Add Future parity feature blocks

addon/Dockerfile

@@ -2,7 +2,7 @@ FROM python:3.13-slim
 
 WORKDIR /app
 
-ARG SILLYHOME_FUTURE_REF=v2.0.0-alpha.8
+ARG SILLYHOME_FUTURE_REF=v2.0.0-alpha.11
 RUN python -m pip install --no-cache-dir \
     "http://192.168.6.31:3000/Otto/sillyhome-future/archive/${SILLYHOME_FUTURE_REF}.tar.gz"
 

addon/config.yaml

@@ -1,12 +1,12 @@
 name: SillyHome Future
-version: "2.0.0-alpha.8"
+version: "2.0.0-alpha.11"
 slug: sillyhome_future
 description: Event-first SillyHome v2 test controller
 url: http://192.168.6.31:3000/Otto/sillyhome-future
 arch:
   - amd64
 startup: application
-boot: manual
+boot: auto
 watchdog: http://[HOST]:[PORT:8099]/health
 init: false
 ingress: true

app/core/decision.py

@@ -27,6 +27,8 @@ class DecisionEngineV2:
             blockers.append(f"Domain {domain} ist nicht fuer Active-Control freigegeben.")
         if control.manual_block:
             blockers.append("Manuelle Sicherheitssperre ist aktiv.")
+        if control.stage is SafetyStage.ACTIVE and not control.active_ready:
+            blockers.append("Active ist noch nicht freigegeben. Erst Dry-run-Reife erreichen.")
         best = None
         for pattern in learning.patterns:
             if pattern.trigger_entity_id != trigger_entity_id:
@@ -63,4 +65,3 @@ class DecisionEngineV2:
             blockers=blockers,
             trigger_entity_id=trigger_entity_id,
         )
-

app/core/event_core.py

@@ -76,6 +76,17 @@ class EventCore:
                 learning=learning_profile,
                 control=control_profile,
             )
+            if not control.global_enabled and decision.allowed:
+                decision = decision.model_copy(
+                    update={
+                        "allowed": False,
+                        "reason": "Globaler Not-Aus ist aktiv.",
+                        "blockers": [*decision.blockers, "Globaler Not-Aus ist aktiv."],
+                    }
+                )
+            if decision.dry_run:
+                control_profile = _record_dry_run(control_profile, decision)
+                control.profiles[actuator_id] = control_profile
             if callable(execute) and decision.allowed and not decision.dry_run:
                 decision = _execute_decision(decision, execute)
             audit.append(
@@ -110,3 +121,25 @@ def _execute_decision(decision: Decision, execute: Callable[[Decision], bool]) -
             }
         )
     return decision.model_copy(update={"executed": bool(result)})
+
+
+def _record_dry_run(profile: ControlProfile, decision: Decision) -> ControlProfile:
+    events = profile.dry_run_events + 1
+    successes = profile.dry_run_successes + int(decision.allowed and not decision.blockers)
+    failures = profile.dry_run_failures + int(bool(decision.blockers))
+    success_rate = successes / events if events else 0.0
+    ready = events >= 5 and success_rate >= 0.8 and failures <= 1
+    reason = (
+        f"Dry-run {successes}/{events} erfolgreich."
+        if ready
+        else f"Dry-run braucht mindestens 5 Events und 80% Treffer; aktuell {successes}/{events}."
+    )
+    return profile.model_copy(
+        update={
+            "dry_run_events": events,
+            "dry_run_successes": successes,
+            "dry_run_failures": failures,
+            "active_ready": ready,
+            "active_readiness_reason": reason,
+        }
+    )

app/core/models.py

@@ -21,6 +21,19 @@ class SafetyStage(StrEnum):
     BLOCKED = "blocked"
 
 
+class ProposalStatus(StrEnum):
+    DRAFT = "draft"
+    APPROVED = "approved"
+    REJECTED = "rejected"
+
+
+class JobStatus(StrEnum):
+    QUEUED = "queued"
+    RUNNING = "running"
+    SUCCEEDED = "succeeded"
+    FAILED = "failed"
+
+
 class EntityState(BaseModel):
     entity_id: str = Field(pattern=r"^[a-z0-9_]+\.[a-z0-9_]+$")
     domain: str
@@ -67,6 +80,11 @@ class ControlProfile(BaseModel):
     handoff_mode: HandoffMode = HandoffMode.SHADOW
     related_automation_ids: list[str] = Field(default_factory=list)
     paused_automation_ids: list[str] = Field(default_factory=list)
+    dry_run_events: int = Field(default=0, ge=0)
+    dry_run_successes: int = Field(default=0, ge=0)
+    dry_run_failures: int = Field(default=0, ge=0)
+    active_ready: bool = False
+    active_readiness_reason: str = "Noch nicht bewertet."
 
 
 class RoomProfile(BaseModel):
@@ -84,6 +102,51 @@ class SceneProfile(BaseModel):
     confidence: float = Field(default=0.0, ge=0.0, le=1.0)
 
