Localize and streamline dashboard loading

CHANGELOG.md

@@ -1,5 +1,156 @@
 # 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
+  angezeigt.
+- Gewichtungen koennen im Dashboard korrigiert und per API unter
+  `/v1/actuators/{actuator_entity_id}/weights` gespeichert werden.
+- Gruppen-Gewichtungen buendeln mehrere Sensoren/Zustaende fuer einen Aktor,
+  damit verbundene Kontextsignale gemeinsam bewertet werden koennen.
+
+## 1.0.3 - 2026-06-17
+- Header-Menue als Pulldown umgesetzt; die separate Navigationsleiste entfaellt.
+- Geraetegruppen und manuelle Kontextbereiche sind standardmaessig geschlossen.
+- Dashboard startet in Phasen: leere Bedienoberflaeche, dann Status, danach
+  Geraetedaten.
+- Detailansicht oeffnet streamartiger: zuerst Basis-Shell, dann Aktorwerte,
+  danach Kontextvorschlaege.
+
+## 1.0.2 - 2026-06-17
+- v1.0-Abnahme als `docs/V1_0_ACCEPTANCE.md` dokumentiert: erledigte,
+  teilweise erledigte und offene v1.0.x-Punkte sind getrennt sichtbar.
+- Dashboard-Startstatistik erweitert: Freigabebereitschaft, Aktiv/Shadow,
+  Gelernt/Wartet und gelernte Handlungen werden direkt im Startbereich
+  zusammengefasst.
+
+## 1.0.1 - 2026-06-17
+- Dashboard-UI nach v1-Korrektur neu strukturiert: feste Steuerungsleiste,
+  separate Geräteübersicht, klare Freigabe-/Detailfläche und Statusbereich.
+- Orange bleibt Primärfarbe; Cyan ist die sichtbare Komplementärfarbe. Rote
+  Aktions- und Fehlerflächen wurden aus der Oberfläche entfernt.
+- Startpfad weiter beschleunigt: Dashboard lädt nur noch lokale Startdaten.
+  HA-Discovery, Vorschläge und Automation-Refresh laufen erst nach Nutzeraktion.
+- Detailansicht öffnet ohne automatische Automation-Discovery. Passende
+  Automationen können gezielt per Button neu gesucht werden.
+
+## 1.0.0 - 2026-06-17
+- Neuer blockweiser Dashboard-Start über `/v1/actuators/dashboard`: lokale
+  Store-/Cache-Daten laden sofort, HA-Discovery und Vorschläge laufen
+  nachgelagert.
+- Discovery liest Entities pro Anfrage nur noch einmal und klassifiziert aus
+  diesem Snapshot weiter. Dadurch entfallen doppelte HA-Vollabfragen.
+- Persistenter JSON-Entity-Cache wird für Friendly Name, Raum, Gerät,
+  Discovery-Gruppen und schnelle Summaries genutzt.
+- Dashboard mit Orange als Primärfarbe, kompakter Navigation, aufklappbarer
+  Anleitung, aufklappbaren Gerätegruppen und Cache-/Systemstatistik.
+- Aktor-/Sensor-Kategorien erweitert: Feuchte, Wetter, Helligkeit, Bewegung,
+  Tür/Fenster, Präsenz, Lichtzustände, Schalter, Steckdosen, Lüftung, Heizung,
+  Cover, Helper, PV/Akku/Einspeisung.
+- Kontextvorschläge vermeiden weitere doppelte HA-Discovery und sortieren
+  aktortypbezogen nach relevanten Bereichen.
+
+## 0.7.21 - 2026-06-17
+- Dashboard-Ladepfad getrennt: beobachtete Geräte laden sofort über
+  `/v1/actuators/summary`; Status, Discovery und Vorschläge laufen unabhängig
+  nachgelagert und blockieren die Übersicht nicht mehr.
+- Systemstatus nutzt Timeouts und bleibt auch bei langsamem ML-/HA-Status
+  bedienbar.
+- HA-Entity-Metadaten werden als JSON-Cache gespeichert und für Friendly Name,
+  Raum und Gerät in schlanken Summaries wiederverwendet.
+- Anleitung, Gerätegruppen und manuelle Kontextauswahl sind aufklappbar und
+  kompakter für Smartphone- und Desktopansichten.
+
+## 0.7.20 - 2026-06-17
+- Dashboard-Übersicht ist kompatibel mit dem leichten Summary-Format und greift
+  nicht mehr auf `record.behavior.status` aus dem Vollformat zu.
+
+## 0.7.19 - 2026-06-17
+- Dashboard-Übersicht nutzt einen leichten `/v1/actuators/summary`-Endpunkt
+  statt voller Lernmuster und kompletter HA-Entityliste.
+- Nach Aktionen werden Dashboard-Caches gezielt invalidiert, damit keine
+  stale oder doppelt geladenen Einträge entstehen.
+
+## 0.7.18 - 2026-06-16
+- Dashboard lädt Aktoren, Entities und Discovery nur noch einmal pro Refresh und
+  rendert daraus Auswahl und Übersicht ohne doppelte API-Ladewege.
+- Manuelle Kontext-Evidenz wird dedupliziert, damit Hinweise wie
+  "Manuell vom Nutzer als relevant festgelegt" nicht mehrfach erscheinen.
+- Kontextauswahl ist vollständiger: Feuchte, Wetter, Licht-/Schalterzustände,
+  Bewegungs-/Tür-/Präsenzmelder, PV/Akku/Einspeisung und Helper werden sauberer
+  kategorisiert und per Suche/Kategorie erreichbar.
+- Domainspezifische Zuordnung geschärft: Lüftungen bevorzugen Feuchte/Temperatur,
+  Lichter Helligkeit/Bewegung/Tür/Präsenz, Heizungen Temperatur/Anwesenheit/Wetter.
+
+## 0.7.17 - 2026-06-16
+- WebSocket-Eventpfad ist schneller: irrelevante HA-State-Changes werden vor
+  dem teuren State-Cache-Listenbau verworfen.
+- WebSocket nutzt Keepalive und reconnectet nach Abbrüchen nach 1s statt 5s.
+
+## 0.7.16 - 2026-06-16
+- Beobachtete Aktoren werden in der Übersicht nach Raum oder Typ gruppiert und
+  mit Friendly Name angezeigt.
+
+## 0.7.15 - 2026-06-16
+- Add-on-Start ist robust gegen Home-Assistant-Core-502 beim Systemboot:
+  API und WebSocket-Listener starten trotzdem, Reconciliation/Training werden
+  im Hintergrund mit Retry nachgeholt.
+- Periodische Reconciliation und Fallback-Auswertung beenden den Dienst nicht
+  mehr bei temporären HA-Fehlern.
+- Add-on-Watchdog prüft `/health`, damit Supervisor den Dienst nach Absturz
+  wieder starten kann.
+
+## 0.7.14 - 2026-06-16
+- Onboarding-Vorschläge laden im Dashboard nachgelagert, damit Status,
+  Aktor-Auswahl und bestehende Geräte nicht auf Automation-Discovery warten.
+
+## 0.7.13 - 2026-06-16
+- Diagnose-/Schutzsensoren wie Überhitzung und Überlast werden nicht mehr nur
+  wegen gleicher Strom-/Monitoring-Bereiche automatisch als Lichtkontext
+  übernommen.
+- Verwendete Kontext-Entities können pro Aktor direkt entfernt und damit als
+  manuelle Zuordnung überschrieben werden.
+- Onboarding-Vorschläge zeigen passende, noch nicht eingerichtete Aktoren aus
+  bestehenden Automationen und naheliegenden Kontexten.
+- TV-/Medien-Aktoren über `media_player` und Fernbedienungen über `remote`
+  werden in Discovery und Auswahl berücksichtigt.
+
 ## 0.7.12 - 2026-06-16
 - Aktor-Auswahlliste zeigt maximal 50 Treffer gleichzeitig und fordert bei
   größeren Mengen zum Eingrenzen per Suche oder Typfilter auf.

