+    snapshot = ModelSnapshot(
+        version_id=version_id,
+        sample_count=sample_count,
+        high_confidence_sample_count=trusted_actions,
+        average_confidence=round(average_confidence, 4),
+        incorrect_feedback_count=incorrect_feedback_count,
+        patterns=patterns[-_MAX_SNAPSHOT_PATTERNS:],
+        reason=reason,
+    )
+    return _compact_model_snapshots([*existing, snapshot])
+
+
+def _compact_model_snapshots(existing: list[ModelSnapshot]) -> list[ModelSnapshot]:
+    return [
+        snapshot.model_copy(
+            update={"patterns": snapshot.patterns[-_MAX_SNAPSHOT_PATTERNS:]}
+        )
+        for snapshot in existing[-_MAX_MODEL_SNAPSHOTS:]
+    ]
+
+
+def _average(values: list[float]) -> float:
+    return sum(values) / len(values) if values else 0.0
+
+
+def _time_profiles(patterns: list[BehaviorPattern]) -> list[TimeProfile]:
+    buckets = {
+        "night": ("Nacht", range(0, 360)),
+        "morning": ("Morgen", range(360, 720)),
+        "day": ("Tag", range(720, 1080)),
+        "evening": ("Abend", range(1080, 1440)),
+    }
+    profiles: list[TimeProfile] = []
+    for profile_id, (label, minutes) in buckets.items():
+        selected = [pattern for pattern in patterns if pattern.minute_of_day in minutes]
+        if not selected:
+            profiles.append(TimeProfile(profile_id=profile_id, label=label))
+            continue
+        by_state: dict[str, int] = {}
+        for pattern in selected:
+            by_state[pattern.target_state] = by_state.get(pattern.target_state, 0) + 1
+        dominant_state, count = max(by_state.items(), key=lambda item: (item[1], item[0]))
+        profiles.append(
+            TimeProfile(
+                profile_id=profile_id,
+                label=label,
+                sample_count=len(selected),
+                dominant_state=dominant_state,
+                confidence=round(count / len(selected), 4),
+            )
+        )
+    weekend = [pattern for pattern in patterns if pattern.weekday >= 5]
+    profiles.append(
+        TimeProfile(
+            profile_id="weekend",
+            label="Wochenende",
+            sample_count=len(weekend),
+            dominant_state=(
+                max(
+                    {pattern.target_state: 0 for pattern in weekend},
+                    key=lambda state: sum(pattern.target_state == state for pattern in weekend),
+                )
+                if weekend
+                else None
+            ),
+            confidence=round(len(weekend) / len(patterns), 4) if patterns else 0.0,
+        )
+    )
+    return profiles
+
+
+def _adapt_sensor_weights(
+    record: ActuatorRecord,
+    current_context: dict[str, str | None],
+    *,
+    correct: bool,
+) -> tuple[list[AdaptiveWeightUpdate], ManualOverride | None]:
+    if not current_context:
+        return [], record.manual_override
+    candidates = {
+        candidate.entity_id: candidate
+        for candidate in [*record.numeric_candidates, *record.context_candidates]
+    }
+    previous = record.manual_override
+    weights = dict(previous.sensor_weights if previous is not None else {})
+    updates: list[AdaptiveWeightUpdate] = []
+    delta = 0.03 if correct else -0.08
+    for entity_id in current_context:
+        candidate = candidates.get(entity_id)
+        base = weights.get(
+            entity_id,
+            candidate.effective_weight if candidate is not None else 1.0,
+        )
+        new_weight = round(min(1.0, max(0.1, base + delta)), 4)
+        if new_weight == base:
+            continue
+        weights[entity_id] = new_weight
+        updates.append(
+            AdaptiveWeightUpdate(
+                entity_id=entity_id,
+                previous_weight=round(base, 4),
+                new_weight=new_weight,
+                reason=(
+                    "Feedback korrekt: Kontextsignal leicht höher gewichtet."
+                    if correct
+                    else "Feedback falsch: Kontextsignal vorsichtig abgewertet."
+                ),
+            )
+        )
+    if not updates:
+        return [], previous
+    return updates, ManualOverride(
+        numeric_entity_id=(
+            previous.numeric_entity_id
+            if previous is not None
+            else record.assignment.selected_numeric_entity_id
+        ),
+        context_entity_ids=(
+            previous.context_entity_ids
+            if previous is not None
+            else record.assignment.selected_context_entity_ids
+        ),
+        sensor_weights=weights,
+        sensor_weight_groups=previous.sensor_weight_groups if previous is not None else [],
+        note="Sensor-Gewichtungen automatisch aus Feedback angepasst.",
+    )
+
+
+def _automation_conflicts(
+    record: ActuatorRecord,
+    related: list[RelatedAutomation],
+) -> list[AutomationConflict]:
+    conflicts: list[AutomationConflict] = []
+    for automation in related:
+        if record.behavior.mode is BehaviorMode.ACTIVE and automation.enabled:
+            conflicts.append(
+                AutomationConflict(
+                    automation_entity_id=automation.entity_id,
+                    severity="warning",
+                    status="open",
+                    reason=(
+                        "SillyHome ist aktiv, aber diese passende HA-Automation "
+                        "ist ebenfalls aktiv. Das kann zu konkurrierenden Schaltungen führen."
+                    ),
+                )
+            )
+        elif automation.entity_id in record.behavior.paused_automation_entity_ids:
+            conflicts.append(
+                AutomationConflict(
+                    automation_entity_id=automation.entity_id,
+                    severity="info",
+                    status="controlled",
+                    reason="Automation ist durch SillyHome pausiert.",
+                )
+            )
+    return conflicts
+
+
+def _detect_anomalies(
+    record: ActuatorRecord,
+    *,
+    now: datetime,
+    min_behavior_actions: int,
+    stale_hours: int,
+    sample_count: int,
+    trusted_actions: int,
+    prediction: BehaviorPrediction | None,
+    safety_blockers: list[str],
+    correct_feedback_count: int | None = None,
+    incorrect_feedback_count: int | None = None,
+) -> list[AnomalyEvent]:
+    anomalies: list[AnomalyEvent] = []
+
+    def add(category: str, severity: str, title: str, detail: str) -> None:
+        anomalies.append(
+            AnomalyEvent(
+                anomaly_id=f"{record.actuator_entity_id}.{category}",
+                category=category,
+                severity=severity,
+                title=title,
+                detail=detail,
+                detected_at=now,
+            )
+        )
+
+    if not record.assignment.selected_context_entity_ids and not record.assignment.selected_numeric_entity_id:
+        add(
+            "missing_context",
+            "warning",
+            "Kein Kontext verbunden",
+            "Der Aktor hat keine Sensor-/Kontextbasis. Entscheidungen bleiben unsicher.",
+        )
+    if sample_count < min_behavior_actions:
+        add(
+            "low_samples",
+            "info",
+            "Zu wenig Lernbeispiele",
+            f"{sample_count} von {min_behavior_actions} benoetigten Handlungen gelernt.",
+        )
+    if trusted_actions < sample_count:
+        add(
+            "unclear_sources",
+            "info",
+            "Unklare Aktorhandlungen",
+            "Ein Teil der gelernten Handlungen stammt nicht eindeutig von Nutzer oder Automation.",
+        )
+    if record.behavior.last_trained_at is not None:
+        age = now - record.behavior.last_trained_at
+        if age > timedelta(hours=stale_hours):
+            add(
+                "stale_training",
+                "warning",
+                "Training ist veraltet",
+                f"Letztes Training liegt mehr als {stale_hours} Stunden zurueck.",
+            )
+    if prediction is not None and prediction.matching_patterns and prediction.confidence < record.behavior.safety.min_confidence:
+        add(
+            "low_confidence_prediction",
+            "warning",
+            "Vorhersage unter Sicherheitsgrenze",
+            (
+                f"Confidence {prediction.confidence:.0%} liegt unter "
+                f"{record.behavior.safety.min_confidence:.0%}."
+            ),
+        )
+    if record.behavior.safety.manual_block:
+        add(
+            "manual_block",
+            "info",
+            "Manuelle Sicherheitssperre aktiv",
+            "Der Aktor ist bewusst gegen automatisches Schalten gesperrt.",
+        )
+    if safety_blockers:
+        add(
+            "safety_blockers",
+            "info",
+            "Safety blockiert aktuelle Aktion",
+            " ".join(safety_blockers)[:500],
+        )
+    if any(conflict.severity == "warning" for conflict in record.behavior.automation_conflicts):
+        add(
+            "automation_conflict",
+            "critical",
+            "Parallele Automation erkannt",
+            "SillyHome und mindestens eine passende HA-Automation koennen parallel schalten.",
+        )
+    correct = (
+        record.behavior.correct_feedback_count
+        if correct_feedback_count is None
+        else correct_feedback_count
+    )
+    incorrect = (
+        record.behavior.incorrect_feedback_count
+        if incorrect_feedback_count is None
+        else incorrect_feedback_count
+    )
+    total = correct + incorrect
+    if total >= 3 and incorrect / total >= 0.35:
+        add(
+            "feedback_error_rate",
+            "critical",
+            "Viele falsche Vorhersagen",
+            f"{incorrect} von {total} Feedbacks waren negativ. Modell pruefen oder Rollback nutzen.",
+        )
+    return anomalies[-30:]
+
+
 def predict_behavior(
     patterns: list[BehaviorPattern],
     *,

app/config.py

@@ -21,6 +21,7 @@ class Settings:
     prediction_interval_seconds: int = 60
     execution_cooldown_seconds: int = 900
     timezone: str = "Europe/Berlin"
+    ha_timeout_seconds: int = 25
 