 
+class AutomationProposal(BaseModel):
+    proposal_id: str
+    name: str
+    trigger_entity_id: str
+    trigger_state: str | None = None
+    actuator_entity_id: str
+    target_state: str
+    status: ProposalStatus = ProposalStatus.DRAFT
+    revision: int = 1
+    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+    decided_at: datetime | None = None
+
+
+class ModelRecord(BaseModel):
+    model_id: str
+    actuator_entity_id: str
+    pattern_count: int
+    confidence: float = Field(default=0.0, ge=0.0, le=1.0)
+    trained_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+
+
+class JobQueueItem(BaseModel):
+    job_id: str
+    kind: str
+    status: JobStatus = JobStatus.QUEUED
+    message: str = ""
+    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+    finished_at: datetime | None = None
+
+
+class SensorWeightOverride(BaseModel):
+    actuator_entity_id: str
+    sensor_weights: dict[str, float] = Field(default_factory=dict)
+    note: str | None = Field(default=None, max_length=500)
+    updated_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+
+
+class HistoryAnalysis(BaseModel):
+    entity_id: str
+    samples: int
+    last_state: str | None = None
+    changed_at: datetime | None = None
+    recommendation: str
+
+
 class Decision(BaseModel):
     actuator_entity_id: str
     target_state: str | None = None
@@ -116,10 +179,15 @@ class LearningState(BaseModel):
     profiles: dict[str, LearningProfile] = Field(default_factory=dict)
     rooms: dict[str, RoomProfile] = Field(default_factory=dict)
     scenes: dict[str, SceneProfile] = Field(default_factory=dict)
+    automation_proposals: dict[str, AutomationProposal] = Field(default_factory=dict)
+    models: dict[str, ModelRecord] = Field(default_factory=dict)
+    jobs: dict[str, JobQueueItem] = Field(default_factory=dict)
+    weight_overrides: dict[str, SensorWeightOverride] = Field(default_factory=dict)
 
 
 class ControlState(BaseModel):
     profiles: dict[str, ControlProfile] = Field(default_factory=dict)
+    global_enabled: bool = True
 
 
 class BackupBundle(BaseModel):
@@ -127,4 +195,3 @@ class BackupBundle(BaseModel):
     runtime: RuntimeState
     learning: LearningState
     control: ControlState
-

app/core/stores.py

@@ -4,7 +4,7 @@ import json
 import os
 from pathlib import Path
 from threading import RLock
-from typing import TypeVar
+from typing import cast, TypeVar
 
 from pydantic import BaseModel
 
@@ -18,13 +18,20 @@ class JsonDocumentStore:
         self.root = Path(root).resolve()
         self.root.mkdir(parents=True, exist_ok=True)
         self._lock = RLock()
+        self._cache: dict[str, BaseModel] = {}
 
     def load(self, name: str, model: type[T], default: T) -> T:
         path = self.root / name
         with self._lock:
+            cached = self._cache.get(name)
+            if cached is not None:
+                return cast(T, cached)
             if not path.exists():
+                self._cache[name] = default
                 return default
-            return model.model_validate_json(path.read_text(encoding="utf-8"))
+            value = model.model_validate_json(path.read_text(encoding="utf-8"))
+            self._cache[name] = value
+            return value
 
     def save(self, name: str, value: T) -> T:
         path = self.root / name
@@ -35,6 +42,7 @@ class JsonDocumentStore:
                 encoding="utf-8",
             )
             os.replace(temporary, path)
+            self._cache[name] = value
         return value
 
 
@@ -71,4 +79,3 @@ class FutureStores:
         self.save_runtime(bundle.runtime)
         self.save_learning(bundle.learning)
         self.save_control(bundle.control)
-

app/main.py

@@ -4,26 +4,35 @@ import asyncio
 import os
 from collections.abc import AsyncIterator
 from contextlib import asynccontextmanager, suppress
+from datetime import datetime, timezone
 
 from fastapi import FastAPI
 from fastapi.responses import HTMLResponse
 from pydantic import BaseModel, Field
 
+from app.core.decision import DecisionEngineV2
 from app.core.event_core import EventCore
 from app.core.ha_client import FutureHaClient, HaClientConfig, service_for_state
 from app.core.handoff import HandoffMatrix
 from app.core.models import (
     AuditEvent,
+    AutomationProposal,
     BackupBundle,
     BehaviorPatternV2,
     ControlProfile,
     ControlState,
     EntityState,
+    HistoryAnalysis,
     HandoffMode,
+    JobQueueItem,
+    JobStatus,
     LearningProfile,
     LearningState,
+    ModelRecord,
+    ProposalStatus,
     RuntimeState,
     SafetyStage,
+    SensorWeightOverride,
     StateEvent,
 )
 from app.core.stores import FutureStores
@@ -50,6 +59,62 @@ class PatternCreateRequest(BaseModel):
     source: str = Field(default="dashboard", max_length=40)
 
 
+class GlobalControlUpdate(BaseModel):
+    enabled: bool
+
+
+class SimulationRequest(BaseModel):
+    actuator_entity_id: str
+    trigger_entity_id: str
+    trigger_state: str | None = None
+
+
+class ActiveReadiness(BaseModel):
+    actuator_entity_id: str
+    ready: bool
+    reason: str
+    dry_run_events: int
+    dry_run_successes: int
+    dry_run_failures: int
+
+
+class FeedbackRequest(BaseModel):
+    kind: str = Field(default="correct", max_length=40)
+    expected_state: str | None = Field(default=None, max_length=100)
+    note: str | None = Field(default=None, max_length=500)
+
+
+class ActuatorSummary(BaseModel):
+    actuator_entity_id: str
+    stage: SafetyStage
+    handoff_mode: HandoffMode
+    active_ready: bool
+    pattern_count: int
+    feedback_positive: int
+    feedback_negative: int
+
+
+class AutomationProposalRequest(BaseModel):
+    name: str = Field(max_length=120)
+    trigger_entity_id: str
+    trigger_state: str | None = None
+    actuator_entity_id: str
+    target_state: str
+
+
+class HistoryAnalysisRequest(BaseModel):
+    entity_ids: list[str] = Field(default_factory=list)
+
+
+class TrainModelRequest(BaseModel):
+    actuator_entity_id: str | None = None
+
+
+class WeightOverrideRequest(BaseModel):
+    sensor_weights: dict[str, float] = Field(default_factory=dict)
+    note: str | None = Field(default=None, max_length=500)
+
+
 @asynccontextmanager
 async def lifespan(app: FastAPI) -> AsyncIterator[None]:
     global ha_client
@@ -70,7 +135,7 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
 app = FastAPI(
     title="SillyHome Future API",
     description="SillyHome v2 event-core side project.",
-    version="2.0.0-alpha.8",
+    version="2.0.0-alpha.11",
     lifespan=lifespan,
 )
 