README.md

@@ -11,6 +11,18 @@ nach einer ausdrücklichen Freigabe ausführen.
   [`docs/CONTROL_HANDOFF.md`](docs/CONTROL_HANDOFF.md)
 - Entwickeln, testen, veröffentlichen und installieren:
   [`docs/OPERATIONS.md`](docs/OPERATIONS.md)
+- Version 1.0.0 bedienen und prüfen:
+  [`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)
 - Arbeitsregeln für Coding-Agenten: [`AGENTS.md`](AGENTS.md)
 
 ## Reifegrad
@@ -58,6 +70,8 @@ uvicorn app.main:app --reload
 - `http://127.0.0.1:8000/v1/discovery` - klassifizierte, filterbare Entities
 - `http://127.0.0.1:8000/v1/history` - normalisierte numerische Zeitreihen
 - `http://127.0.0.1:8000/v1/actuators/discovery` - unterstützte Aktuatoren für den aktor-zentrierten Workflow
+- `http://127.0.0.1:8000/v1/actuators/dashboard` - schnelle Dashboard-Startdaten aus Store und JSON-Cache
+- `http://127.0.0.1:8000/v1/actuators/summary` - schlanke Liste beobachteter Aktoren
 - `POST http://127.0.0.1:8000/v1/actuators` - Aktor freigeben; Kontextzuordnung und Modell-Lebenszyklus starten automatisch
 - `POST http://127.0.0.1:8000/v1/actuators/{entity_id}/evaluate` - Shadow-Vorhersage aktualisieren
 - `POST http://127.0.0.1:8000/v1/actuators/{entity_id}/activation` - autonomes Schalten pro Aktor freigeben oder stoppen

addon/config.yaml

@@ -1,5 +1,5 @@
 name: SillyHome Next
-version: "0.7.12"
+version: "1.4.0"
 slug: sillyhome_next
 description: Lernt automatisch aus deinem Verhalten und steuert freigegebene Aktoren
 url: http://192.168.6.31:3000/pino/sillyhome-next
@@ -7,6 +7,7 @@ arch:
   - amd64
 startup: application
 boot: auto
+watchdog: http://[HOST]:[PORT:8000]/health
 init: false
 ingress: true
 ingress_port: 8000

app/actuators/lifecycle.py

@@ -16,11 +16,12 @@ from app.actuators.models import (
     ManualOverride,
     ModelLifecycleState,
     ReconciliationState,
+    SensorWeightGroup,
     model_id_for_actuator,
 )
 from app.actuators.store import ActuatorStore
 from app.config import Settings
-from app.ha.discovery import DiscoveredEntity, EntityRole
+from app.ha.discovery import DiscoveredEntity, EntityRole, discover_entities
 from app.ha.history import EntityHistorySeries, NumericHistoryPoint
 from app.ha.models import HaEntitySummary
 from app.ha.reader import HaReader
@@ -57,7 +58,7 @@ _STOPWORDS = frozenset(
         "value",
     }
 )
-_GENERIC_AREA_NAMES = frozenset({"monitoring", "system", "technik"})
+_GENERIC_AREA_NAMES = frozenset({"energie", "monitoring", "power", "strom", "system", "technik"})
 _NUMERIC_AUTO_ACCEPT_SCORE = 0.82
 _NUMERIC_AUTO_ACCEPT_MIN_SCORE = 0.5
 _NUMERIC_MIN_MARGIN = 0.18
@@ -72,38 +73,71 @@ _MANUAL_CONTEXT_DOMAINS = frozenset({
     "device_tracker",
     "fan",
     "humidifier",
+    "input_boolean",
+    "input_number",
+    "input_select",
     "light",
+    "media_player",
     "person",
+    "remote",
+    "scene",
     "sensor",
+    "sun",
     "switch",
     "weather",
 })
-_CONTEXT_SUGGESTION_LIMIT = 120
+_CONTEXT_SUGGESTION_LIMIT = 500
 _OUTDOOR_TOKENS = frozenset({"aussen", "außen", "outdoor", "garten", "terrasse", "balkon"})
 _DIAGNOSTIC_TOKENS = frozenset({
     "basic",
     "battery",
+    "bytes",
     "connect",
     "count",
+    "data",
     "diagnostic",
     "firmware",
     "gesehen",
+    "heat",
+    "inbytes",
+    "interface",
     "last",
     "linkquality",
     "knoten",
     "knotens",
     "mqtt",
     "node",
+    "outbytes",
+    "pfsense",
     "reason",
     "restart",
     "rssi",
     "signal",
     "ssid",
     "status",
+    "overheat",
+    "overheating",
+    "overload",
     "uptime",
+    "vpn",
+    "uberhitzung",
+    "ueberhitzung",
+    "ueberlast",
+    "überhitzung",
+    "überlast",
     "wifi",
     "zuletzt",
 })
+_AUTO_CONTEXT_CLASSES = frozenset({
+    "door",
+    "garage_door",
+    "illuminance",
+    "motion",
+    "occupancy",
+    "opening",
+    "presence",
+    "window",
+})
 