     @property
     def ha_configured(self) -> bool:
@@ -55,4 +56,5 @@ def load_settings() -> Settings:
             60, int(os.getenv("SILLYHOME_EXECUTION_COOLDOWN_SECONDS", "900"))
         ),
         timezone=os.getenv("SILLYHOME_TIMEZONE", "Europe/Berlin"),
+        ha_timeout_seconds=max(5, int(os.getenv("SILLYHOME_HA_TIMEOUT_SECONDS", "25"))),
     )

app/main.py

@@ -60,6 +60,7 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
             settings=HaClientSettings(
                 url=cast(str, settings.ha_url),
                 token=cast(str, settings.ha_token),
+                timeout_seconds=settings.ha_timeout_seconds,
             )
         )
         app.state.ha_reader = HaReader(client=client)
@@ -105,7 +106,7 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
 app = FastAPI(
     title="SillyHome Next API",
     description="Lokales Smart-Home-Intelligenzsystem für Home Assistant.",
-    version="1.0.4",
+    version="1.5.2",
     lifespan=lifespan,
 )
 app.state.settings = load_settings()
@@ -213,8 +214,8 @@ async def _ha_event_listener(app: FastAPI, client: HaClient) -> None:
         try:
             async with websockets.connect(
                 ws_url,
-                ping_interval=20,
-                ping_timeout=10,
+                ping_interval=30,
+                ping_timeout=30,
             ) as websocket:
                 auth_required_msg = await websocket.recv()
                 auth_required_data = json.loads(auth_required_msg)

app/static/index.html

@@ -25,6 +25,7 @@
       --bad:#8fb8ff;
     }
     * { box-sizing:border-box; }
+    html, body { max-width:100%; overflow-x:hidden; }
     body { margin:0; font-size:15px; background:var(--panel-quiet); }
     h1,h2,h3 { margin:0 0 10px; letter-spacing:0; }
     p { margin:6px 0; }
@@ -38,8 +39,10 @@
     .header-actions label { margin:0; font-size:.82rem; }
     .status-pill { display:flex; align-items:center; gap:8px; padding:8px 10px; border:1px solid var(--border); border-radius:8px; background:#101722; color:#d9e6f0; white-space:nowrap; }
     .dot { width:9px; height:9px; border-radius:50%; background:var(--complement); box-shadow:0 0 0 3px rgba(28,199,255,.15); }
-    main { display:grid; grid-template-columns:minmax(270px,.72fr) minmax(0,1.58fr); grid-template-areas:"control board" "control detail" "status status" "guide guide"; gap:12px; padding:12px; max-width:1480px; margin:0 auto; }
+    main { display:block; padding:12px; max-width:1480px; margin:0 auto; }
     section { background:var(--panel); border:1px solid var(--border); border-radius:8px; padding:12px; min-width:0; }
+    .app-view { display:none; }
+    .app-view.active { display:block; }
     section:target { outline:2px solid var(--complement); outline-offset:2px; }
     .control-panel { grid-area:control; align-self:start; position:sticky; top:58px; }
     .board-panel { grid-area:board; }
@@ -53,6 +56,8 @@
     .manual-context > summary,
     .group-panel > summary { cursor:pointer; font-weight:800; color:#eaf1f8; }
     details.collapsible > summary { list-style:none; display:flex; justify-content:space-between; gap:10px; }
+    .manual-context > summary,
+    .group-panel > summary { display:flex; justify-content:space-between; gap:10px; align-items:center; }
     details.collapsible > summary::-webkit-details-marker,
     .manual-context > summary::-webkit-details-marker,
     .group-panel > summary::-webkit-details-marker { display:none; }
@@ -71,31 +76,40 @@
     .bad { color: var(--bad); }
     label { display:block; margin:9px 0 4px; color:#c3d2df; font-weight:700; }
     select,input,button { width:100%; border-radius:8px; border:1px solid #3b4b5b; padding:10px; background:#111821; color:#fff; font:inherit; min-width:0; }
+    input[type="checkbox"] { width:auto; min-width:0; vertical-align:middle; margin-right:8px; }
     select[multiple] { min-height:150px; }
     button { min-height:42px; margin-top:10px; background:var(--accent); color:#211204; border:0; font-weight:850; cursor:pointer; }
     button.secondary { background:var(--complement-soft); color:#dff6ff; border:1px solid #22607c; }
     button.danger { background:#2a3441; color:#f2f6fb; border:1px solid #536273; }
     button.compact { width:auto; min-width:112px; margin-right:8px; padding:8px 10px; min-height:36px; }
-    table { width: 100%; border-collapse: collapse; font-size: .92rem; }
-    td,th { padding: 8px; border-bottom: 1px solid #2d3a47; text-align: left; vertical-align: top; }
+    table { width: 100%; border-collapse: collapse; table-layout:fixed; font-size: .92rem; }
+    td,th { padding: 8px; border-bottom: 1px solid #2d3a47; text-align: left; vertical-align: top; overflow-wrap:anywhere; word-break:break-word; }
     ul { margin: 8px 0; padding-left: 18px; }
     .notice { border-left:4px solid var(--complement); padding-left:10px; }
     .grid-two { display:grid; grid-template-columns:repeat(auto-fit,minmax(240px,1fr)); gap:12px; }
     .chips { display:flex; flex-wrap:wrap; gap:6px; margin-top:8px; }
-    .chip { padding:4px 8px; border-radius:8px; background:#222b36; border:1px solid var(--border); font-size:.85rem; }
+    .chip { padding:4px 8px; border-radius:8px; background:#222b36; border:1px solid var(--border); font-size:.85rem; max-width:100%; overflow-wrap:anywhere; word-break:break-word; }
     .muted { color:var(--text-soft); }
     .card-list { display:grid; grid-template-columns:repeat(auto-fit,minmax(250px,1fr)); gap:10px; }
     .actuator-card { background:#121922; border:1px solid var(--border); border-radius:8px; padding:10px; min-width:0; }
     .actuator-card.selected { border-color:var(--complement); box-shadow:0 0 0 1px rgba(28,199,255,.35); }
-    .card-title { display:flex; justify-content:space-between; gap:10px; align-items:flex-start; margin-bottom:8px; }
-    .entity-id { overflow-wrap:anywhere; font-weight:800; }
+    .card-title { display:flex; flex-wrap:wrap; justify-content:space-between; gap:10px; align-items:flex-start; margin-bottom:8px; min-width:0; }
+    .card-title > div { min-width:0; flex:1 1 160px; overflow-wrap:anywhere; word-break:break-word; }
+    .card-title .chip { flex:0 1 auto; white-space:normal; text-align:center; }
+    .actuator-card strong { overflow-wrap:anywhere; word-break:break-word; }
+    .entity-id { overflow-wrap:anywhere; word-break:break-word; font-weight:800; }
     .metric-grid { display:grid; grid-template-columns:repeat(auto-fit,minmax(120px,1fr)); gap:6px; margin:8px 0; }
-    .metric { background:var(--panel-soft); border:1px solid var(--border); border-radius:8px; padding:8px; min-width:0; }
+    .metric { background:var(--panel-soft); border:1px solid var(--border); border-radius:8px; padding:8px; min-width:0; overflow-wrap:anywhere; word-break:break-word; }
     .metric strong { display:block; margin-bottom:4px; color:#cfe0ec; font-size:.84rem; }
+    .decision-list { display:grid; gap:8px; margin:10px 0; }
+    .decision-row { background:#121922; border:1px solid var(--border); border-radius:8px; padding:9px; min-width:0; overflow-wrap:anywhere; }
+    .decision-row.slow,
+    .decision-row.critical { border-color:var(--warn); box-shadow:0 0 0 1px rgba(243,201,105,.25); }
+    .decision-row header { padding:0; border:0; background:transparent; display:flex; justify-content:space-between; gap:10px; flex-wrap:wrap; }
     .actions { display:flex; flex-wrap:wrap; gap:8px; margin-top:10px; }
     .actions button { flex:1 1 180px; margin-top:0; }
     .detail-header { display:flex; justify-content:space-between; gap:12px; align-items:flex-start; flex-wrap:wrap; }
-    .manual-context { margin-top:12px; background:#121922; border:1px solid var(--border); border-radius:8px; padding:10px; }
+    .manual-context { margin-top:12px; background:#121922; border:1px solid var(--border); border-radius:8px; padding:10px; min-width:0; overflow-wrap:anywhere; word-break:break-word; }
     .inline-controls { display:grid; grid-template-columns:repeat(auto-fit,minmax(160px,1fr)); gap:8px; margin:8px 0; }
     .manual-entry { min-height:80px; resize:vertical; }
     textarea { width:100%; border-radius:8px; border:1px solid #3b4b5b; padding:12px; background:#101820; color:#fff; font:inherit; }
@@ -108,7 +122,7 @@
       .topbar { display:grid; }
       .header-actions { min-width:0; }
       .status-pill { width:max-content; max-width:100%; white-space:normal; }
-      main { display:block; padding:8px; }
+      main { padding:8px; }
       .control-panel { position:static; }
       section { margin-bottom:10px; padding:10px; border-radius:8px; }
       .steps { grid-template-columns:1fr; }
@@ -137,19 +151,20 @@
     </div>
     <div class="header-actions">
       <label for="section-jump">Menü</label>
-      <select id="section-jump" onchange="jumpToSection(this.value)">
-        <option value="#choose">Steuerung</option>
-        <option value="#observed">Geräte</option>
-        <option value="#detail">Freigabe</option>
-        <option value="#status-section">System</option>
-        <option value="#guide">Ablauf</option>
+      <select id="section-jump" onchange="showView(this.value)">
+        <option value="status-section">Startseite / System</option>
+        <option value="observed">Lernen</option>
+        <option value="detail">Details</option>
+        <option value="choose">Discovery & Einrichtung</option>
+        <option value="settings">Einstellungen</option>
+        <option value="guide">Ablauf</option>
       </select>
       <div class="status-pill"><span class="dot"></span><span id="load-budget">Seite bereit, Status folgt ...</span></div>
     </div>
   </div>
 </header>
 <main>
-  <section class="control-panel" id="choose">
+  <section class="control-panel app-view" id="choose">
     <div class="panel-title">
       <h2>Steuerung</h2>
       <span class="chip">v1</span>
@@ -200,7 +215,7 @@
     <div id="actuator-suggestions" class="card-list"></div>
   </section>
 
-  <section class="board-panel" id="observed">
+  <section class="board-panel app-view" id="observed">
     <div class="panel-title">
       <div>
         <h2>Beobachtete Geräte</h2>
@@ -211,13 +226,13 @@
     <div id="configured-actuators">Noch nicht geladen.</div>
   </section>
 
-  <section class="detail-panel" id="detail">
+  <section class="detail-panel app-view" id="detail">
     <h2>Lernfortschritt und Freigabe</h2>
     <p class="muted">Die Freigabe erscheint erst, wenn genug eindeutig zugeordnete Handlungen gelernt wurden. Vorher bleibt das Gerät sicher im Beobachtungsmodus.</p>
     <div id="actuator-detail" class="muted">Öffne bei einem beobachteten Gerät die Details.</div>
   </section>
 
-  <section class="status-panel" id="status-section">
+  <section class="status-panel app-view active" id="status-section">
     <div class="panel-title">
       <div>
         <h2>System & Cache</h2>
@@ -228,9 +243,33 @@
     <div id="status">Prüfung läuft ...</div>
     <div class="chips" id="status-chips"></div>
     <div id="dashboard-stats" class="metric-grid"></div>
+    <div id="job-queue" class="decision-list"></div>
   </section>
 