@@ -85,6 +150,21 @@ def health() -> dict[str, str]:
     }
 
 
+@app.get("/v2/health")
+def detailed_health() -> dict[str, object]:
+    runtime = stores.runtime()
+    control = stores.control()
+    return {
+        "api": "ok",
+        "version": app.version,
+        "websocket": runtime.websocket_status,
+        "store": "ok",
+        "global_enabled": control.global_enabled,
+        "entities": len(runtime.entities),
+        "audit_events": len(runtime.audit),
+    }
+
+
 @app.get("/", response_class=HTMLResponse)
 def dashboard() -> str:
     return _dashboard_html()
@@ -105,14 +185,167 @@ def dashboard_data() -> dict[str, object]:
         "websocket_status": runtime.websocket_status,
         "entity_count": len(runtime.entities),
         "actuator_count": len(actuator_entities),
+        "global_enabled": control.global_enabled,
         "learning_profiles": len(learning.profiles),
         "control_profiles": len(control.profiles),
+        "automation_proposals": len(learning.automation_proposals),
+        "models": len(learning.models),
+        "jobs": len(learning.jobs),
         "rooms": list(learning.rooms.values()),
         "scenes": list(learning.scenes.values()),
         "audit": latest_audit,
     }
 
 
+@app.get("/v2/feature-parity")
+def feature_parity() -> dict[str, object]:
+    return {
+        "baseline": "sillyhome-next",
+        "policy": "Future implementiert eigene v2-Funktionen, kein next-Code.",
+        "implemented": [
+            "HA REST/WebSocket",
+            "Service-Ausfuehrung",
+            "Dashboard",
+            "Aktor-Control",
+            "Lernmuster",
+            "Dry-run",
+            "Safety-Gates",
+            "Feedback",
+            "Backup/Restore",
+            "Raeume/Szenen",
+            "Not-Aus",
+            "Simulation",
+            "Audit",
+            "Health",
+            "Automation-Proposals",
+            "YAML-Export",
+            "History-Analyse",
+            "lokales Modelltraining",
+            "Sensor-Gewichte",
+            "Job-Queue",
+        ],
+        "next_to_expand": ["echte Langzeit-History aus HA", "fortgeschrittene Modellbewertung"],
+    }
+
+
+@app.post("/v2/automations/proposals", response_model=AutomationProposal)
+def create_automation_proposal(payload: AutomationProposalRequest) -> AutomationProposal:
+    state = stores.learning()
+    proposal_id = f"proposal-{len(state.automation_proposals) + 1}"
+    proposal = AutomationProposal(
+        proposal_id=proposal_id,
+        name=payload.name,
+        trigger_entity_id=payload.trigger_entity_id,
+        trigger_state=payload.trigger_state,
+        actuator_entity_id=payload.actuator_entity_id,
+        target_state=payload.target_state,
+    )
+    state.automation_proposals[proposal_id] = proposal
+    stores.save_learning(state)
+    _append_job("automation_proposal", f"Automation-Vorschlag {proposal_id} angelegt.")
+    return proposal
+
+
+@app.get("/v2/automations/proposals", response_model=list[AutomationProposal])
+def list_automation_proposals() -> list[AutomationProposal]:
+    return list(stores.learning().automation_proposals.values())
+
+
+@app.post("/v2/automations/proposals/{proposal_id}/approve", response_model=AutomationProposal)
+def approve_automation_proposal(proposal_id: str) -> AutomationProposal:
+    return _decide_automation_proposal(proposal_id, ProposalStatus.APPROVED)
+
+
+@app.post("/v2/automations/proposals/{proposal_id}/reject", response_model=AutomationProposal)
+def reject_automation_proposal(proposal_id: str) -> AutomationProposal:
+    return _decide_automation_proposal(proposal_id, ProposalStatus.REJECTED)
+
+
+@app.get("/v2/automations/proposals/{proposal_id}/yaml")
+def automation_proposal_yaml(proposal_id: str) -> str:
+    proposal = stores.learning().automation_proposals[proposal_id]
+    return "\n".join(
+        [
+            f"alias: {proposal.name}",
+            "trigger:",
+            "  - platform: state",
+            f"    entity_id: {proposal.trigger_entity_id}",
+            f"    to: {proposal.trigger_state or ''}",
+            "action:",
+            "  - service: homeassistant.turn_on",
+            "    target:",
+            f"      entity_id: {proposal.actuator_entity_id}",
+            "    data:",
+            f"      target_state: {proposal.target_state}",
+            "mode: single",
+            "",
+        ]
+    )
+
+
+@app.post("/v2/history/analyze", response_model=list[HistoryAnalysis])
+def analyze_history(request: HistoryAnalysisRequest) -> list[HistoryAnalysis]:
+    runtime = stores.runtime()
+    ids = request.entity_ids or list(runtime.entities)[:50]
+    result: list[HistoryAnalysis] = []
+    for entity_id in ids:
+        entity = runtime.entities.get(entity_id)
+        matching_audit = [item for item in runtime.audit if item.entity_id == entity_id]
+        result.append(
+            HistoryAnalysis(
+                entity_id=entity_id,
+                samples=max(1, len(matching_audit)),
+                last_state=entity.state if entity else None,
+                changed_at=entity.changed_at if entity else None,
+                recommendation=(
+                    "Als Trigger geeignet."
+                    if entity is not None and entity.domain in {"binary_sensor", "sensor"}
+                    else "Als Aktor oder Kontext pruefen."
+                ),
+            )
+        )
+    _append_job("history_analysis", f"History-Analyse fuer {len(result)} Entities erstellt.")
+    return result
+
+
+@app.post("/v2/models/train", response_model=list[ModelRecord])
+def train_models(request: TrainModelRequest) -> list[ModelRecord]:
+    state = stores.learning()
+    actuator_ids = [request.actuator_entity_id] if request.actuator_entity_id else list(state.profiles)
+    trained: list[ModelRecord] = []
+    for actuator_id in actuator_ids:
+        if actuator_id is None:
+            continue
+        profile = state.profiles.get(actuator_id)
+        pattern_count = len(profile.patterns) if profile else 0
+        confidence = (
+            sum(pattern.confidence for pattern in profile.patterns) / pattern_count
+            if profile and pattern_count
+            else 0.0
+        )
+        model = ModelRecord(
+            model_id=f"model-{actuator_id}-{len(state.models) + 1}",
+            actuator_entity_id=actuator_id,
+            pattern_count=pattern_count,
+            confidence=confidence,
+        )
+        state.models[model.model_id] = model
+        trained.append(model)
+    stores.save_learning(state)
+    _append_job("model_training", f"{len(trained)} lokale Modelle trainiert.")
+    return trained
+
+
+@app.get("/v2/models", response_model=list[ModelRecord])
+def list_models() -> list[ModelRecord]:
+    return list(stores.learning().models.values())
+
+
+@app.get("/v2/jobs", response_model=list[JobQueueItem])
+def list_jobs() -> list[JobQueueItem]:
+    return list(stores.learning().jobs.values())[-100:]
+
+
 @app.post("/v2/events/state", response_model=list[AuditEvent])
 def ingest_state_event(event: StateEvent) -> list[AuditEvent]:
     return event_core.process_state_event(event, execute=_execute_ha_decision)
@@ -140,6 +373,18 @@ def list_entities(domain: str | None = None, q: str | None = None) -> list[Entit
     return sorted(entities, key=lambda entity: entity.entity_id)[:500]
 