 
 class ActuatorReconciliationService:
@@ -137,7 +171,7 @@ class ActuatorReconciliationService:
         limit: int = _CONTEXT_SUGGESTION_LIMIT,
     ) -> list[HaEntitySummary]:
         entities = {entity.entity_id: entity for entity in self._ha_reader.read_entities()}
-        discovered = {entity.entity_id: entity for entity in self._ha_reader.discover()}
+        discovered = {entity.entity_id: entity for entity in discover_entities(list(entities.values()))}
         actuator = entities.get(actuator_entity_id)
         if actuator is None:
             raise KeyError("Aktuator-Konfiguration nicht gefunden.")
@@ -156,13 +190,10 @@ class ActuatorReconciliationService:
             selected = entity.entity_id in selected_ids
             if selected:
                 score = max(score, 1.0)
-            if not selected and (
-                _is_diagnostic_context(entity)
-                or not _has_context_relationship(actuator, entity)
-            ):
-                continue
-            if not selected and score < 0.1:
+            if not selected and _is_diagnostic_context(entity):
                 continue
+            if not selected and not _has_context_relationship(actuator, entity):
+                score = max(score, 0.01)
             ranked.append((score, _context_sort_group(entity), entity))
         ranked.sort(
             key=lambda item: (
@@ -209,6 +240,10 @@ class ActuatorReconciliationService:
         override = ManualOverride(
             numeric_entity_id=numeric_entity_id,
             context_entity_ids=selected_context_ids,
+            sensor_weights=record.manual_override.sensor_weights if record.manual_override else {},
+            sensor_weight_groups=(
+                record.manual_override.sensor_weight_groups if record.manual_override else []
+            ),
             updated_at=now,
             note=note,
         )
@@ -223,17 +258,23 @@ class ActuatorReconciliationService:
             update={
                 "assignment": assignment,
                 "manual_override": override,
-                "numeric_candidates": _merge_manual_candidates(
-                    record.numeric_candidates,
-                    entities,
-                    [numeric_entity_id] if numeric_entity_id else [],
-                    role=EntityRole.MEASUREMENT,
+                "numeric_candidates": _apply_weight_overrides(
+                    _merge_manual_candidates(
+                        record.numeric_candidates,
+                        entities,
+                        [numeric_entity_id] if numeric_entity_id else [],
+                        role=EntityRole.MEASUREMENT,
+                    ),
+                    override,
                 ),
-                "context_candidates": _merge_manual_candidates(
-                    record.context_candidates,
-                    entities,
-                    selected_context_ids,
-                    role=EntityRole.CONTEXT,
+                "context_candidates": _apply_weight_overrides(
+                    _merge_manual_candidates(
+                        record.context_candidates,
+                        entities,
+                        selected_context_ids,
+                        role=EntityRole.CONTEXT,
+                    ),
+                    override,
                 ),
                 "lifecycle": lifecycle,
                 "updated_at": now,
@@ -241,6 +282,65 @@ class ActuatorReconciliationService:
         )
         return self._store.upsert(updated)
 
+    def set_weight_overrides(
+        self,
+        actuator_entity_id: str,
+        *,
+        sensor_weights: dict[str, float],
+        sensor_weight_groups: list[SensorWeightGroup],
+        note: str | None = None,
+    ) -> ActuatorRecord:
+        now = datetime.now(timezone.utc)
+        record = self._store.get(actuator_entity_id)
+        selected_ids = {
+            entity_id
+            for entity_id in [
+                record.assignment.selected_numeric_entity_id,
+                *record.assignment.selected_context_entity_ids,
+            ]
+            if entity_id
+        }
+        selected_ids.update(sensor_weights)
+        for group in sensor_weight_groups:
+            selected_ids.update(group.entity_ids)
+        entities = {entity.entity_id: entity for entity in self._ha_reader.read_entities()}
+        missing = [entity_id for entity_id in selected_ids if entity_id not in entities]
+        if missing:
+            raise ValueError(f"Unbekannte Home-Assistant-Entity: {', '.join(sorted(missing))}")
+
+        previous = record.manual_override
+        override = 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={entity_id: round(weight, 4) for entity_id, weight in sensor_weights.items()},
+            sensor_weight_groups=sensor_weight_groups,
+            updated_at=now,
+            note=note,
+        )
+        updated = record.model_copy(
+            update={
+                "manual_override": override,
+                "numeric_candidates": _apply_weight_overrides(
+                    record.numeric_candidates,
+                    override,
+                ),
+                "context_candidates": _apply_weight_overrides(
+                    record.context_candidates,
+                    override,
+                ),
+                "updated_at": now,
+            }
+        )
+        return self._store.upsert(updated)
+
     def reconcile_all(self, trigger: str = "manual") -> ReconciliationState:
         state = self._store.load_reconciliation_state().model_copy(
             update={
@@ -346,6 +446,9 @@ class ActuatorReconciliationService:
             ),
             context=True,
         )
+        if record.manual_override is not None:
+            numeric_candidates = _apply_weight_overrides(numeric_candidates, record.manual_override)
+            context_candidates = _apply_weight_overrides(context_candidates, record.manual_override)
         assignment = (
             self._manual_assignment(record.manual_override)
             if record.manual_override is not None
@@ -628,8 +731,12 @@ class ActuatorReconciliationService:
                 if context
                 else _NUMERIC_AUTO_ACCEPT_MIN_SCORE
             )
+            can_auto_accept_context = (
+                not context or _eligible_for_auto_context(actuator, candidate)
+            )
             auto_accepted = (
-                candidate.score >= minimum_score
+                can_auto_accept_context
+                and candidate.score >= minimum_score
                 and confidence >= auto_score
                 and (context or margin >= _NUMERIC_MIN_MARGIN)
             )
@@ -702,6 +809,11 @@ def _manual_context_role(
 