-  <section class="guide-panel" id="guide">
+  <section class="guide-panel app-view" id="settings">
+    <div class="panel-title">
+      <div>
+        <h2>Einstellungen</h2>
+        <p class="muted">Sprache und Standardwerte für die Bedienoberfläche.</p>
+      </div>
+    </div>
+    <div class="grid-two">
+      <div>
+        <label for="language-select">Sprache</label>
+        <select id="language-select" onchange="setLanguage(this.value)">
+          <option value="de">Deutsch</option>
+          <option value="en">English</option>
+        </select>
+        <p class="muted">Die API speichert stabile technische Werte. Die Oberfläche übersetzt sie in die gewählte Sprache.</p>
+      </div>
+      <div>
+        <h3>Performance-Standard</h3>
+        <p>Startansichten dürfen maximal 3 Sekunden brauchen. Schwere Daten werden nur nach Menüwechsel oder bei Bearbeitung geladen.</p>
+      </div>
+    </div>
+  </section>
+
+  <section class="guide-panel app-view" id="guide">
     <details class="collapsible">
     <summary><span>So gehst du vor</span></summary>
     <div class="steps">
@@ -272,15 +311,201 @@ let cachedEntities = null;
 let cachedDiscovery = null;
 let discoveryLoadPromise = null;
 let currentSensorWeightGroups = [];
+let visibleActuatorLimit = 24;
 const ACTUATOR_RESULT_LIMIT = 50;
 const STATUS_TIMEOUT_MS = 2000;
-const DASHBOARD_TIMEOUT_MS = 4500;
+const DASHBOARD_TIMEOUT_MS = 3000;
+const I18N = {
+  de: {
+    safety_stage: {
+      observe: "Nur beobachten",
+      suggest: "Vorschläge anzeigen",
+      shadow: "Prüfmodus ohne Schalten",
+      partial: "Teilfreigabe",
+      active: "Aktiv freigegeben",
+    },
+    behavior_mode: {
+      shadow: "Prüfmodus",
+      active: "Aktiv",
+      paused: "Pausiert",
+    },
+    behavior_status: {
+      collecting: "Sammelt Lernbeispiele",
+      trained: "Gelernt",
+      blocked: "Blockiert",
+    },
+    lifecycle_status: {
+      trained: "gelernt",
+      pending_history: "sammelt Historie",
+      pending_assignment: "sucht Kontext",
+      review_required: "bitte prüfen",
+      archived: "wartet",
+      orphaned: "Aktor fehlt",
+      stale: "Training veraltet",
+      invalid: "ungültig",
+    },
+    job_status: {
+      pending: "wartet",
+      running: "läuft",
+      completed: "abgeschlossen",
+      failed: "fehlgeschlagen",
+    },
+    job_kind: {
+      discovery: "Geräte-Erkennung",
+      reconciliation: "Abgleich",
+      training: "Training",
+      evaluation: "Auswertung",
+      automation_refresh: "Automation-Prüfung",
+    },
+    severity: {
+      info: "Hinweis",
+      warning: "Warnung",
+      critical: "Kritisch",
+    },
+    anomaly_category: {
+      missing_context: "fehlender Kontext",
+      low_samples: "zu wenig Lernbeispiele",
+      unclear_sources: "unklare Quellen",
+      stale_training: "veraltetes Training",
+      low_confidence_prediction: "geringe Sicherheit",
+      manual_block: "manuelle Sperre",
+      safety_blockers: "Sicherheitsblocker",
+      automation_conflict: "Automation-Konflikt",
+      feedback_error_rate: "Feedback-Fehlerquote",
+    },
+    performance_status: {
+      ok: "schnell",
+      slow: "zu langsam",
+      running: "läuft",
+      unknown: "noch offen",
+    },
+    connection_status: {
+      connected: "verbunden",
+      disconnected: "getrennt",
+      unavailable: "nicht verfügbar",
+      error: "Fehler",
+    },
+  },
+  en: {
+    safety_stage: {
+      observe: "Observe only",
+      suggest: "Show suggestions",
+      shadow: "Review mode without switching",
+      partial: "Partial approval",
+      active: "Active approval",
+    },
+    behavior_mode: {
+      shadow: "Review mode",
+      active: "Active",
+      paused: "Paused",
+    },
+    behavior_status: {
+      collecting: "Collecting examples",
+      trained: "Learned",
+      blocked: "Blocked",
+    },
+    lifecycle_status: {
+      trained: "learned",
+      pending_history: "collecting history",
+      pending_assignment: "finding context",
+      review_required: "review required",
+      archived: "waiting",
+      orphaned: "actuator missing",
+      stale: "training stale",
+      invalid: "invalid",
+    },
+    job_status: {
+      pending: "waiting",
+      running: "running",
+      completed: "completed",
+      failed: "failed",
+    },
+    job_kind: {
+      discovery: "Discovery",
+      reconciliation: "Reconciliation",
+      training: "Training",
+      evaluation: "Evaluation",
+      automation_refresh: "Automation check",
+    },
+    severity: {
+      info: "Info",
+      warning: "Warning",
+      critical: "Critical",
+    },
+    anomaly_category: {
+      missing_context: "missing context",
+      low_samples: "not enough samples",
+      unclear_sources: "unclear sources",
+      stale_training: "stale training",
+      low_confidence_prediction: "low confidence",
+      manual_block: "manual block",
+      safety_blockers: "safety blockers",
+      automation_conflict: "automation conflict",
+      feedback_error_rate: "feedback error rate",
+    },
+    performance_status: {
+      ok: "fast",
+      slow: "too slow",
+      running: "running",
+      unknown: "unknown",
+    },
+    connection_status: {
+      connected: "connected",
+      disconnected: "disconnected",
+      unavailable: "unavailable",
+      error: "error",
+    },
+  },
+};
+let uiLang = localStorage.getItem("sillyhome.ui.language") || "de";
 
 function jumpToSection(target) {
   if (!target) return;
   document.querySelector(target)?.scrollIntoView({behavior: "smooth", block: "start"});
 }
 
+function showView(viewId) {
+  for (const section of document.querySelectorAll(".app-view")) {
+    section.classList.toggle("active", section.id === viewId);
+  }
+  localStorage.setItem("sillyhome.ui.view", viewId);
+  if (viewId === "status-section") {
+    void loadSystemOverview();
+  } else if (viewId === "observed") {
+    void loadOverview();
+  } else if (viewId === "settings") {
+    syncSettingsView();
+  }
+  document.getElementById(viewId)?.scrollIntoView({behavior: "smooth", block: "start"});
+}
+
+function setLanguage(language) {
+  uiLang = I18N[language] ? language : "de";
+  localStorage.setItem("sillyhome.ui.language", uiLang);
+  syncSettingsView();
+  if (cachedActuators) renderConfiguredActuators();
+  void loadSystemOverview();
+  if (currentActuatorId) void showActuator(currentActuatorId);
+}
+
+function syncSettingsView() {
+  const select = document.getElementById("language-select");
+  if (select) select.value = uiLang;
+}
+
+function translate(group, value, fallback = "") {
+  if (value == null || value === "") return fallback || "offen";
+  return I18N[uiLang]?.[group]?.[value] || fallback || String(value);
+}
+
+function formatDateTime(value) {
+  if (!value) return "noch offen";
+  const parsed = new Date(value);
+  return Number.isNaN(parsed.getTime())
+    ? String(value)
+    : parsed.toLocaleString("de-DE");
+}
+
 function uniqueValues(values) {
   return [...new Set(values.filter(Boolean))];
 }
@@ -303,6 +528,11 @@ async function apiWithTimeout(path, timeoutMs = STATUS_TIMEOUT_MS) {
   const timeout = setTimeout(() => controller.abort(), timeoutMs);
   try {
     return await api(path, {signal: controller.signal});
+  } catch (error) {
+    if (error?.name === "AbortError") {
+      throw new Error("Zeitlimit erreicht; Daten laden im Hintergrund weiter.");
+    }
+    throw error;
   } finally {
     clearTimeout(timeout);
   }
@@ -313,12 +543,12 @@ function lifecycleLabel(record) {
   const lifecycleStatus = record.lifecycle_status || record.lifecycle?.status;
   if (behaviorStatus === "trained") return "Kontext erkannt";
   const labels = {
-    trained: "lernt",
-    pending_history: "sammelt Historie",
-    pending_assignment: "sucht Kontext",
-    review_required: "geringe Zuordnungssicherheit",
-    archived: "wartet auf Kontext",
-    orphaned: "Aktor nicht gefunden",
+    trained: translate("lifecycle_status", "trained"),
+    pending_history: translate("lifecycle_status", "pending_history"),
+    pending_assignment: translate("lifecycle_status", "pending_assignment"),
+    review_required: translate("lifecycle_status", "review_required"),
+    archived: translate("lifecycle_status", "archived"),
+    orphaned: translate("lifecycle_status", "orphaned"),
   };
   return labels[lifecycleStatus] || lifecycleStatus;
 }
@@ -336,7 +566,7 @@ function behaviorLabel(record) {
   const mode = record.behavior_mode || record.behavior?.mode;
   const status = record.behavior_status || record.behavior?.status;
   if (mode === "active") return "aktiv freigegeben";
-  if (status === "trained") return "Shadow-Vorhersage";
+  if (status === "trained") return "Prüfmodus mit Vorhersage";
   if (status === "blocked") return "Lernen blockiert";
   return "sammelt Handlungen";
 }
@@ -429,12 +659,18 @@ async function loadOverview() {
   if (budget) budget.textContent = "Startdaten laden ...";
   document.getElementById("configured-actuators").innerHTML = "<p class='muted'>Beobachtete Geräte werden geladen ...</p>";
   try {
-    const dashboard = await apiWithTimeout("v1/actuators/dashboard", DASHBOARD_TIMEOUT_MS);
+    const dashboard = await api("v1/actuators/dashboard/start");
+    dashboard._load_elapsed_ms = Math.round(performance.now() - startedAt);
     cachedActuators = dashboard.actuators || [];
     cachedEntities = [];
     renderDashboardStatus(dashboard);
     renderConfiguredActuators();
-    if (budget) budget.textContent = `Bereit in ${Math.round(performance.now() - startedAt)} ms`;
+    if (budget) {
+      const loadMs = dashboard._load_elapsed_ms;
+      budget.textContent = loadMs <= DASHBOARD_TIMEOUT_MS
+        ? `Bereit in ${loadMs} ms`
+        : `Langsam: ${loadMs} ms`;
+    }
   } catch (error) {
     document.getElementById("configured-actuators").innerHTML = `<p class="bad">${escapeHtml(error.message)}</p>`;
     if (budget) budget.textContent = "Startdaten verzögert";
@@ -445,6 +681,60 @@ async function loadOverview() {
       document.getElementById("configured-actuators").innerHTML = "<div class='empty-state'>Startdaten sind gerade nicht verfügbar.</div>";
     }
   }
+  scheduleDashboardExtras();
+}
+
+async function loadSystemOverview() {
+  const startedAt = performance.now();
+  const budget = document.getElementById("load-budget");
+  if (budget) budget.textContent = "Systemübersicht lädt ...";
+  try {
+    const dashboard = await api("v1/actuators/dashboard/system");
+    dashboard._load_elapsed_ms = Math.round(performance.now() - startedAt);
+    cachedActuators = dashboard.actuators || cachedActuators;
+    renderDashboardStatus(dashboard);
+    if (budget) {
+      const loadMs = dashboard._load_elapsed_ms;
+      budget.textContent = loadMs <= DASHBOARD_TIMEOUT_MS
+        ? `Systemübersicht bereit in ${loadMs} ms`
+        : `Systemübersicht langsam: ${loadMs} ms`;
+    }
+    scheduleDashboardExtras();
+  } catch (error) {
+    document.getElementById("status").innerHTML = `<p class="warn">Systemübersicht verzögert: ${escapeHtml(error.message)}</p>`;
+    if (budget) budget.textContent = "Systemübersicht verzögert";
+  }
+}
+
+function scheduleDashboardExtras() {
+  const run = () => {
+    void loadDashboardExtras();
+  };
+  if ("requestIdleCallback" in window) {
+    window.requestIdleCallback(run, {timeout: 1800});
+  } else {
+    setTimeout(run, 250);
+  }
+}
+
+async function loadDashboardExtras() {
+  try {
+    const [jobs, reconciliation] = await Promise.allSettled([
+      apiWithTimeout("v1/actuators/job-queue/state", STATUS_TIMEOUT_MS),
+      apiWithTimeout("v1/actuators/reconciliation/state", STATUS_TIMEOUT_MS),
+    ]);
+    if (jobs.status === "fulfilled") {
+      renderJobQueue(jobs.value.jobs || []);
+    }
+    if (reconciliation.status === "fulfilled") {
+      const text = document.getElementById("reconciliation-status");
+      if (text) {
+        text.textContent = `Letzte automatische Prüfung: ${formatDateTime(reconciliation.value.last_completed_at)}`;
+      }
+    }
+  } catch (_) {
+    // Die Startansicht bleibt auch ohne Hintergrunddaten bedienbar.
+  }
 }
 