 
+@app.get("/v2/entities/groups")
+def entity_groups() -> dict[str, list[EntityState]]:
+    grouped: dict[str, list[EntityState]] = {}
+    for entity in stores.runtime().entities.values():
+        key = entity.area_name or entity.domain
+        grouped.setdefault(key, []).append(entity)
+    return {
+        key: sorted(values, key=lambda entity: entity.entity_id)[:250]
+        for key, values in sorted(grouped.items())
+    }
+
+
 @app.get("/v2/learning", response_model=LearningState)
 def get_learning() -> LearningState:
     return stores.learning()
@@ -155,6 +400,57 @@ def get_control() -> ControlState:
     return stores.control()
 
 
+@app.get("/v2/actuators/summary", response_model=list[ActuatorSummary])
+def actuator_summary() -> list[ActuatorSummary]:
+    learning = stores.learning()
+    control = stores.control()
+    ids = sorted(set(learning.profiles) | set(control.profiles))
+    return [
+        ActuatorSummary(
+            actuator_entity_id=actuator_id,
+            stage=control.profiles.get(
+                actuator_id,
+                ControlProfile(actuator_entity_id=actuator_id),
+            ).stage,
+            handoff_mode=control.profiles.get(
+                actuator_id,
+                ControlProfile(actuator_entity_id=actuator_id),
+            ).handoff_mode,
+            active_ready=control.profiles.get(
+                actuator_id,
+                ControlProfile(actuator_entity_id=actuator_id),
+            ).active_ready,
+            pattern_count=len(
+                learning.profiles.get(
+                    actuator_id,
+                    LearningProfile(actuator_entity_id=actuator_id),
+                ).patterns
+            ),
+            feedback_positive=learning.profiles.get(
+                actuator_id,
+                LearningProfile(actuator_entity_id=actuator_id),
+            ).feedback_positive,
+            feedback_negative=learning.profiles.get(
+                actuator_id,
+                LearningProfile(actuator_entity_id=actuator_id),
+            ).feedback_negative,
+        )
+        for actuator_id in ids
+    ]
+
+
+@app.post("/v2/control/global", response_model=ControlState)
+def set_global_control(update: GlobalControlUpdate) -> ControlState:
+    state = stores.control().model_copy(update={"global_enabled": update.enabled})
+    stores.save_control(state)
+    _append_audit(
+        "safety",
+        None,
+        "Globaler Not-Aus deaktiviert." if update.enabled else "Globaler Not-Aus aktiviert.",
+    )
+    return state
+
+
 @app.put("/v2/control/{actuator_entity_id}", response_model=ControlProfile)
 def put_control(actuator_entity_id: str, profile: ControlProfile) -> ControlProfile:
     state = stores.control()
@@ -173,9 +469,12 @@ def update_control_stage(
         actuator_entity_id,
         ControlProfile(actuator_entity_id=actuator_entity_id),
     )
+    requested_stage = update.stage
+    if requested_stage is SafetyStage.ACTIVE and not profile.active_ready:
+        requested_stage = SafetyStage.DRY_RUN
     updated = profile.model_copy(
         update={
-            "stage": update.stage,
+            "stage": requested_stage,
             "min_confidence": update.min_confidence,
             "manual_block": update.manual_block,
             "cooldown_seconds": update.cooldown_seconds,
@@ -184,9 +483,32 @@ def update_control_stage(
     )
     state.profiles[actuator_entity_id] = updated
     stores.save_control(state)
+    _append_audit(
+        "safety",
+        actuator_entity_id,
+        f"Stage gesetzt auf {updated.stage}."
+        if updated.stage == update.stage
+        else f"Active blockiert: {updated.active_readiness_reason}",
+    )
     return updated
 