 def _context_sort_group(entity: HaEntitySummary) -> str:
     device_class = entity.device_class or ""
+    text = " ".join(
+        value.lower().replace("_", " ")
+        for value in [entity.entity_id, entity.friendly_name, entity.area_name, entity.device_name]
+        if value
+    )
     if device_class in {"motion", "occupancy", "presence"}:
         return "01_presence"
     if device_class in {"illuminance"}:
@@ -710,10 +822,26 @@ def _context_sort_group(entity: HaEntitySummary) -> str:
         return "03_opening"
     if device_class in {"humidity", "moisture"}:
         return "04_humidity"
+    if device_class in {"temperature"}:
+        return "05_temperature"
+    if any(token in text for token in {"pv", "solar", "akku", "batterie", "battery", "einspeisung"}):
+        return "06_pv_battery"
     if device_class in {"power", "energy", "current", "voltage"}:
-        return "05_power"
+        return "07_power"
+    if entity.domain in {"weather"}:
+        return "08_weather"
+    if entity.domain in {"fan", "humidifier"}:
+        return "09_ventilation"
+    if entity.domain in {"climate"}:
+        return "10_heating"
+    if entity.domain in {"cover"}:
+        return "11_cover"
     if entity.domain in {"light", "switch"}:
-        return "06_states"
+        return "12_states"
+    if entity.domain.startswith("input_"):
+        return "13_helper"
+    if entity.domain in {"person", "device_tracker"}:
+        return "14_people"
     return f"20_{entity.domain}_{device_class}"
 
 
@@ -743,6 +871,23 @@ def _has_context_relationship(actuator: HaEntitySummary, entity: HaEntitySummary
     )
 
 
+def _eligible_for_auto_context(
+    actuator: HaEntitySummary,
+    candidate: AssignmentCandidate,
+) -> bool:
+    device_class = candidate.device_class or ""
+    if device_class in _AUTO_CONTEXT_CLASSES:
+        return True
+    if (
+        actuator.device_name
+        and candidate.device_name
+        and actuator.device_name == candidate.device_name
+        and candidate.domain in {"light", "switch"}
+    ):
+        return True
+    return False
+
+
 def _score_candidate(
     actuator: HaEntitySummary,
     entity: HaEntitySummary,
@@ -809,12 +954,17 @@ def _merge_manual_candidates(
     for entity_id in selected_entity_ids:
         existing = by_id.get(entity_id)
         if existing is not None:
+            evidence = [
+                item
+                for item in existing.evidence
+                if item != "Manuell vom Nutzer als relevant festgelegt."
+            ]
             by_id[entity_id] = existing.model_copy(
                 update={
                     "auto_accepted": True,
                     "confidence": 1.0,
                     "evidence": [
-                        *existing.evidence,
+                        *evidence,
                         "Manuell vom Nutzer als relevant festgelegt.",
                     ],
                 }
@@ -841,15 +991,66 @@ def _merge_manual_candidates(
     return sorted(by_id.values(), key=lambda item: (-item.confidence, item.entity_id))
 
 
+def _apply_weight_overrides(
+    candidates: list[AssignmentCandidate],
+    override: ManualOverride,
+) -> list[AssignmentCandidate]:
+    if not override.sensor_weights and not override.sensor_weight_groups:
+        return candidates
+    group_weights: dict[str, float] = {}
+    for group in override.sensor_weight_groups:
+        for entity_id in group.entity_ids:
+            group_weights[entity_id] = max(group_weights.get(entity_id, 0.0), group.weight)
+    weighted: list[AssignmentCandidate] = []
+    for candidate in candidates:
+        explicit = override.sensor_weights.get(candidate.entity_id)
+        group_weight = group_weights.get(candidate.entity_id)
+        manual_weight = explicit if explicit is not None else group_weight
+        effective_weight = manual_weight if manual_weight is not None else 1.0
+        evidence = [
+            item
+            for item in candidate.evidence
+            if not item.startswith("Manuelle Gewichtung:")
+        ]
+        if manual_weight is not None:
+            evidence.append(f"Manuelle Gewichtung: {round(manual_weight * 100)} %.")
+        weighted.append(
+            candidate.model_copy(
+                update={
+                    "manual_weight": manual_weight,
+                    "effective_weight": round(effective_weight, 4),
+                    "evidence": evidence,
+                }
+            )
+        )
+    return sorted(weighted, key=lambda item: (-item.confidence * item.effective_weight, item.entity_id))
+
+
 def _preferred_device_classes(domain: str, *, context: bool) -> frozenset[str]:
     if context:
-        return frozenset({"door", "garage_door", "motion", "occupancy", "opening", "presence"})
+        mapping = {
+            "climate": {"humidity", "illuminance", "occupancy", "presence", "temperature", "window"},
+            "cover": {"illuminance", "motion", "occupancy", "presence", "wind_speed"},
+            "fan": {"humidity", "moisture", "occupancy", "presence", "temperature"},
+            "humidifier": {"humidity", "moisture", "temperature"},
+            "light": {"door", "garage_door", "illuminance", "motion", "occupancy", "opening", "presence", "window"},
+            "media_player": {"occupancy", "presence"},
+            "switch": {"door", "garage_door", "motion", "occupancy", "opening", "presence", "window"},
+        }
+        return frozenset(
+            mapping.get(
+                domain,
+                {"door", "garage_door", "motion", "occupancy", "opening", "presence"},
+            )
+        )
     mapping = {
-        "climate": {"temperature", "humidity", "power"},
+        "climate": {"temperature", "humidity"},
         "cover": {"illuminance", "temperature", "wind_speed"},
-        "fan": {"temperature", "humidity", "power"},
-        "humidifier": {"humidity", "temperature", "power"},
-        "light": {"illuminance", "power", "energy"},
+        "fan": {"temperature", "humidity", "moisture"},
+        "humidifier": {"humidity", "moisture", "temperature"},
+        "light": {"illuminance"},
+        "media_player": {"power", "energy"},
+        "remote": {"battery"},
         "switch": {"power", "energy", "current"},
         "valve": {"temperature", "pressure", "humidity"},
     }