 async function loadStatus() {
@@ -465,10 +755,10 @@ async function loadStatus() {
     const hasError = values.some(value => value === null);
     status.innerHTML = hasError
       ? "<p class='warn'>Status teilweise verfügbar. Das Dashboard bleibt bedienbar.</p>"
-      : `<p class="ok">System bereit</p><p>Letzte automatische Prüfung: ${escapeHtml(reconciliationValue.last_completed_at || "noch nie")}</p>`;
+      : `<p class="ok">System bereit</p><p>Letzte automatische Prüfung: ${escapeHtml(formatDateTime(reconciliationValue.last_completed_at))}</p>`;
     chips.innerHTML = [
       `<span class="chip">API: ${escapeHtml(healthValue?.status || "offen")}</span>`,
-      `<span class="chip">WebSocket: ${escapeHtml(websocketValue?.status || "offen")}</span>`,
+      `<span class="chip">WebSocket: ${escapeHtml(translate("connection_status", websocketValue?.status, websocketValue?.status || "offen"))}</span>`,
       `<span class="chip">Lernsystem: ${escapeHtml(mlValue?.status || "offen")}</span>`,
       `<span class="chip">Lernbereite Geräte: ${escapeHtml(reconciliationValue?.trained_models ?? "offen")}</span>`,
     ].join("");
@@ -494,14 +784,27 @@ function renderDashboardStatus(dashboard) {
   ).length;
   const trainedCount = actuators.filter(record => record.behavior_status === "trained").length;
   const sampleTotal = actuators.reduce((sum, record) => sum + Number(record.sample_count || 0), 0);
+  const anomalyTotal = Number(system.anomaly_count || 0);
+  const criticalAnomalyTotal = Number(system.critical_anomaly_count || 0);
+  const loadMs = Number(dashboard._load_elapsed_ms || 0);
+  const jobs = dashboard.jobs?.jobs || [];
+  const runningJobs = jobs.filter(job => job.status === "running").length;
+  const slowJobs = Number(system.slow_job_count || 0);
+  const p95 = system.job_p95_duration_ms == null ? "offen" : `${system.job_p95_duration_ms} ms`;
+  const performanceClass = (
+    loadMs > DASHBOARD_TIMEOUT_MS
+    || slowJobs > 0
+    || system.performance_status === "slow"
+  ) ? "warn" : "ok";
   const cacheLabel = cache.available
     ? `Cache aktuell mit ${cache.entity_count} Entities`
     : "Cache wird nach Discovery aufgebaut";
   status.innerHTML = `
-    <p class="${system.websocket_status === "connected" ? "ok" : "warn"}">
-      Dashboard bereit. WebSocket: ${escapeHtml(system.websocket_status || "unbekannt")}
+    <p class="${performanceClass}">
+      Dashboard bereit in ${escapeHtml(loadMs || "offen")} ms. Budget: ${escapeHtml(system.performance_budget_ms || DASHBOARD_TIMEOUT_MS)} ms.
     </p>
-    <p class="muted">Letzte automatische Prüfung: ${escapeHtml(system.reconciliation_last_completed_at || "noch nicht abgeschlossen")}</p>
+    <p class="${system.websocket_status === "connected" ? "ok" : "warn"}">WebSocket: ${escapeHtml(translate("connection_status", system.websocket_status, system.websocket_status || "unbekannt"))}</p>
+    <p class="muted" id="reconciliation-status">Letzte automatische Prüfung: ${escapeHtml(formatDateTime(system.reconciliation_last_completed_at))}</p>
   `;
   chips.innerHTML = [
     `<span class="chip">API: ${escapeHtml(system.api_status || "ok")}</span>`,
@@ -509,16 +812,44 @@ function renderDashboardStatus(dashboard) {
     `<span class="chip">Aktoren: ${escapeHtml(system.configured_actuators ?? 0)}</span>`,
     `<span class="chip">Lernbereit: ${escapeHtml(system.trained_models ?? 0)}</span>`,
     `<span class="chip">Prüfen: ${escapeHtml(system.review_required ?? 0)}</span>`,
+    `<span class="chip">Jobs aktiv: ${escapeHtml(runningJobs)}</span>`,
+    `<span class="chip">Anomalien: ${escapeHtml(anomalyTotal)}</span>`,
+    `<span class="chip">Kritisch: ${escapeHtml(criticalAnomalyTotal)}</span>`,
   ].join("");
   stats.innerHTML = [
     `<div class="metric"><strong>Geladene Startdaten</strong>${escapeHtml(actuators.length)} Geräte</div>`,
     `<div class="metric"><strong>Freigabebereit</strong>${escapeHtml(readyCount)} Geräte</div>`,
-    `<div class="metric"><strong>Aktiv / Shadow</strong>${escapeHtml(activeCount)} / ${escapeHtml(shadowCount)}</div>`,
+    `<div class="metric"><strong>Aktiv / Prüfmodus</strong>${escapeHtml(activeCount)} / ${escapeHtml(shadowCount)}</div>`,
     `<div class="metric"><strong>Gelernt / Wartet</strong>${escapeHtml(trainedCount)} / ${escapeHtml(pendingCount)}</div>`,
     `<div class="metric"><strong>Gelernte Handlungen</strong>${escapeHtml(sampleTotal)}</div>`,
+    `<div class="metric"><strong>Performance-Budget</strong>${escapeHtml(system.performance_budget_ms || 3000)} ms</div>`,
+    `<div class="metric"><strong>Job p95</strong>${escapeHtml(p95)}</div>`,
+    `<div class="metric"><strong>Langsame Jobs</strong>${escapeHtml(slowJobs)}</div>`,
+    `<div class="metric"><strong>Anomalien</strong>${escapeHtml(anomalyTotal)} offen</div>`,
     `<div class="metric"><strong>Discovery-Gruppen</strong>${escapeHtml(discoveryGroups.length)} Kategorien</div>`,
     `<div class="metric"><strong>Cache-Zeitpunkt</strong>${escapeHtml(cache.updated_at || "noch offen")}</div>`,
   ].join("");
+  renderJobQueue(jobs);
+}
+
+function renderJobQueue(jobs) {
+  const jobsBox = document.getElementById("job-queue");
+  if (!jobsBox) return;
+  jobsBox.innerHTML = jobs.length ? `
+    <h3>Aufgabenliste</h3>
+    ${jobs.slice(-6).reverse().map(job => `
+      <div class="decision-row ${Number(job.duration_ms || 0) >= DASHBOARD_TIMEOUT_MS ? "slow" : ""}">
+        <header>
+          <strong>${escapeHtml(translate("job_kind", job.kind, job.kind))}${job.target ? `: ${escapeHtml(job.target)}` : ""}</strong>
+          <span class="chip">${escapeHtml(translate("job_status", job.status, job.status))}${Number(job.duration_ms || 0) >= DASHBOARD_TIMEOUT_MS ? " · langsam" : ""}</span>
+        </header>
+        <p class="muted">${escapeHtml(job.summary || "Keine Zusammenfassung")}</p>
+        <p class="muted">Start: ${escapeHtml(formatDateTime(job.started_at))} · Dauer: ${escapeHtml(job.duration_ms == null ? "läuft/offen" : `${job.duration_ms} ms`)}</p>
+        ${job.error ? `<p class="bad">${escapeHtml(job.error)}</p>` : ""}
+        ${job.status === "failed" ? "<p class='warn'>Erneut versuchen: Aktion im Dashboard noch einmal starten; der nächste Lauf schreibt einen neuen Eintrag.</p>" : ""}
+      </div>
+    `).join("")}
+  ` : "";
 }
 
 async function loadSummaryData() {
@@ -743,13 +1074,17 @@ function renderConfiguredActuators() {
   const box = document.getElementById("configured-actuators");
   try {
     const rows = cachedActuators || [];
+    const visibleRows = rows.slice(0, visibleActuatorLimit);
     const groups = new Map();
-    for (const record of rows) {
+    for (const record of visibleRows) {
       const group = record.area_name || actuatorGroupLabel(record.domain || record.actuator_entity_id.split(".", 1)[0]);
       if (!groups.has(group)) groups.set(group, []);
       groups.get(group).push({record});
     }
     const groupedRows = [...groups.entries()].sort(([left], [right]) => left.localeCompare(right));
+    const moreButton = rows.length > visibleRows.length
+      ? `<button class="secondary" onclick="visibleActuatorLimit += 24; renderConfiguredActuators()">Weitere ${Math.min(24, rows.length - visibleRows.length)} Geräte anzeigen</button>`
+      : "";
     box.innerHTML = rows.length ? `
       ${groupedRows.map(([group, items]) => `
         <details class="group-panel">
@@ -783,7 +1118,9 @@ function renderConfiguredActuators() {
         `).join("")}
         </div>
         </details>
-      `).join("")}` : "<p>Noch keine Aktoren ausgewählt.</p>";
+      `).join("")}
+      ${moreButton}
+    ` : "<p>Noch keine Aktoren ausgewählt.</p>";
   } catch (error) {
     box.textContent = error.message;
   }
@@ -791,10 +1128,15 @@ function renderConfiguredActuators() {
 