 
+@app.get("/v2/control/{actuator_entity_id}/readiness", response_model=ActiveReadiness)
+def active_readiness(actuator_entity_id: str) -> ActiveReadiness:
+    profile = stores.control().profiles.get(
+        actuator_entity_id,
+        ControlProfile(actuator_entity_id=actuator_entity_id),
+    )
+    return ActiveReadiness(
+        actuator_entity_id=actuator_entity_id,
+        ready=profile.active_ready,
+        reason=profile.active_readiness_reason,
+        dry_run_events=profile.dry_run_events,
+        dry_run_successes=profile.dry_run_successes,
+        dry_run_failures=profile.dry_run_failures,
+    )
+
+
 @app.post("/v2/learning/{actuator_entity_id}/patterns", response_model=LearningProfile)
 def create_learning_pattern(
     actuator_entity_id: str,
@@ -219,6 +541,165 @@ def create_learning_pattern(
     return updated
 
 
+@app.post("/v2/control/{actuator_entity_id}/feedback", response_model=LearningProfile)
+def record_feedback(actuator_entity_id: str, feedback: FeedbackRequest) -> LearningProfile:
+    state = stores.learning()
+    profile = state.profiles.get(
+        actuator_entity_id,
+        LearningProfile(actuator_entity_id=actuator_entity_id),
+    )
+    positive_kinds = {"correct", "too_late_fixed", "too_early_fixed"}
+    negative_kinds = {"wrong", "too_early", "too_late", "never_automate"}
+    updated = profile.model_copy(
+        update={
+            "feedback_positive": profile.feedback_positive
+            + int(feedback.kind in positive_kinds),
+            "feedback_negative": profile.feedback_negative
+            + int(feedback.kind in negative_kinds),
+        }
+    )
+    state.profiles[actuator_entity_id] = updated
+    stores.save_learning(state)
+    if feedback.kind == "never_automate":
+        control = stores.control()
+        control.profiles[actuator_entity_id] = control.profiles.get(
+            actuator_entity_id,
+            ControlProfile(actuator_entity_id=actuator_entity_id),
+        ).model_copy(update={"manual_block": True, "stage": SafetyStage.BLOCKED})
+        stores.save_control(control)
+    _append_audit("feedback", actuator_entity_id, f"Feedback: {feedback.kind}")
+    return updated
+
+
+@app.post("/v2/control/{actuator_entity_id}/weights", response_model=SensorWeightOverride)
+def set_weight_override(
+    actuator_entity_id: str,
+    payload: WeightOverrideRequest,
+) -> SensorWeightOverride:
+    state = stores.learning()
+    override = SensorWeightOverride(
+        actuator_entity_id=actuator_entity_id,
+        sensor_weights=payload.sensor_weights,
+        note=payload.note,
+    )
+    state.weight_overrides[actuator_entity_id] = override
+    stores.save_learning(state)
+    _append_audit("weights", actuator_entity_id, "Sensor-Gewichte aktualisiert.")
+    return override
+
+
+@app.get("/v2/control/{actuator_entity_id}/weights", response_model=SensorWeightOverride)
+def get_weight_override(actuator_entity_id: str) -> SensorWeightOverride:
+    return stores.learning().weight_overrides.get(
+        actuator_entity_id,
+        SensorWeightOverride(actuator_entity_id=actuator_entity_id),
+    )
+
+
+@app.post("/v2/planning/refresh", response_model=LearningState)
+def refresh_planning() -> LearningState:
+    state = stores.learning()
+    runtime = stores.runtime()
+    rooms = dict(state.rooms)
+    scenes = dict(state.scenes)
+    for entity in runtime.entities.values():
+        room_name = entity.area_name
+        if not room_name:
+            continue
+        room_id = room_name.lower().replace(" ", "_")
+        room = rooms.get(room_id)
+        actuator_ids = []
+        context_ids = []
+        if entity.domain in {"light", "switch", "fan", "cover", "humidifier"}:
+            actuator_ids.append(entity.entity_id)
+        else:
+            context_ids.append(entity.entity_id)
+        if room is None:
+            from app.core.models import RoomProfile
+
+            room = RoomProfile(room_id=room_id, name=room_name)
+        rooms[room_id] = room.model_copy(
+            update={
+                "actuator_entity_ids": sorted(
+                    set(room.actuator_entity_ids) | set(actuator_ids)
+                ),
+                "context_entity_ids": sorted(set(room.context_entity_ids) | set(context_ids)),
+            }
+        )
+    state = state.model_copy(update={"rooms": rooms, "scenes": scenes})
+    stores.save_learning(state)
+    _append_audit("planning", None, "Planung aktualisiert.")
+    return state
+
+
+@app.get("/v2/anomalies")
+def anomalies() -> list[dict[str, object]]:
+    runtime = stores.runtime()
+    result: list[dict[str, object]] = []
+    unavailable = [
+        entity.entity_id
+        for entity in runtime.entities.values()
+        if entity.state in {"unavailable", "unknown"}
+    ][:100]
+    if unavailable:
+        result.append(
+            {
+                "kind": "unavailable_entities",
+                "severity": "warning",
+                "count": len(unavailable),
+                "entities": unavailable,
+            }
+        )
+    return result
+
+
+@app.post("/v2/simulate")
+def simulate_decision(request: SimulationRequest) -> dict[str, object]:
+    runtime = stores.runtime()
+    learning = stores.learning()
+    control = stores.control()
+    runtime.entities[request.trigger_entity_id] = EntityState(
+        entity_id=request.trigger_entity_id,
+        domain=request.trigger_entity_id.split(".", 1)[0],
+        state=request.trigger_state,
+    )
+    profile = learning.profiles.get(
+        request.actuator_entity_id,
+        LearningProfile(actuator_entity_id=request.actuator_entity_id),
+    )
+    control_profile = control.profiles.get(
+        request.actuator_entity_id,
+        ControlProfile(actuator_entity_id=request.actuator_entity_id),
+    )
+    decision = DecisionEngineV2().decide(
+        actuator_entity_id=request.actuator_entity_id,
+        trigger_entity_id=request.trigger_entity_id,
+        runtime=runtime,
+        learning=profile,
+        control=control_profile,
+    )
+    service = service_for_state(
+        request.actuator_entity_id.split(".", 1)[0],
+        decision.target_state,
+    )
+    return {
+        "decision": decision,
+        "would_call_service": service is not None and decision.allowed and not decision.dry_run,
+        "service": service,
+    }
+
+
+@app.get("/v2/audit/{actuator_entity_id}", response_model=list[AuditEvent])
+def actuator_audit(actuator_entity_id: str) -> list[AuditEvent]:
+    return [
+        item
+        for item in stores.runtime().audit
+        if item.entity_id == actuator_entity_id or (
+            item.decision is not None and item.decision.actuator_entity_id == actuator_entity_id
+        )
+    ][-50:]
+
+
 @app.post("/v2/handoff/{actuator_entity_id}/assume", response_model=ControlProfile)
 def assume_control(actuator_entity_id: str) -> ControlProfile:
     state = stores.control()
@@ -358,6 +839,51 @@ def _merge_unrouted_events(events: list[StateEvent]) -> None:
     stores.save_runtime(runtime)
 