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
@@ -49,6 +64,8 @@ class AssignmentCandidate(BaseModel):
     device_name: str | None = None
     score: float = Field(ge=0.0)
     confidence: float = Field(ge=0.0, le=1.0)
+    manual_weight: float | None = Field(default=None, ge=0.0, le=1.0)
+    effective_weight: float = Field(default=1.0, ge=0.0, le=1.0)
     auto_accepted: bool = False
     evidence: list[str] = Field(default_factory=list)
 
@@ -62,9 +79,18 @@ class AssignmentSelection(BaseModel):
     reason: str = "Noch keine Zuordnung vorhanden."
 
 
+class SensorWeightGroup(BaseModel):
+    group_id: str = Field(pattern=r"^[a-z0-9_-]{1,64}$")
+    name: str = Field(min_length=1, max_length=120)
+    entity_ids: list[str] = Field(default_factory=list)
+    weight: float = Field(default=1.0, ge=0.0, le=1.0)
+
+
 class ManualOverride(BaseModel):
     numeric_entity_id: str | None = None
     context_entity_ids: list[str] = Field(default_factory=list)
+    sensor_weights: dict[str, float] = Field(default_factory=dict)
+    sensor_weight_groups: list[SensorWeightGroup] = Field(default_factory=list)
     updated_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
     note: str | None = None
 
@@ -110,11 +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)
@@ -139,6 +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):
@@ -165,5 +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

@@ -1,14 +1,21 @@
 from __future__ import annotations
 
+import json
+import os
+from datetime import datetime, timezone
+from pathlib import Path
+
 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
+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
 from app.dependencies import get_ha_reader
-from app.ha.discovery import EntityRole
+from app.ha.discovery import DiscoveredEntity, EntityRole, discover_entities
 from app.ha.exceptions import HaClientError
 from app.ha.models import HaEntitySummary
 from app.ha.reader import HaReader
@@ -38,15 +45,124 @@ class ManualAssignmentRequest(BaseModel):
     note: str | None = Field(default=None, max_length=500)
 
 
+class WeightOverrideRequest(BaseModel):
+    sensor_weights: dict[str, float] = Field(default_factory=dict)
+    sensor_weight_groups: list[SensorWeightGroup] = Field(default_factory=list)
+    note: str | None = Field(default=None, max_length=500)
+
+
 class FeedbackRequest(BaseModel):
     correct: bool
     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
+    friendly_name: str | None = None
+    area_name: str | None = None
+    device_name: str | None = None
+    confidence: float
+    reason: str
+    related_automation_count: int = 0
+    likely_context_count: int = 0
+
+
+class ActuatorSummary(BaseModel):
+    actuator_entity_id: str
+    domain: str
+    friendly_name: str | None = None
+    area_name: str | None = None
+    device_name: str | None = None
+    enabled: bool
+    behavior_mode: str
+    behavior_status: str
+    lifecycle_status: str
+    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
+
+
+class EntityCacheStatus(BaseModel):
+    available: bool
+    updated_at: str | None = None
+    entity_count: int = 0
+
+
+class DashboardSystemStatus(BaseModel):
+    api_status: str = "ok"
+    websocket_status: str = "unavailable"
+    websocket_error: str | None = None
+    reconciliation_last_completed_at: str | None = None
+    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):
+    category: str
+    role: str
+    count: int
+
+
+class DashboardOverview(BaseModel):
+    system: DashboardSystemStatus
+    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])
-def discover_actuators(ha_reader: HaReader = Depends(get_ha_reader)) -> list[HaEntitySummary]:
-    entities = {entity.entity_id: entity for entity in ha_reader.read_entities()}
-    discovered = ha_reader.discover()
+def discover_actuators(
+    request: Request,
+    refresh: bool = Query(default=False),
+    ha_reader: HaReader = Depends(get_ha_reader),
+) -> list[HaEntitySummary]:
+    cached_entities = [] if refresh else _load_cached_entities(request)
+    if cached_entities:
+        entities = {entity.entity_id: entity for entity in cached_entities}
+    else:
+        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(
         [
             (entity.entity_id, entity.category)
@@ -58,6 +174,66 @@ def discover_actuators(ha_reader: HaReader = Depends(get_ha_reader)) -> list[HaE
     return [entities[entity_id] for entity_id in actuator_ids if entity_id in entities]
 