 async function showActuator(actuatorId, evaluationMessage = "") {
   currentActuatorId = actuatorId;
+  for (const section of document.querySelectorAll(".app-view")) {
+    section.classList.toggle("active", section.id === "detail");
+  }
+  document.getElementById("section-jump").value = "detail";
+  localStorage.setItem("sillyhome.ui.view", "detail");
   const box = document.getElementById("actuator-detail");
   renderActuatorDetailShell(actuatorId);
   try {
-    const record = await api(`v1/actuators/${encodeURIComponent(actuatorId)}`);
+    const record = await api(`v1/actuators/${encodeURIComponent(actuatorId)}/detail`);
     contextOptions = [];
     const contexts = [
       record.assignment.selected_numeric_entity_id,
@@ -869,9 +1211,132 @@ async function showActuator(actuatorId, evaluationMessage = "") {
         `).join("")}</ul>`
       : "<p class='muted'>Noch keine Kontext-Entity ausgewählt.</p>";
     const prediction = record.behavior.prediction;
-    const learnedAutomationActions = record.behavior.patterns.filter(
-      pattern => pattern.source === "automation",
-    ).length;
+    const safety = record.behavior.safety || {};
+    const blockers = record.behavior.safety_blockers || [];
+    const decisionFactors = record.behavior.decision_factors || [];
+    const knowledge = record.behavior.knowledge || [];
+    const assumptions = record.behavior.assumptions || [];
+    const uncertainties = record.behavior.uncertainties || [];
+    const snapshots = record.behavior.model_snapshots || [];
+    const activeModelVersion = record.behavior.active_model_version || "";
+    const adaptiveUpdates = record.behavior.adaptive_weight_updates || [];
+    const automationConflicts = record.behavior.automation_conflicts || [];
+    const timeProfiles = record.behavior.time_profiles || [];
+    const anomalies = (record.behavior.anomalies || []).filter(item => !item.resolved);
+    const safetyControls = `
+      <details class="manual-context" open>
+        <summary>Sicherheit und manuelles Gegensteuern</summary>
+        <div class="inline-controls">
+          <div>
+            <label for="safety-stage">Freigabestufe</label>
+            <select id="safety-stage">
+              ${["observe", "suggest", "shadow", "partial", "active"].map(stage => `
+                <option value="${stage}" ${safety.stage === stage ? "selected" : ""}>${escapeHtml(translate("safety_stage", stage))}</option>
+              `).join("")}
+            </select>
+          </div>
+          <div>
+            <label for="safety-confidence">Mindest-Sicherheit in %</label>
+            <input id="safety-confidence" type="number" min="0" max="100" step="1" value="${Math.round((safety.min_confidence ?? 0.82) * 100)}">
+          </div>
+          <div>
+            <label for="safety-cooldown">Cooldown Sekunden</label>
+            <input id="safety-cooldown" type="number" min="0" step="10" value="${safety.cooldown_seconds ?? ""}" placeholder="Standard">
+          </div>
+        </div>
+        <label>
+          <input id="safety-manual-block" type="checkbox" ${safety.manual_block ? "checked" : ""}>
+          Manuelle Sicherheitssperre aktiv
+        </label>
+        ${blockers.length ? `<p class="warn">Aktuelle Blocker: ${blockers.map(escapeHtml).join(" ")}</p>` : "<p class='ok'>Keine lokalen Sicherheitsblocker für die aktuelle Vorhersage.</p>"}
+        <button class="secondary" onclick="saveSafetyProfile('${escapeHtml(record.actuator_entity_id)}')">Sicherheitsprofil speichern</button>
+      </details>
+    `;
+    const decisionArchive = `
+      <details class="manual-context" open>
+        <summary>Entscheidungsakte</summary>
+        <div class="grid-two">
+          <div>
+            <h3>Wissen</h3>
+            <ul>${knowledge.map(item => `<li>${escapeHtml(item)}</li>`).join("") || "<li>Keine gesicherten Punkte gespeichert.</li>"}</ul>
+          </div>
+          <div>
+            <h3>Annahmen</h3>
+            <ul>${assumptions.map(item => `<li>${escapeHtml(item)}</li>`).join("") || "<li>Keine Annahmen gespeichert.</li>"}</ul>
+          </div>
+        </div>
+        <h3>Unsicherheit</h3>
+        <ul>${uncertainties.map(item => `<li>${escapeHtml(item)}</li>`).join("") || "<li>Keine Unsicherheit gespeichert.</li>"}</ul>
+        <h3>Beitragsfaktoren</h3>
+        <div class="decision-list">
+          ${decisionFactors.length ? decisionFactors.map(factor => `
+            <div class="decision-row">
+              <header>
+                <strong>${escapeHtml(factor.label)}</strong>
+                <span class="chip">${Math.round((factor.contribution || 0) * 100)} % Beitrag</span>
+              </header>
+              <p class="muted">${escapeHtml(factor.entity_id || factor.factor_type)} · Zustand: ${escapeHtml(factor.state || "offen")} · Gewicht: ${Math.round((factor.weight || 0) * 100)} %</p>
+              <p>${(factor.evidence || []).map(escapeHtml).join(" ")}</p>
+            </div>
+          `).join("") : "<p class='muted'>Noch keine aktuelle Entscheidungsfaktoren berechnet.</p>"}
+        </div>
+      </details>
+    `;
+    const adaptivePanel = `
+      <details class="manual-context">
+        <summary>v1.2 Lernen, Rollback und Konflikte</summary>
+        <h3>Zeitprofile</h3>
+        <div class="metric-grid">
+          ${timeProfiles.length ? timeProfiles.map(profile => `
+            <div class="metric">
+              <strong>${escapeHtml(profile.label)}</strong>
+              ${escapeHtml(profile.sample_count)} Beispiele · ${escapeHtml(profile.dominant_state || "offen")}
+              <p class="muted">${Math.round((profile.confidence || 0) * 100)} % Profilklarheit</p>
+            </div>
+          `).join("") : "<div class='metric'><strong>Zeitprofile</strong>Noch keine Daten</div>"}
+        </div>
+        <h3>Modell-Snapshots</h3>
+        <div class="decision-list">
+          ${snapshots.length ? snapshots.slice(-5).reverse().map(snapshot => `
+            <div class="decision-row">
+              <header>
+                <strong>${escapeHtml(snapshot.version_id)}</strong>
+                <span class="chip">${snapshot.version_id === activeModelVersion ? "aktiv" : "Rollback möglich"}</span>
+              </header>
+              <p class="muted">${escapeHtml(snapshot.sample_count)} Beispiele · ${escapeHtml(snapshot.high_confidence_sample_count)} eindeutig · Ø ${Math.round((snapshot.average_confidence || 0) * 100)} %</p>
+              <p>${escapeHtml(snapshot.reason || "Kein Kommentar")}</p>
+              ${snapshot.version_id !== activeModelVersion ? `<button class="secondary compact" onclick="rollbackModel('${escapeHtml(record.actuator_entity_id)}', '${escapeHtml(snapshot.version_id)}')">Rollback</button>` : ""}
+            </div>
+          `).join("") : "<p class='muted'>Noch kein Modell-Snapshot gespeichert.</p>"}
+        </div>
+        <h3>Automatische Gewichtsanpassungen</h3>
+        <ul>${adaptiveUpdates.length ? adaptiveUpdates.slice(-8).reverse().map(update => `
+          <li><code>${escapeHtml(update.entity_id)}</code>: ${Math.round(update.previous_weight * 100)} % → ${Math.round(update.new_weight * 100)} %. ${escapeHtml(update.reason)}</li>
+        `).join("") : "<li>Noch keine automatische Gewichtsanpassung.</li>"}</ul>
+        <h3>Automation-Konflikte</h3>
+        <ul>${automationConflicts.length ? automationConflicts.map(conflict => `
+          <li><code>${escapeHtml(conflict.automation_entity_id)}</code>: <span class="${conflict.severity === "warning" ? "warn" : "muted"}">${escapeHtml(translate("severity", conflict.severity, conflict.status))}</span> ${escapeHtml(conflict.reason)}</li>
+        `).join("") : "<li>Keine aktiven Automation-Konflikte erkannt.</li>"}</ul>
+      </details>
+    `;
+    const anomalyPanel = `
+      <details class="manual-context" ${anomalies.length ? "open" : ""}>
+        <summary>v1.3 Anomalie- und Performance-Hinweise</summary>
+        <div class="decision-list">
+          ${anomalies.length ? anomalies.map(anomaly => `
+            <div class="decision-row ${anomaly.severity === "critical" ? "critical" : ""}">
+              <header>
+                <strong>${escapeHtml(anomaly.title)}</strong>
+                <span class="chip">${escapeHtml(translate("severity", anomaly.severity))} · ${escapeHtml(translate("anomaly_category", anomaly.category, anomaly.category))}</span>
+              </header>
+              <p>${escapeHtml(anomaly.detail)}</p>
+              <p class="muted">Erkannt: ${escapeHtml(formatDateTime(anomaly.detected_at))}</p>
+            </div>
+          `).join("") : "<p class='ok'>Keine offenen Anomalien fuer diesen Aktor.</p>"}
+        </div>
+      </details>
+    `;
+    const learnedAutomationActions = "wird bei Bedarf im Training ausgewertet";
     const relatedAutomations = record.behavior.related_automations || [];
     const manualContextIds = new Set(record.assignment.selected_context_entity_ids || []);
     const numericOptions = contextOptions.filter(entity => entity.domain === "sensor");
@@ -960,8 +1425,8 @@ async function showActuator(actuatorId, evaluationMessage = "") {
           <p><strong>Betriebsart:</strong> ${escapeHtml(behaviorLabel(record))}</p>
           <p><strong>Gelernte Handlungen:</strong> ${record.behavior.sample_count}</p>
           <p><strong>Davon eindeutig geregelt:</strong> ${record.behavior.high_confidence_sample_count}</p>
-          <p><strong>Davon erkannte HA-Automationen:</strong> ${learnedAutomationActions}</p>
-          <p><strong>Letztes Training:</strong> ${escapeHtml(record.behavior.last_trained_at || "noch nicht")}</p>
+          <p><strong>Erkannte HA-Automationen:</strong> ${escapeHtml(learnedAutomationActions)}</p>
+          <p><strong>Letztes Training:</strong> ${escapeHtml(formatDateTime(record.behavior.last_trained_at))}</p>
           <p><strong>Was noch passiert:</strong> ${escapeHtml(record.behavior.reason)}</p>
           <p><strong>Freigabestatus:</strong> <span class="${record.behavior.activation_ready ? "ok" : "warn"}">${escapeHtml(record.behavior.activation_reason)}</span></p>
           <div class="actions">${activationButton}</div>
@@ -978,6 +1443,10 @@ async function showActuator(actuatorId, evaluationMessage = "") {
         <button class="secondary" onclick="sendFeedback('${escapeHtml(record.actuator_entity_id)}', true)">Vorhersage korrekt</button>
         <button class="secondary" onclick="sendFeedback('${escapeHtml(record.actuator_entity_id)}', false)">Vorhersage falsch</button>
       </div>
+      ${safetyControls}
+      ${decisionArchive}
+      ${adaptivePanel}
+      ${anomalyPanel}
       <h3>Passende Home-Assistant-Automationen</h3>
       <p class="muted">Bei einer Übernahme pausiert SillyHome diese Automationen. Beim Stoppen können sie gezielt fortgesetzt werden.</p>
       <button class="secondary compact" onclick="refreshRelatedAutomations('${escapeHtml(record.actuator_entity_id)}')">Automationen neu suchen</button>
@@ -992,7 +1461,6 @@ async function showActuator(actuatorId, evaluationMessage = "") {
       ${currentContextControls}
       ${manualAssignment}
     `;
-    void hydrateContextOptions(record);
     document.getElementById("detail").scrollIntoView({behavior: "smooth", block: "start"});
   } catch (error) {
     box.textContent = error.message;
@@ -1180,6 +1648,51 @@ async function sendFeedback(actuatorId, correct) {
   }
 }
 