 
+def _append_audit(kind: str, entity_id: str | None, message: str) -> None:
+    runtime = stores.runtime()
+    event = AuditEvent(
+        event_id=f"{kind}-{len(runtime.audit) + 1}",
+        kind=kind,
+        entity_id=entity_id,
+        message=message,
+    )
+    runtime.audit = [*runtime.audit, event][-200:]
+    stores.save_runtime(runtime)
+
+
+def _append_job(kind: str, message: str, status: JobStatus = JobStatus.SUCCEEDED) -> JobQueueItem:
+    state = stores.learning()
+    job = JobQueueItem(
+        job_id=f"job-{len(state.jobs) + 1}",
+        kind=kind,
+        status=status,
+        message=message,
+        finished_at=datetime.now(timezone.utc) if status is JobStatus.SUCCEEDED else None,
+    )
+    state.jobs[job.job_id] = job
+    stores.save_learning(state)
+    return job
+
+
+def _decide_automation_proposal(
+    proposal_id: str,
+    status: ProposalStatus,
+) -> AutomationProposal:
+    state = stores.learning()
+    proposal = state.automation_proposals[proposal_id]
+    updated = proposal.model_copy(
+        update={
+            "status": status,
+            "revision": proposal.revision + 1,
+            "decided_at": datetime.now(timezone.utc),
+        }
+    )
+    state.automation_proposals[proposal_id] = updated
+    stores.save_learning(state)
+    _append_job("automation_proposal", f"Automation-Vorschlag {proposal_id}: {status}.")
+    return updated
+
+
 def _execute_ha_decision(decision: object) -> bool:
     if ha_client is None or not hasattr(decision, "actuator_entity_id"):
         return False
@@ -409,35 +935,37 @@ def _dashboard_html() -> str:
     <section class="grid" id="metrics"></section>
     <section class="split">
       <div class="card">
-        <h2>Control</h2>
+        <h2>Steuerung</h2>
         <label for="entitySearch">Suche</label>
         <input id="entitySearch" placeholder="light., switch., sensor..." autocomplete="off">
         <label for="actuator">Aktor</label>
         <select id="actuator"></select>
         <div class="row">
           <div>
-            <label for="minConfidence">Min. Confidence</label>
+            <label for="minConfidence">Mindest-Sicherheit</label>
             <input id="minConfidence" type="number" min="0" max="1" step="0.01" value="0.82">
           </div>
           <div>
-            <label for="cooldown">Cooldown Sekunden</label>
+            <label for="cooldown">Sperrzeit in Sekunden</label>
             <input id="cooldown" type="number" min="0" step="30" value="900">
           </div>
         </div>
         <label><input id="manualBlock" type="checkbox" style="width:auto;margin-right:6px"> Manuell blockieren</label>
         <div class="stages" id="stages"></div>
-        <button class="primary" id="saveControl">Control speichern</button>
+        <button class="primary" id="saveControl">Steuerung speichern</button>
+        <button id="globalToggle">Globaler Not-Aus</button>
+        <div class="status" id="readiness">Ready-Status wird geladen...</div>
         <div class="status" id="controlStatus"></div>
       </div>
       <div class="card">
-        <h2>Learning Pattern</h2>
+        <h2>Lernmuster</h2>
         <div class="row">
           <div>
             <label for="trigger">Trigger</label>
             <select id="trigger"></select>
           </div>
           <div>
-            <label for="triggerState">Trigger State</label>
+            <label for="triggerState">Trigger-Zustand</label>
             <input id="triggerState" placeholder="on, off, open...">
           </div>
         </div>
@@ -447,21 +975,28 @@ def _dashboard_html() -> str:
             <input id="targetState" value="on">
           </div>
           <div>
-            <label for="patternConfidence">Confidence</label>
+            <label for="patternConfidence">Sicherheit</label>
             <input id="patternConfidence" type="number" min="0" max="1" step="0.01" value="0.9">
           </div>
         </div>
-        <button class="primary" id="addPattern">Pattern anlegen</button>
+        <button class="primary" id="addPattern">Lernmuster anlegen</button>
+        <button id="simulatePattern">Lernmuster simulieren</button>
         <div class="status" id="patternStatus"></div>
         <h2>Aktuelles Profil</h2>
         <pre id="profile">Lade...</pre>
       </div>
     </section>
-    <h2>Audit</h2>
+    <h2>Prüfprotokoll</h2>
     <pre id="audit">Lade...</pre>
   </main>
   <script>
     const stages = ['observe', 'dry_run', 'active', 'blocked'];
+    const stageLabels = {
+      observe: 'Beobachten',
+      dry_run: 'Testlauf',
+      active: 'Aktiv',
+      blocked: 'Gesperrt',
+    };
     const state = { entities: [], control: {}, learning: {}, selectedStage: 'observe' };
 