 
+@router.get("/suggestions", response_model=list[ActuatorSuggestion])
+def suggest_actuators(
+    request: Request,
+    ha_reader: HaReader = Depends(get_ha_reader),
+) -> list[ActuatorSuggestion]:
+    entities = {entity.entity_id: entity for entity in ha_reader.read_entities()}
+    discovered = {entity.entity_id: entity for entity in discover_entities(list(entities.values()))}
+    configured_ids = {record.actuator_entity_id for record in _service(request).list_configured()}
+    actuator_ids = _deduplicate_actuator_ids(
+        [
+            (entity.entity_id, entity.category)
+            for entity in discovered.values()
+            if entity.role is EntityRole.ACTUATOR
+        ],
+        entities,
+    )
+    suggestions: list[ActuatorSuggestion] = []
+    for entity_id in actuator_ids:
+        if entity_id in configured_ids:
+            continue
+        entity = entities.get(entity_id)
+        if entity is None:
+            continue
+        try:
+            automations = ha_reader.find_automations_for_entity(entity_id)
+        except Exception:
+            automations = []
+        context_count = _likely_context_count(entity, entities, discovered)
+        if not automations and context_count == 0:
+            continue
+        confidence = 1.0 if automations else min(0.85, 0.35 + context_count * 0.1)
+        reason_parts = []
+        if automations:
+            reason_parts.append(f"{len(automations)} passende HA-Automation(en)")
+        if context_count:
+            reason_parts.append(f"{context_count} naheliegende Kontext-Entity(s)")
+        suggestions.append(
+            ActuatorSuggestion(
+                entity_id=entity.entity_id,
+                domain=entity.domain,
+                friendly_name=entity.friendly_name,
+                area_name=entity.area_name,
+                device_name=entity.device_name,
+                confidence=round(confidence, 4),
+                reason=", ".join(reason_parts),
+                related_automation_count=len(automations),
+                likely_context_count=context_count,
+            )
+        )
+    return sorted(
+        suggestions,
+        key=lambda item: (
+            -item.related_automation_count,
+            -item.confidence,
+            item.area_name or "",
+            item.friendly_name or item.entity_id,
+        ),
+    )[:30]
+
+
 @router.get("/context-options", response_model=list[HaEntitySummary])
 def context_options(
     request: Request,
@@ -71,6 +247,150 @@ def context_options(
         raise HTTPException(status_code=404, detail=str(exc)) from exc
 
 
+@router.get("/summary", response_model=list[ActuatorSummary])
+def list_configured_summary(request: Request) -> list[ActuatorSummary]:
+    records = _service(request).list_configured()
+    entity_map = _load_cached_entity_map(
+        request,
+        {record.actuator_entity_id for record in records},
+    )
+    return [
+        ActuatorSummary(
+            actuator_entity_id=record.actuator_entity_id,
+            domain=record.actuator_entity_id.split(".", 1)[0],
+            friendly_name=(
+                entity_map[record.actuator_entity_id].friendly_name
+                if record.actuator_entity_id in entity_map
+                else None
+            ),
+            area_name=(
+                entity_map[record.actuator_entity_id].area_name
+                if record.actuator_entity_id in entity_map
+                else None
+            ),
+            device_name=(
+                entity_map[record.actuator_entity_id].device_name
+                if record.actuator_entity_id in entity_map
+                else None
+            ),
+            enabled=record.enabled,
+            behavior_mode=record.behavior.mode.value,
+            behavior_status=record.behavior.status.value,
+            lifecycle_status=record.lifecycle.status.value,
+            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
+                else None
+            ),
+            prediction_confidence=(
+                record.behavior.prediction.confidence
+                if record.behavior.prediction is not None
+                else None
+            ),
+            updated_at=record.updated_at.isoformat(),
+        )
+        for record in records
+    ]
+
+
+@router.get("/dashboard", response_model=DashboardOverview)
+def dashboard_overview(request: Request) -> DashboardOverview:
+    return _dashboard_overview(request, include_background=True)
+
+
+@router.get("/dashboard/start", response_model=DashboardOverview)
+def dashboard_start(request: Request) -> DashboardOverview:
+    return _dashboard_overview(request, include_background=False)
+
+
+def _dashboard_overview(request: Request, *, include_background: bool) -> DashboardOverview:
+    cache_payload = _load_entity_cache_payload(request)
+    raw_entities = cache_payload.get("entities", [])
+    if not isinstance(raw_entities, list):
+        raw_entities = []
+    raw_updated_at = cache_payload.get("updated_at")
+    updated_at = raw_updated_at if isinstance(raw_updated_at, str) else None
+    raw_groups = cache_payload.get("discovery_groups", [])
+    cached_groups = [
+        DashboardDiscoveryGroup.model_validate(group)
+        for group in raw_groups
+        if isinstance(group, dict)
+    ] 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)
+    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"),
+            websocket_error=getattr(ws_status, "error", None),
+            reconciliation_last_completed_at=(
+                reconciliation.last_completed_at.isoformat()
+                if reconciliation.last_completed_at is not None
+                else None
+            ),
+            configured_actuators=len(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),
+            updated_at=updated_at,
+            entity_count=len(raw_entities),
+        ),
+        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()
@@ -142,6 +462,32 @@ 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,
@@ -182,6 +528,27 @@ def set_manual_assignment(
         raise HTTPException(status_code=422, detail=str(exc)) from exc
 
 
+@router.post("/{actuator_entity_id}/weights", response_model=ActuatorRecord)
+def set_weight_overrides(
+    actuator_entity_id: str,
+    payload: WeightOverrideRequest,
+    request: Request,
+) -> ActuatorRecord:
+    try:
+        _validate_weight_payload(payload)
+        record = _service(request).set_weight_overrides(
+            actuator_entity_id,
+            sensor_weights=payload.sensor_weights,
+            sensor_weight_groups=payload.sensor_weight_groups,
+            note=payload.note,
+        )
+        return record
+    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}/related-automations/refresh",
     response_model=ActuatorRecord,
@@ -190,11 +557,27 @@ 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
 
 
@@ -235,12 +618,105 @@ 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):
@@ -261,6 +737,111 @@ def _behavior(request: Request) -> BehaviorEngine:
     return engine
 