+async function saveSafetyProfile(actuatorId) {
+  const confidence = Number(document.getElementById("safety-confidence")?.value || 82);
+  const cooldownRaw = document.getElementById("safety-cooldown")?.value || "";
+  const cooldown = cooldownRaw === "" ? null : Math.max(0, Number(cooldownRaw));
+  const profile = {
+    stage: document.getElementById("safety-stage")?.value || "shadow",
+    manual_block: Boolean(document.getElementById("safety-manual-block")?.checked),
+    min_confidence: Math.max(0, Math.min(100, Number.isFinite(confidence) ? confidence : 82)) / 100,
+    cooldown_seconds: Number.isFinite(cooldown) ? cooldown : null,
+    rules: [
+      {rule_id: "activation_ready", label: "Nur nach Lernfreigabe aktiv schalten", enabled: true, blocking: true, reason: "Der Aktor muss genug eindeutiges Verhalten gelernt haben."},
+      {rule_id: "confidence_threshold", label: "Mindest-Sicherheit einhalten", enabled: true, blocking: true, reason: "Vorhersagen unter der Schaltschwelle bleiben im Prüfmodus."},
+      {rule_id: "cooldown", label: "Sicherheits-Cooldown gegen Hin-und-her-Schalten", enabled: true, blocking: true, reason: "Gleiche Zielzustände werden nicht zu schnell wiederholt."},
+      {rule_id: "manual_block", label: "Manuelle Sperre respektieren", enabled: true, blocking: true, reason: "Nutzer können jeden Aktor sofort blockieren."},
+    ],
+    note: "Sicherheitsprofil im Dashboard gespeichert",
+  };
+  try {
+    await api(`v1/actuators/${encodeURIComponent(actuatorId)}/safety`, {
+      method: "POST",
+      body: JSON.stringify({safety: profile}),
+    });
+    invalidateDashboardCache();
+    await loadConfiguredActuators();
+    await showActuator(actuatorId, "Sicherheitsprofil gespeichert.");
+  } catch (error) {
+    alert(error.message);
+  }
+}
+
+async function rollbackModel(actuatorId, versionId) {
+  if (!confirm(`${actuatorId}: wirklich auf Modell ${versionId} zurückrollen?`)) return;
+  try {
+    await api(`v1/actuators/${encodeURIComponent(actuatorId)}/model/rollback`, {
+      method: "POST",
+      body: JSON.stringify({version_id: versionId}),
+    });
+    invalidateDashboardCache();
+    await loadConfiguredActuators();
+    await showActuator(actuatorId, `Rollback auf ${versionId} ausgeführt.`);
+  } catch (error) {
+    alert(error.message);
+  }
+}
+
 async function setActivation(actuatorId, active, pauseMatchingAutomations, restorePausedAutomations) {
   const question = active
     ? pauseMatchingAutomations
@@ -1252,11 +1765,13 @@ async function removeActuator(actuatorId) {
 
 async function startDashboard() {
   document.getElementById("status").innerHTML = "<p class='muted'>Status lädt nach ...</p>";
-  document.getElementById("configured-actuators").innerHTML = "<div class='empty-state'>Geräte werden nach dem Status geladen.</div>";
+  document.getElementById("configured-actuators").innerHTML = "<div class='empty-state'>Öffne „Lernen“, um Geräte zu laden.</div>";
   document.getElementById("actuator-detail").innerHTML = "<div class='empty-state'>Wähle später ein Gerät aus der Übersicht.</div>";
+  syncSettingsView();
+  document.getElementById("section-jump").value = "status-section";
+  showView("status-section");
   await new Promise(resolve => requestAnimationFrame(resolve));
-  await loadStatus();
-  await loadOverview();
+  setTimeout(() => void loadStatus(), 100);
 }
 
 void startDashboard();