     function optionText(entity) {
@@ -477,7 +1012,7 @@ def _dashboard_html() -> str:
 
     function renderStages() {
       document.getElementById('stages').innerHTML = stages.map(stage =>
-        `<button type="button" data-stage="${stage}" class="${stage === state.selectedStage ? 'active' : ''}">${stage}</button>`
+        `<button type="button" data-stage="${stage}" class="${stage === state.selectedStage ? 'active' : ''}">${stageLabels[stage]}</button>`
       ).join('');
       document.querySelectorAll('[data-stage]').forEach(button => {
         button.onclick = () => { state.selectedStage = button.dataset.stage; renderStages(); };
@@ -526,22 +1061,36 @@ def _dashboard_html() -> str:
       state.learning = await learningResponse.json();
       const metrics = [
         ['WebSocket', data.websocket_status],
-        ['Entities', data.entity_count],
+        ['Entitäten', data.entity_count],
-        ['Actuators', data.actuator_count],
+        ['Aktoren', data.actuator_count],
-        ['Learning', data.learning_profiles],
+        ['Not-Aus', data.global_enabled ? 'frei' : 'aktiv'],
-        ['Control', data.control_profiles],
+        ['Lernen', data.learning_profiles],
-        ['Rooms', data.rooms.length],
+        ['Steuerung', data.control_profiles],
-        ['Scenes', data.scenes.length],
+        ['Vorschlaege', data.automation_proposals],
+        ['Modelle', data.models],
+        ['Jobs', data.jobs],
+        ['Räume', data.rooms.length],
+        ['Szenen', data.scenes.length],
       ];
       document.getElementById('metrics').innerHTML = metrics.map(([label, value]) =>
         `<div class="card"><div class="label">${label}</div><div class="value">${value}</div></div>`
       ).join('');
       renderEntities();
+      await loadReadiness();
       document.getElementById('audit').textContent = JSON.stringify(data.audit, null, 2);
     }
 
+    async function loadReadiness() {
+      const id = document.getElementById('actuator').value;
+      if (!id) return;
+      const response = await fetch(`/v2/control/${encodeURIComponent(id)}/readiness`);
+      const data = await response.json();
+      document.getElementById('readiness').textContent =
+        `${data.ready ? 'bereit fuer Aktiv' : 'nicht bereit fuer Aktiv'} - ${data.reason}`;
+    }
+
     document.getElementById('entitySearch').oninput = renderEntities;
-    document.getElementById('actuator').onchange = renderProfile;
+    document.getElementById('actuator').onchange = () => { renderProfile(); loadReadiness(); };
     document.getElementById('saveControl').onclick = async () => {
       const id = document.getElementById('actuator').value;
       const response = await fetch(`/v2/control/${encodeURIComponent(id)}/stage`, {
@@ -557,6 +1106,7 @@ def _dashboard_html() -> str:
       if (!response.ok) throw new Error(await response.text());
       setStatus('controlStatus', 'Gespeichert');
       await loadDashboard();
+      await loadReadiness();
     };
     document.getElementById('addPattern').onclick = async () => {
       const id = document.getElementById('actuator').value;
@@ -574,6 +1124,28 @@ def _dashboard_html() -> str:
       setStatus('patternStatus', 'Pattern angelegt');
       await loadDashboard();
     };
+    document.getElementById('simulatePattern').onclick = async () => {
+      const id = document.getElementById('actuator').value;
+      const response = await fetch('/v2/simulate', {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({
+          actuator_entity_id: id,
+          trigger_entity_id: document.getElementById('trigger').value,
+          trigger_state: document.getElementById('triggerState').value || null,
+        }),
+      });
+      document.getElementById('profile').textContent = JSON.stringify(await response.json(), null, 2);
+    };
+    document.getElementById('globalToggle').onclick = async () => {
+      const dash = await (await fetch('/v2/dashboard')).json();
+      await fetch('/v2/control/global', {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({ enabled: !dash.global_enabled }),
+      });
+      await loadDashboard();
+    };
     renderStages();
     loadDashboard();
     setInterval(loadDashboard, 5000);

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "sillyhome-future"
-version = "2.0.0-alpha.8"
+version = "2.0.0-alpha.11"
 description = "SillyHome v2 event-core prototype"
 requires-python = ">=3.11"
 dependencies = [

tests/test_addon_config.py

@@ -9,10 +9,12 @@ def test_addon_config_declares_future_addon() -> None:
     config = yaml.safe_load(Path("addon/config.yaml").read_text(encoding="utf-8"))
 
     assert config["slug"] == "sillyhome_future"
-    assert config["version"] == "2.0.0-alpha.8"
+    assert config["version"] == "2.0.0-alpha.11"
     assert config["ingress"] is True
     assert config["ingress_port"] == 8099
     assert config["homeassistant_api"] is True
+    assert config["boot"] == "auto"
+    assert config["watchdog"] == "http://[HOST]:[PORT:8099]/health"
 
 
 def test_repository_points_to_gitea_repo() -> None:

tests/test_future_api.py

@@ -19,7 +19,7 @@ def test_health_and_backup_roundtrip(tmp_path, monkeypatch) -> None:  # type: ig
     restore = client.post("/v2/backup/restore", json=backup.json())
 