 
+def _validate_weight_payload(payload: WeightOverrideRequest) -> None:
+    for entity_id, weight in payload.sensor_weights.items():
+        if "." not in entity_id:
+            raise ValueError(f"Ungültige Entity-ID: {entity_id}")
+        if not 0.0 <= weight <= 1.0:
+            raise ValueError(f"Ungültige Gewichtung für {entity_id}: {weight}")
+    for group in payload.sensor_weight_groups:
+        if not group.entity_ids:
+            raise ValueError(f"Gruppe {group.name} enthält keine Entities.")
+        for entity_id in group.entity_ids:
+            if "." not in entity_id:
+                raise ValueError(f"Ungültige Entity-ID in Gruppe {group.name}: {entity_id}")
+
+
+def _reconciliation_state_or_default(request: Request) -> ReconciliationState:
+    store = getattr(request.app.state, "actuator_store", None)
+    if not isinstance(store, ActuatorStore):
+        return ReconciliationState()
+    try:
+        return store.load_reconciliation_state()
+    except ValueError:
+        return ReconciliationState(last_summary="Reconciliation-Status ist unlesbar.")
+
+
+def _entity_cache_path(request: Request) -> Path:
+    store = getattr(request.app.state, "actuator_store", None)
+    settings = getattr(request.app.state, "settings", None)
+    if isinstance(store, ActuatorStore):
+        base_dir = store._root
+    elif isinstance(settings, Settings):
+        base_dir = Path(settings.actuator_store).resolve().parent
+    else:
+        base_dir = Path(".").resolve()
+    return Path(os.getenv("SILLYHOME_ENTITY_CACHE", base_dir / "ha_entity_cache.json"))
+
+
+def _load_cached_entities(request: Request) -> list[HaEntitySummary]:
+    payload = _load_entity_cache_payload(request)
+    raw_entities = payload.get("entities", [])
+    if not isinstance(raw_entities, list):
+        return []
+    try:
+        return [HaEntitySummary.model_validate(entity) for entity in raw_entities]
+    except ValueError:
+        return []
+
+
+def _load_cached_entity_map(
+    request: Request,
+    entity_ids: set[str],
+) -> dict[str, HaEntitySummary]:
+    if not entity_ids:
+        return {}
+    payload = _load_entity_cache_payload(request)
+    raw_entities = payload.get("entities", [])
+    if not isinstance(raw_entities, list):
+        return {}
+    result: dict[str, HaEntitySummary] = {}
+    for raw_entity in raw_entities:
+        if not isinstance(raw_entity, dict):
+            continue
+        entity_id = raw_entity.get("entity_id")
+        if not isinstance(entity_id, str) or entity_id not in entity_ids:
+            continue
+        try:
+            result[entity_id] = HaEntitySummary.model_validate(raw_entity)
+        except ValueError:
+            continue
+    return result
+
+
+def _load_entity_cache_payload(request: Request) -> dict[str, object]:
+    path = _entity_cache_path(request)
+    if not path.exists():
+        return {}
+    try:
+        payload = json.loads(path.read_text(encoding="utf-8"))
+        return payload if isinstance(payload, dict) else {}
+    except (OSError, TypeError, ValueError):
+        return {}
+
+
+def _save_cached_entities(request: Request, entities: list[HaEntitySummary]) -> None:
+    path = _entity_cache_path(request)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    group_counts: dict[tuple[str, str], int] = {}
+    for entity in discover_entities(entities):
+        key = (entity.category, entity.role.value)
+        group_counts[key] = group_counts.get(key, 0) + 1
+    payload = {
+        "updated_at": datetime.now(timezone.utc).isoformat(),
+        "discovery_groups": [
+            {"category": category, "role": role, "count": count}
+            for (category, role), count in sorted(group_counts.items())
+        ],
+        "entities": [entity.model_dump(mode="json") for entity in entities],
+    }
+    temporary = path.with_suffix(".json.tmp")
+    temporary.write_text(
+        json.dumps(payload, ensure_ascii=True, sort_keys=True) + "\n",
+        encoding="utf-8",
+    )
+    os.replace(temporary, path)
+
+
 def _deduplicate_actuator_ids(
     discovered: list[tuple[str, str]],
     entities: dict[str, HaEntitySummary],
@@ -294,3 +875,67 @@ def _actuator_duplicate_key(entity: HaEntitySummary, category: str) -> str:
     if entity.device_name and category in {"light", "switch_socket", "button"}:
         return f"device-name:{entity.device_name.lower()}:control"
     return f"entity:{entity.entity_id}"
+
+
+def _likely_context_count(
+    actuator: HaEntitySummary,
+    entities: dict[str, HaEntitySummary],
+    discovered: dict[str, DiscoveredEntity],
+) -> int:
+    actuator_tokens = _tokens(actuator)
+    count = 0
+    for entity in entities.values():
+        if entity.entity_id == actuator.entity_id:
+            continue
+        descriptor = discovered.get(entity.entity_id)
+        role = descriptor.role if descriptor is not None else None
+        if role not in {
+            EntityRole.MEASUREMENT,
+            EntityRole.BINARY_CONTEXT,
+            EntityRole.CONTEXT,
+        }:
+            continue
+        if entity.device_class not in {
+            "door",
+            "energy",
+            "garage_door",
+            "humidity",
+            "illuminance",
+            "motion",
+            "occupancy",
+            "opening",
+            "power",
+            "presence",
+            "temperature",
+            "window",
+        }:
+            continue
+        same_area = bool(
+            actuator.area_name
+            and entity.area_name
+            and actuator.area_name == entity.area_name
+        )
+        same_device = bool(
+            actuator.device_id
+            and entity.device_id
+            and actuator.device_id == entity.device_id
+        )
+        token_match = bool(actuator_tokens.intersection(_tokens(entity)))
+        if same_area or same_device or token_match:
+            count += 1
+    return count
+
+
+def _tokens(entity: HaEntitySummary) -> set[str]:
+    values = [
+        entity.entity_id,
+        entity.friendly_name,
+        entity.area_name,
+        entity.device_name,
+    ]
+    tokens: set[str] = set()
+    for value in values:
+        if not value:
+            continue
+        tokens.update(token for token in value.lower().replace("_", " ").split() if len(token) > 2)
+    return tokens