docs/V1_0_0_OPERATING_GUIDE.md

@@ -101,6 +101,21 @@ wget -qO /tmp/summary.json http://58adbe1e-sillyhome-next:8000/v1/actuators/summ
 wget -qO /tmp/dashboard.json http://58adbe1e-sillyhome-next:8000/v1/actuators/dashboard
 ```
 
+Wenn der Add-on-Container aus dem Agent-Host nicht direkt routbar ist, gilt der
+Home-Assistant-Supervisor als Verifikationsquelle:
+
+- Add-on-Info pruefen: Version, `version_latest`, `update_available`, `state`,
+  `boot` und `watchdog`.
+- Vor Updates eine Home-Assistant-Teil-Sicherung fuer **SillyHome Next**
+  erstellen.
+- Nach einem Store-Reload und Update muss `version == version_latest`,
+  `update_available == false`, `state == started`, `boot == auto` und
+  `watchdog == true` gelten.
+- Den HA-/Ingress-Tab nach jedem Update hart neu laden, weil Home Assistant
+  sonst alte HTML-/JavaScript-Ressourcen aus dem bestehenden Tab verwenden kann.
+- Rollback erfolgt ueber die vorherige Add-on-Teil-Sicherung oder den letzten
+  Git-Tag; beide Referenzen im Release-/Abnahmeprotokoll notieren.
+
 ## Rollback
 
 Der stabile Vor-1.0-Stand ist `v0.7.21`. Vor dem 1.0.0-Umbau wurde ein

docs/V1_0_ACCEPTANCE.md

@@ -44,6 +44,12 @@ expliziter Freigabe.
   - `ruff check .`
   - `mypy app backend tests`
   - `git diff --check`
+- Performance-Budget:
+  - Automatisierter Test prueft Root-HTML und `/v1/actuators/dashboard` gegen
+    das 5-Sekunden-Budget mit kontrollierten Fake-HA-/Cache-Daten.
+- HA-/Ingress-Verifikation:
+  - Supervisor-Update, Add-on-Status, Watchdog, Backup, Ingress-Hard-Reload
+    und Rollback sind im Operating Guide dokumentiert.
 
 ## Teilweise Erfuellt
 
@@ -62,14 +68,10 @@ expliziter Freigabe.
 
 ## Offen Fuer v1.0.x
 
-- Echte Dashboard-Performance-Budget-Tests, die Start-HTML und
-  `/v1/actuators/dashboard` gegen ein 5-Sekunden-Limit messen.
 - Dashboard-Jobstatus fuer Reconciliation, Training, Discovery und
   Automation-Refresh.
 - Mehr Entscheidungsstatistik pro Aktor: welche Sensoren wie stark
   beigetragen haben, wie sich Confidence und Sample Count entwickeln.
-- Dokumentierte HA-Installationspruefung mit Supervisor-/Ingress-Hinweisen,
-  weil direkte Container-HTTP-Pruefung ausserhalb HA nicht immer routbar ist.
 
 ## Rollback
 

docs/V1_1_0_OPERATING_GUIDE.md

@@ -0,0 +1,72 @@
+# SillyHome Next v1.1.0 Operating Guide
+
+## Ziel
+
+v1.1.0 macht das Dashboard zur Zentrale fuer Visualisierung, Einrichtung,
+Sicherheit und manuelles Gegensteuern. Autonomes Schalten bleibt ein kurzer
+lokaler Pfad: Vorhersage und Safety-Profil werden aus bereits vorhandenen Daten
+bewertet, danach folgt direkt der Home-Assistant-Serviceaufruf.
+
+## Sicherheitsmodell
+
+Jeder Aktor hat ein Safety-Profil:
+
+- `stage`: Beobachten, Vorschlagen, Shadow, Teilaktiv oder Aktiv.
+- `manual_block`: harte manuelle Sperre.
+- `min_confidence`: Mindest-Sicherheit fuer autonomes Schalten.
+- `cooldown_seconds`: optionaler Aktor-Cooldown gegen schnelles Hin-und-her.
+- Safety-Regeln: Freigabe, Confidence, Cooldown und manuelle Sperre.
+
+Ein Aktor schaltet nur, wenn alle lokalen Safety-Regeln frei sind, der
+Behavior-Modus aktiv ist, die Freigabe bereit ist, die Confidence passt, der
+Zielzustand noch nicht erreicht ist und der Cooldown abgelaufen ist.
+
+## Transparenz
+
+Die Aktor-Detailansicht trennt:
+
+- Wissen: belegte Fakten aus Historie, Zuordnung und Automationen.
+- Annahmen: heuristische Schluesse wie Zeit-/Kontext-Aehnlichkeit.
+- Unsicherheiten: geringe Datenmenge, unklare Quellen, Review-Bedarf oder
+  negatives Feedback.
+- Beitragsfaktoren: Sensoren, Kontextsignale, aktive Gewichtung und Beitrag.
+- Safety-Blocker: Gruende, warum nicht geschaltet wird.
+
+## Job-Queue
+
+Das Dashboard zeigt die letzten Jobs mit Status, Dauer, Fehler und
+Zusammenfassung. Sichtbar sind:
+
+- Discovery
+- Reconciliation
+- Training
+- Evaluation
+- Automation-Refresh
+
+Die Queue ist persistent in `job_queue.json` und dient als Betriebsanzeige. Sie
+blockiert nicht den Startpfad und nicht den Schaltpfad.
+
+## Manuelles Gegensteuern
+
+Im Dashboard koennen pro Aktor gesetzt werden:
+
+- manuelle Sicherheitssperre
+- Freigabestufe
+- Mindest-Confidence
+- optionaler Cooldown
+- Sensor-Gewichtungen und Gruppen-Gewichtungen
+- Kontextauswahl
+- Feedback: Vorhersage korrekt/falsch
+- HA-Automationen pausieren/fortsetzen
+
+## Qualitaetspruefung
+
+Vor Release:
+
+```bash
+.venv/bin/pytest -q
+.venv/bin/ruff check .
+.venv/bin/mypy app backend tests
+git diff --check
+node --check /tmp/sillyhome-dashboard.js
+```

docs/V1_2_0_OPERATING_GUIDE.md

@@ -0,0 +1,62 @@
+# SillyHome Next v1.2.0 Operating Guide
+
+## Ziel
+
+v1.2.0 erweitert die sichere v1.1-Grundlage um adaptive Lernfunktionen. Diese
+Funktionen laufen bei Feedback, Training oder Automation-Refresh und blockieren
+nicht den direkten Schaltpfad.
+
+## Adaptive Gewichtung
+
+Feedback passt die Gewichtung aktuell beteiligter Kontextsignale vorsichtig an:
+
+- korrektes Feedback: +3 Prozentpunkte bis maximal 100 %
+- falsches Feedback: -8 Prozentpunkte bis minimal 10 %
+
+Die Aenderungen werden als `adaptive_weight_updates` gespeichert und im
+Dashboard angezeigt. Manuelle Gewichtungen bleiben weiter direkt korrigierbar.
+
+## Modell-Snapshots und Rollback
+
+Bei jedem Training wird ein Snapshot gespeichert:
+
+- Version-ID
+- Sample Count
+- eindeutig zugeordnete Handlungen
+- durchschnittliche Confidence
+- negative Feedbacks
+- Musterliste
+- Begruendung
+
+Ueber das Dashboard kann auf einen frueheren Snapshot zurueckgerollt werden.
+
+## Automation-Konflikte
+
+Beim Automation-Refresh markiert SillyHome Konflikte, wenn:
+
+- SillyHome fuer einen Aktor aktiv ist
+- eine passende Home-Assistant-Automation ebenfalls aktiv bleibt
+
+Pausierte Automationen werden als kontrolliert markiert.
+
+## Zeitprofile
+
+SillyHome bildet Profile fuer:
+
+- Nacht
+- Morgen
+- Tag
+- Abend
+- Wochenende
+
+Diese Profile zeigen Sample Count, dominanten Zielzustand und Profilklarheit.
+
+## Performance-Grenze
+
+v1.2-Funktionen duerfen den Schaltmoment nicht verlangsamen. Der direkte
+Schaltpfad bleibt:
+
+1. vorhandene aktuelle States nutzen
+2. lokale Safety-Pruefung
+3. direkter Home-Assistant-Serviceaufruf
+4. Persistenz der Entscheidung

docs/V1_3_0_OPERATING_GUIDE.md

@@ -0,0 +1,68 @@
+# SillyHome Next v1.3.0 Operating Guide
+
+v1.3.0 ergänzt die v1.2-Lernfunktionen um Anomalie-Erkennung und
+Performance-Überwachung. Das Dashboard bleibt Visualisierung und Einrichtung;
+der direkte Schaltpfad bleibt kurz und führt vor dem Home-Assistant-Service-Call
+keine Discovery, kein Training und keine Modellanalyse aus.
+
+## Performance-Budget
+
+- Dashboard-Start und `/v1/actuators/dashboard` haben ein Budget von 3000 ms.
+- Das Dashboard zeigt die eigene Ladezeit, das aktive Budget, Job-p95 und die
+  Anzahl langsamer Jobs.
+- Jobs ab 3000 ms werden in der Job-Queue als langsam markiert.
+- Der automatisierte API-Test prüft den Root- und Dashboard-Startpfad gegen das
+  3-Sekunden-Budget.
+
+## Anomalie-Erkennung
+
+Anomalien werden pro Aktor gespeichert und im Aktor-Detail angezeigt. Erkannt
+werden aktuell:
+
+- fehlender Sensor-/Kontextbezug
+- zu wenige Lernbeispiele
+- unklare Quellen historischer Schaltungen
+- veraltetes Training
+- Vorhersagen unter der Sicherheitsgrenze
+- aktive manuelle Sicherheitssperren
+- Safety-Blocker
+- parallele HA-Automationen bei aktivem SillyHome
+- hohe negative Feedbackquote
+
+Die Anomalien sind Hinweise für Setup und manuelles Gegensteuern. Sie lösen
+keine automatische Eskalation und keine langsamere Schaltung aus.
+
+## API
+
+- `GET /v1/actuators/dashboard` liefert jetzt zusätzlich:
+  - `performance_budget_ms`
+  - `job_p95_duration_ms`
+  - `slow_job_count`
+  - `performance_status`
+  - `anomaly_count`
+  - `critical_anomaly_count`
+- `GET /v1/actuators/anomalies` liefert offene Anomalien gruppiert nach Aktor.
+
+## Betrieb
+
+Bei Ladezeiten ab 3 Sekunden gilt die Seite als nicht performant. Dann zuerst
+prüfen:
+
+1. Dashboard-Statistik: Ladezeit, Job-p95, langsame Jobs.
+2. Job-Queue: welche Aktion langsam war.
+3. Aktor-Detail: Anomalien, Safety-Blocker und Automation-Konflikte.
+4. Falls Discovery oder Training langsam war: nicht in den Startpfad ziehen,
+   sondern geplant, manuell oder über Queue laufen lassen.
+
+## Qualität
+
+Vor Release/Installation ausführen:
+
+```bash
+pytest -q
+ruff check .
+mypy app backend tests
+git diff --check
+```
+
+Zusätzlich das eingebettete Dashboard-JavaScript mit `node --check` prüfen.

docs/V1_4_0_OPERATING_GUIDE.md

@@ -0,0 +1,42 @@
+# SillyHome Next v1.4.0 Operating Guide
+
+v1.4.0 überarbeitet das Dashboard für mobile Nutzung, deutsche Verständlichkeit
+und stabileren Datenabruf.
+
+## Schneller Startpfad
+
+- Die Startseite lädt zuerst nur die Bedienoberfläche und den kompakten
+  Dashboard-Startdatensatz.
+- Neuer Start-Endpunkt: `GET /v1/actuators/dashboard/start`.
+- Der Start-Endpunkt liefert keine Discovery-Gruppen und keine Aufgabenliste.
+- Status, Aufgabenliste, Reconciliation-Zeitpunkt und Detail-Kontext werden
+  danach im Hintergrund geladen.
+- Auf der Startansicht werden zunächst nur die ersten 24 Aktoren gerendert.
+  Weitere Geräte werden auf Knopfdruck nachgerendert.
+
+## Deutsche Oberfläche
+
+Interne Protokollwerte bleiben stabil, werden in der Oberfläche aber übersetzt:
+
+- `observe` -> `Nur beobachten`
+- `suggest` -> `Vorschläge anzeigen`
+- `shadow` -> `Prüfmodus ohne Schalten`
+- `partial` -> `Teilfreigabe`
+- `active` -> `Aktiv freigegeben`
+- Job-Status wie `running`, `completed`, `failed` erscheinen als `läuft`,
+  `abgeschlossen`, `fehlgeschlagen`.
+- Anomalie-Schweregrade erscheinen als `Hinweis`, `Warnung`, `Kritisch`.
+
+## Stabilität
+
+- Startdaten und Statusdaten sind getrennt. Ein langsamer Statuscheck blockiert
+  nicht mehr die Geräteübersicht.
+- Die Aufgabenliste wird separat geladen und kann ausfallen, ohne die
+  Bedienoberfläche zu blockieren.
+- Detaildaten bleiben gestuft: zuerst Shell und gespeicherte Werte, danach
+  Kontextvorschläge.
+
+## Performance-Regel
+
+3 Sekunden bleiben die harte Grenze für den Startpfad. Alles, was schwerer ist
+als Startdaten, muss nachgelagert oder auf Nutzeraktion geladen werden.

docs/V1_5_0_OPERATING_GUIDE.md

@@ -0,0 +1,47 @@
+# SillyHome Next v1.5.0 Operating Guide
+
+v1.5.0 trennt Dashboard-Ansichten, Datenabruf und Detaildaten weiter auf. Ziel
+ist, dass die Seite auf mobiler Datenverbindung schneller nutzbar wird und keine
+schweren Lern-, Discovery- oder Detaildaten beim Start lädt.
+
+## Menüstruktur
+
+- Startseite / System: Systemübersicht, Cache, Performance, Status.
+- Lernen: konfigurierte Aktoren und Lernstand.
+- Details: genau ein ausgewählter Aktor.
+- Discovery & Einrichtung: Geräteliste, Vorschläge und neue Aktoren.
+- Einstellungen: Sprache und Standardverhalten.
+- Ablauf: Bedienhinweise.
+
+Beim Öffnen der Seite wird immer nur die Startseite geladen. Andere Ansichten
+laden erst beim Öffnen.
+
+## Kompakte Detaildaten
+
+Neuer Endpunkt:
+
+```text
+GET /v1/actuators/{actuator_entity_id}/detail
+```
+
+Dieser Endpunkt entfernt große Musterlisten und Snapshot-Muster aus dem ersten
+Detailabruf. Geladen werden nur die Werte, die für die erste Detailansicht
+benötigt werden. Kontextvorschläge bleiben ein separater Abruf und laufen erst
+auf Nutzeraktion.
+
+## Sprache
+
+Die Sprache kann unter `Einstellungen` gewählt werden. Deutsch ist Standard.
+Technische API-Werte bleiben stabil, werden aber im Dashboard über die
+Sprachschicht angezeigt.
+
+## Performance-Regeln
+
+- Kein Discovery beim Start.
+- Keine Aufgabenliste beim Start.
+- Keine Kontextvorschläge beim Öffnen eines Aktors.
+- Keine Musterlisten im ersten Detailabruf.
+- Geräteübersicht rendert begrenzt und lädt weitere Karten per Button nach.
+
+Die Angabe „bereit in X ms“ beschreibt nur den jeweiligen API-/Ansichtsabruf.
+Sie ist nicht gleichzusetzen mit der kompletten HA/Ingress-Navigationszeit.

docs/V1_5_1_OPERATING_GUIDE.md

@@ -0,0 +1,32 @@
+# SillyHome Next v1.5.1 Operating Guide
+
+v1.5.1 ist ein Stabilisierungshotfix für die nach v1.2.0 entstandenen
+Dashboard-Änderungen. Fachlich gehört diese Arbeit zur v1.2.x-Patchlinie; die
+höhere technische Versionsnummer ist nur nötig, weil Home Assistant bereits
+v1.5.0 installiert hat und Add-on-Updates monoton nach oben laufen.
+
+## Korrekturen
+
+- Die System-Startseite nutzt `GET /v1/actuators/dashboard/system` und lädt
+  keine Aktorenliste.
+- Sichtbare 3-Sekunden-Abbrüche mit Browsertexten wie `signal is aborted
+  without reason` wurden entfernt.
+- Startdaten und Detaildaten werden ohne künstlichen Frontend-Abbruch geladen.
+- Timeout-Meldungen werden deutsch und verständlich angezeigt, wenn sie bei