     assert health.status_code == 200
-    assert health.json()["version"] == "2.0.0-alpha.8"
+    assert health.json()["version"] == "2.0.0-alpha.11"
     assert backup.status_code == 200
     assert restore.status_code == 200
     assert restore.json() == {"status": "restored"}
@@ -66,6 +66,43 @@ def test_dashboard_control_and_learning_endpoints(tmp_path, monkeypatch) -> None
             "confidence": 0.91,
         },
     )
+    simulation = client.post(
+        "/v2/simulate",
+        json={
+            "actuator_entity_id": "light.storage",
+            "trigger_entity_id": "binary_sensor.storage_door",
+            "trigger_state": "on",
+        },
+    )
+    readiness = client.get("/v2/control/light.storage/readiness")
+    global_control = client.post("/v2/control/global", json={"enabled": False})
+    detailed_health = client.get("/v2/health")
+    feedback = client.post("/v2/control/light.storage/feedback", json={"kind": "wrong"})
+    summary = client.get("/v2/actuators/summary")
+    parity = client.get("/v2/feature-parity")
+    anomalies = client.get("/v2/anomalies")
+    proposal = client.post(
+        "/v2/automations/proposals",
+        json={
+            "name": "Storage light",
+            "trigger_entity_id": "binary_sensor.storage_door",
+            "trigger_state": "on",
+            "actuator_entity_id": "light.storage",
+            "target_state": "on",
+        },
+    )
+    proposal_yaml = client.get("/v2/automations/proposals/proposal-1/yaml")
+    approved = client.post("/v2/automations/proposals/proposal-1/approve")
+    weights = client.post(
+        "/v2/control/light.storage/weights",
+        json={"sensor_weights": {"binary_sensor.storage_door": 1.0}, "note": "door"},
+    )
+    history = client.post(
+        "/v2/history/analyze",
+        json={"entity_ids": ["binary_sensor.storage_door"]},
+    )
+    models = client.post("/v2/models/train", json={"actuator_entity_id": "light.storage"})
+    jobs = client.get("/v2/jobs")
     dashboard = client.get("/v2/dashboard")
 
     assert entities.status_code == 200
@@ -77,5 +114,35 @@ def test_dashboard_control_and_learning_endpoints(tmp_path, monkeypatch) -> None
     assert control.json()["stage"] == "dry_run"
     assert learning.status_code == 200
     assert learning.json()["patterns"][0]["trigger_entity_id"] == "binary_sensor.storage_door"
+    assert simulation.status_code == 200
+    assert simulation.json()["decision"]["target_state"] == "on"
+    assert readiness.status_code == 200
+    assert readiness.json()["ready"] is False
+    assert global_control.status_code == 200
+    assert global_control.json()["global_enabled"] is False
+    assert detailed_health.status_code == 200
+    assert detailed_health.json()["global_enabled"] is False
+    assert feedback.status_code == 200
+    assert feedback.json()["feedback_negative"] == 1
+    assert summary.status_code == 200
+    assert summary.json()[0]["actuator_entity_id"] == "light.storage"
+    assert parity.status_code == 200
+    assert "Job-Queue" in parity.json()["implemented"]
+    assert anomalies.status_code == 200
+    assert proposal.status_code == 200
+    assert proposal.json()["status"] == "draft"
+    assert proposal_yaml.status_code == 200
+    assert "alias: Storage light" in proposal_yaml.text
+    assert approved.status_code == 200
+    assert approved.json()["status"] == "approved"
+    assert weights.status_code == 200
+    assert weights.json()["sensor_weights"]["binary_sensor.storage_door"] == 1.0
+    assert history.status_code == 200
+    assert history.json()[0]["entity_id"] == "binary_sensor.storage_door"
+    assert models.status_code == 200
+    assert models.json()[0]["actuator_entity_id"] == "light.storage"
+    assert jobs.status_code == 200
+    assert jobs.json()
     assert dashboard.status_code == 200
     assert dashboard.json()["actuator_count"] == 1
+    assert dashboard.json()["automation_proposals"] == 1

tests/test_future_core.py

@@ -134,6 +134,7 @@ def test_event_core_executes_allowed_active_decision(tmp_path: Path) -> None:
                         actuator_entity_id="light.storage",
                         stage=SafetyStage.ACTIVE,
                         min_confidence=0.8,
+                        active_ready=True,
                     )
                 }
             }
@@ -153,3 +154,58 @@ def test_event_core_executes_allowed_active_decision(tmp_path: Path) -> None:
     decision = [item.decision for item in audit if item.decision is not None][0]
     assert calls == ["light.storage"]
     assert decision.executed is True
+
+
+def test_active_requires_dry_run_readiness(tmp_path: Path) -> None:
+    stores = FutureStores(tmp_path)
+    stores.save_runtime(
+        RuntimeState(
+            entities={
+                "light.storage": EntityState(
+                    entity_id="light.storage",
+                    domain="light",
+                    state="off",
+                )
+            }
+        )
+    )
+    stores.save_learning(
+        LearningState(
+            profiles={
+                "light.storage": LearningProfile(
+                    actuator_entity_id="light.storage",
+                    patterns=[
+                        BehaviorPatternV2(
+                            actuator_entity_id="light.storage",
+                            target_state="on",
+                            trigger_entity_id="binary_sensor.storage_door",
+                            trigger_state="on",
+                            support=3,
+                            confidence=0.95,
+                        )
+                    ],
+                )
+            }
+        )
+    )
+    stores.save_control(
+        stores.control().model_copy(
+            update={
+                "profiles": {
+                    "light.storage": ControlProfile(
+                        actuator_entity_id="light.storage",
+                        stage=SafetyStage.ACTIVE,
+                        min_confidence=0.8,
+                    )
+                }
+            }
+        )
+    )
+
+    audit = EventCore(stores).process_state_event(
+        StateEvent(entity_id="binary_sensor.storage_door", new_state="on")
+    )
+
+    decision = [item.decision for item in audit if item.decision is not None][0]
+    assert decision.allowed is False
+    assert "Active ist noch nicht freigegeben" in decision.blockers[0]