+  Nebenprüfungen auftreten.
+- `summary`-Zeilen wie `anzeigenaufklappen` haben jetzt Abstand und Layout.
+
+## Ladeverhalten
+
+- Statische Seite wird sofort gerendert.
+- Systemdaten laden im Hintergrund.
+- Lernen/Geräte laden nur im Menü `Lernen`.
+- Discovery lädt nur im Menü `Discovery & Einrichtung`.
+- Aktorwerte laden erst beim Öffnen der Detailansicht.
+- Kontextvorschläge laden erst auf Nutzeraktion.
+
+## Hinweis zur Performance-Anzeige
+
+Die App zeigt keine echte HA/Ingress-Navigationszeit an. Gemessen werden nur
+einzelne interne Abrufe nach Start der Seite. Aussagen zur gesamten Ladezeit
+müssen über Browser/Ingress oder HA-Messung geprüft werden.

docs/V1_5_2_OPERATING_GUIDE.md

@@ -0,0 +1,32 @@
+# SillyHome Next v1.5.2 Operating Guide
+
+v1.5.2 begrenzt den Rollback-Speicher und entschärft Home-Assistant-Timeouts,
+die in den Add-on-Logs sichtbar wurden.
+
+## Rollback-Speicher
+
+- Pro Aktor bleiben maximal 3 Modell-Snapshots erhalten.
+- Pro Snapshot bleiben maximal 120 Muster erhalten.
+- Beim Speichern eines Aktors werden ältere oder zu große Snapshots automatisch
+  gekappt.
+- Der kompakte Detail-Endpunkt liefert ebenfalls maximal 3 Rollback-Snapshots
+  und keine Musterlisten.
+
+Damit bleibt Rollback nutzbar, ohne dass die JSON-Dateien mit alten Modellen
+stark wachsen.
+
+## Home-Assistant-Zugriffe
+
+- REST-Zugriffe auf Home Assistant haben jetzt standardmäßig 25 Sekunden
+  Timeout statt 10 Sekunden.
+- Der Wert ist über `SILLYHOME_HA_TIMEOUT_SECONDS` konfigurierbar.
+- WebSocket-Keepalive wurde auf 30 Sekunden Ping-Intervall und 30 Sekunden
+  Ping-Timeout entschärft.
+
+## Log-Einordnung
+
+- `GET ... HTTP/1.1` ist bei Uvicorn/HA-Ingress normal und kein Fehler.
+- `Zeitüberschreitung beim Zugriff auf Home Assistant` bedeutet, dass HA selbst
+  zu langsam geantwortet hat oder der Ingress/Netzpfad verzögert war.
+- `keepalive ping timeout` bedeutet, dass die HA-WebSocket-Verbindung nicht
+  rechtzeitig geantwortet hat. SillyHome reconnectet automatisch.

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "sillyhome-next"
-version = "1.0.4"
+version = "1.5.2"
 description = "Lokales Smart-Home-Intelligenzsystem für Home Assistant"
 requires-python = ">=3.11"
 dependencies = [

tests/api/test_actuators.py

@@ -1,11 +1,13 @@
 from __future__ import annotations
 
-from datetime import datetime, timedelta
+from time import perf_counter
+from datetime import datetime, timedelta, timezone
 from pathlib import Path
 
 from fastapi.testclient import TestClient
 
 from app.actuators.lifecycle import ActuatorReconciliationService
+from app.actuators.models import JobStatus, ModelSnapshot
 from app.actuators.store import ActuatorStore
 from app.behavior.engine import BehaviorEngine
 from app.config import Settings
@@ -29,6 +31,7 @@ class FakeHaReader(HaReader):
         self._entities = entities
         self._history = history
         self.read_entities_calls = 0
+        self.service_calls: list[tuple[str, str, dict[str, object]]] = []
 
     def read_entities(self) -> list[HaEntitySummary]:
         self.read_entities_calls += 1
@@ -85,6 +88,7 @@ class FakeHaReader(HaReader):
         service: str,
         service_data: dict[str, object],
     ) -> list[object]:
+        self.service_calls.append((domain, service, service_data))
         return []
 
     def find_automations_for_entity(
@@ -110,6 +114,7 @@ def _install_service(tmp_path: Path) -> None:
             unit_of_measurement="lx",
             friendly_name="Abstellkammer Helligkeit",
             area_name="Abstellkammer",
+            state="12",
         ),
         HaEntitySummary(
             entity_id="binary_sensor.abstellkammer_motion",
@@ -117,6 +122,7 @@ def _install_service(tmp_path: Path) -> None:
             device_class="motion",
             friendly_name="Abstellkammer Bewegung",
             area_name="Abstellkammer",
+            state="off",
         ),
         HaEntitySummary(
             entity_id="sensor.pfsense_interface_vpn_inbytes",
@@ -263,6 +269,88 @@ def test_weight_override_endpoint_updates_sensor_relevance(tmp_path: Path) -> No
     assert numeric["sensor.abstellkammer_illuminance"]["effective_weight"] == 0.75
 
 
+def test_safety_profile_can_block_actuator_manually(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.post(
+            "/v1/actuators/light.abstellkammer/safety",
+            json={
+                "safety": {
+                    "stage": "shadow",
+                    "manual_block": True,
+                    "min_confidence": 0.9,
+                    "cooldown_seconds": 120,
+                    "rules": [
+                        {
+                            "rule_id": "manual_block",
+                            "label": "Manuelle Sperre respektieren",
+                            "enabled": True,
+                            "blocking": True,
+                            "reason": "Test",
+                        }
+                    ],
+                    "note": "Test",
+                }
+            },
+        )
+
+    assert response.status_code == 200
+    payload = response.json()
+    assert payload["behavior"]["safety"]["manual_block"] is True
+    assert payload["behavior"]["safety"]["min_confidence"] == 0.9
+    assert payload["behavior"]["safety"]["cooldown_seconds"] == 120
+
+
+def test_feedback_adapts_sensor_weights_and_model_can_rollback(tmp_path: Path) -> None:
+    with TestClient(app) as client:
+        _install_service(tmp_path)
+        client.post(
+            "/v1/actuators",
+            json={"actuator_entity_id": "light.abstellkammer"},
+        )
+        record = app.state.actuator_store.get("light.abstellkammer")
+        version_id = "model-test"
+        snapshot = ModelSnapshot(
+            version_id=version_id,
+            sample_count=1,
+            high_confidence_sample_count=1,
+            average_confidence=0.9,
+            patterns=[],
+            reason="Test-Snapshot",
+        )
+        app.state.actuator_store.upsert(
+            record.model_copy(
+                update={
+                    "behavior": record.behavior.model_copy(
+                        update={
+                            "model_snapshots": [snapshot],
+                            "active_model_version": "model-current",
+                            "sample_count": 2,
+                        }
+                    )
+                }
+            )
+        )
+
+        feedback = client.post(
+            "/v1/actuators/light.abstellkammer/feedback",
+            json={"correct": False, "expected_state": "off"},
+        )
+        rollback = client.post(
+            "/v1/actuators/light.abstellkammer/model/rollback",
+            json={"version_id": version_id},
+        )
+
+    assert feedback.status_code == 200
+    feedback_payload = feedback.json()
+    assert feedback_payload["behavior"]["adaptive_weight_updates"]
+    assert feedback_payload["manual_override"]["sensor_weights"]
+    assert rollback.status_code == 200
+    assert rollback.json()["behavior"]["active_model_version"] == version_id
+
+
 def test_summary_is_lightweight_and_uses_cached_entity_metadata(tmp_path: Path) -> None:
     with TestClient(app) as client:
         _install_service(tmp_path)
@@ -297,6 +385,101 @@ def test_dashboard_overview_uses_cache_without_ha_roundtrip(tmp_path: Path) -> N
     assert payload["cache"]["entity_count"] == 4
     assert payload["actuators"][0]["friendly_name"] == "Abstellkammer Licht"
     assert payload["discovery_groups"]
+    assert payload["jobs"]["jobs"][-1]["kind"] == "discovery"
+
+
+def test_reconciliation_run_records_visible_job_queue(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.post("/v1/actuators/reconciliation/run")
+        jobs = client.get("/v1/actuators/job-queue/state")
+
+    assert response.status_code == 200
+    assert jobs.status_code == 200
+    payload = jobs.json()
+    assert [job["kind"] for job in payload["jobs"][-3:]] == [
+        "reconciliation",
+        "training",
+        "evaluation",
+    ]
+    assert payload["jobs"][-1]["status"] == "completed"
+
+
+def test_dashboard_start_path_stays_within_three_second_budget(tmp_path: Path) -> None:
+    with TestClient(app) as client:
+        _install_service(tmp_path)
+        client.get("/v1/actuators/discovery")
+        client.post("/v1/actuators", json={"actuator_entity_id": "light.abstellkammer"})
+
+        root_started_at = perf_counter()
+        root_response = client.get("/")
+        root_elapsed = perf_counter() - root_started_at
+
+        dashboard_started_at = perf_counter()
+        dashboard_response = client.get("/v1/actuators/dashboard/start")
+        dashboard_elapsed = perf_counter() - dashboard_started_at
+
+    assert root_response.status_code == 200
+    assert dashboard_response.status_code == 200
+    assert root_elapsed < 3.0
+    assert dashboard_elapsed < 3.0
+
+
+def test_dashboard_reports_performance_budget_and_anomalies(tmp_path: Path) -> None:
+    with TestClient(app) as client:
+        _install_service(tmp_path)
+        client.get("/v1/actuators/discovery")
+        client.post("/v1/actuators", json={"actuator_entity_id": "light.abstellkammer"})
+        store = app.state.actuator_store
+        job = store.start_job(kind="training", trigger="test", summary="Langsamer Testjob")
+        queue = store.load_job_queue()
+        queue.jobs = [
+            item.model_copy(update={"started_at": datetime.now(timezone.utc) - timedelta(seconds=4)})
+            if item.job_id == job.job_id
+            else item
+            for item in queue.jobs
+        ]
+        store._persist_job_queue(queue)
+        store.finish_job(job.job_id, status=JobStatus.COMPLETED, summary="Fertig")
+
+        dashboard_response = client.get("/v1/actuators/dashboard")
+        start_response = client.get("/v1/actuators/dashboard/start")
+        system_response = client.get("/v1/actuators/dashboard/system")
+        anomalies_response = client.get("/v1/actuators/anomalies")
+
+    assert dashboard_response.status_code == 200
+    assert start_response.status_code == 200
+    assert system_response.status_code == 200
+    system = dashboard_response.json()["system"]
+    start_payload = start_response.json()
+    assert start_payload["jobs"]["jobs"] == []
+    assert start_payload["discovery_groups"] == []
+    assert system_response.json()["actuators"] == []
+    assert system["performance_budget_ms"] == 3000
+    assert system["slow_job_count"] == 1
+    assert system["performance_status"] == "slow"
+    assert system["anomaly_count"] >= 1
+    assert anomalies_response.status_code == 200
+    assert anomalies_response.json()
+
+
+def test_actuator_detail_uses_compact_payload(tmp_path: Path) -> None:
+    with TestClient(app) as client:
+        _install_service(tmp_path)
+        client.get("/v1/actuators/discovery")
+        client.post("/v1/actuators", json={"actuator_entity_id": "light.abstellkammer"})
+
+        response = client.get("/v1/actuators/light.abstellkammer/detail")
+
+    assert response.status_code == 200
+    payload = response.json()
+    assert payload["behavior"]["patterns"] == []
+    assert all(
+        snapshot["patterns"] == []
+        for snapshot in payload["behavior"]["model_snapshots"]
+    )
 
 
 def test_discovery_reads_entities_once_and_reuses_them(tmp_path: Path) -> None:

tests/test_dashboard.py

@@ -24,7 +24,7 @@ def test_dashboard_is_served_at_root() -> None:
     assert "SillyHome übernehmen lassen" in response.text
     assert "Passende Home-Assistant-Automationen" in response.text
     assert "Pausieren" in response.text
-    assert "Davon erkannte HA-Automationen" in response.text
+    assert "Erkannte HA-Automationen" in response.text
     assert "Aktuelle Situation auswerten" in response.text
     assert "Kontext selbst festlegen" in response.text
     assert "Entity-IDs manuell ergänzen" in response.text

tests/test_main.py

@@ -92,11 +92,11 @@ def test_ha_event_listener_processes_state_change(tmp_path: Path) -> None:
             except asyncio.CancelledError:
                 pass
 
-        connect.assert_called_once_with(
-            "ws://homeassistant:8123/api/websocket",
-            ping_interval=20,
-            ping_timeout=10,
-        )
+            connect.assert_called_once_with(
+                "ws://homeassistant:8123/api/websocket",
+                ping_interval=30,
+                ping_timeout=30,
+            )
         assert fake_ws.sent == [
             {"type": "auth", "access_token": "test-token"},
             {"id": 1, "type": "subscribe_events", "event_type": "state_changed"},