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
@@ -80,6 +89,16 @@ 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 +139,16 @@ 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 +194,7 @@ 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 +205,32 @@ 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 +324,25 @@ 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 +352,31 @@ 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 +481,8 @@ 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 +511,13 @@ 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 +528,68 @@ 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 +608,24 @@ 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 +693,9 @@ 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 +776,9 @@ 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 +823,51 @@ 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 +918,8 @@ 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 +927,9 @@ 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(
@@ -791,6 +1012,378 @@ 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,
+        reason=reason,
+    )
+    return [*existing, snapshot][-10:]
+
+
+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],
     *,
@@ -898,8 +1491,10 @@ def predict_behavior(
 
 
 def service_for_state(domain: str, target_state: str) -> str | None:
-    if domain in {"fan", "humidifier", "light", "switch"}:
+    if domain in {"fan", "humidifier", "light", "media_player", "remote", "switch"}:
         return {"on": "turn_on", "off": "turn_off"}.get(target_state)
+    if domain == "scene":
+        return "turn_on" if target_state == "on" else None
     if domain == "cover":
         return {"open": "open_cover", "closed": "close_cover"}.get(target_state)
     return None

app/ha/discovery.py

@@ -92,7 +92,10 @@ _ACTUATOR_DOMAINS = frozenset({
     "input_button",
     "lock",
     "light",
+    "media_player",
     "number",
+    "remote",
+    "scene",
     "siren",
     "switch",
     "valve",
@@ -211,9 +214,12 @@ def _result(
 
 
 def _actuator_category(entity: HaEntitySummary) -> str:
+    text = _entity_text(entity)
     if entity.domain == "light":
         return "light"
     if entity.domain == "switch":
+        if any(token in text for token in {"steckdose", "socket", "plug", "outlet", "shelly"}):
+            return "socket"
         return "switch_socket"
     if entity.domain == "button" or entity.domain == "input_button":
         return "button"
@@ -225,6 +231,10 @@ def _actuator_category(entity: HaEntitySummary) -> str:
         return "lock"
     if entity.domain == "fan":
         return "fan"
+    if entity.domain in {"media_player", "remote"}:
+        return "media_tv"
+    if entity.domain == "scene":
+        return "scene"
     if entity.domain in {"input_boolean", "number"}:
         return "helper"
     return entity.domain
@@ -232,13 +242,31 @@ def _actuator_category(entity: HaEntitySummary) -> str:
 
 def _measurement_category(entity: HaEntitySummary) -> str:
     device_class = entity.device_class or ""
+    text = _entity_text(entity)
+    if any(
+        token in text
+        for token in {
+            "pv",
+            "solar",
+            "photovoltaik",
+            "akku",
+            "batterie",
+            "battery",
+            "einspeisung",
+            "wechselrichter",
+            "inverter",
+        }
+    ):
+        return "pv_battery_grid"
+    if entity.domain == "weather":
+        return "weather"
     if device_class == "illuminance":
         return "brightness"
     if device_class == "temperature":
         return "temperature"
     if device_class in {"humidity", "moisture"}:
         return "humidity"
-    if device_class in {"power", "energy", "current", "voltage"}:
+    if device_class in {"power", "energy", "current", "voltage", "apparent_power"}:
         return "energy_power"
     if device_class in {"battery", "signal_strength"}:
         return "diagnostic"
@@ -253,12 +281,42 @@ def _binary_category(entity: HaEntitySummary) -> str:
         return "opening"
     if device_class in {"smoke", "safety", "problem"}:
         return "safety"
+    if device_class in {"lock"}:
+        return "lock_state"
     return "binary"
 
 
 def _context_category(entity: HaEntitySummary) -> str:
+    text = _entity_text(entity)
     if entity.domain.startswith("input_"):
         return "helper"
     if entity.domain in {"person", "device_tracker", "zone"}:
         return "presence_location"
+    if entity.domain == "weather":
+        return "weather"
+    if entity.domain in {"light"}:
+        return "light_state"
+    if entity.domain in {"switch"}:
+        if any(token in text for token in {"steckdose", "socket", "plug", "outlet", "shelly"}):
+            return "socket_state"
+        return "switch_state"
+    if entity.domain in {"climate"}:
+        return "heating_state"
+    if entity.domain in {"fan", "humidifier"}:
+        return "ventilation_state"
+    if entity.domain in {"cover"}:
+        return "cover_state"
     return entity.domain
+
+
+def _entity_text(entity: HaEntitySummary) -> str:
+    return " ".join(
+        value.lower().replace("_", " ")
+        for value in [
+            entity.entity_id,
+            entity.friendly_name,
+            entity.area_name,
+            entity.device_name,
+        ]
+        if value
+    )

app/main.py

@@ -43,6 +43,7 @@ class _WsStatus:
 async def lifespan(app: FastAPI) -> AsyncIterator[None]:
     settings = app.state.settings
     client: HaClient | None = None
+    startup_task: asyncio.Task[None] | None = None
     reconcile_task: asyncio.Task[None] | None = None
     event_listener_task: asyncio.Task[None] | None = None
     fallback_task: asyncio.Task[None] | None = None
@@ -74,15 +75,17 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
             settings=settings,
         )
         app.state.ws_status = _WsStatus()
-        await asyncio.to_thread(app.state.actuator_service.reconcile_all, "startup")
-        await asyncio.to_thread(app.state.behavior_engine.train_all)
-        await asyncio.to_thread(app.state.behavior_engine.evaluate_all)
+        startup_task = asyncio.create_task(_startup_reconciliation(app))
         reconcile_task = asyncio.create_task(_periodic_reconciliation(app))
         event_listener_task = asyncio.create_task(_ha_event_listener(app, client))
         fallback_task = asyncio.create_task(_fallback_prediction(app))
     try:
         yield
     finally:
+        if startup_task is not None:
+            startup_task.cancel()
+            with suppress(asyncio.CancelledError):
+                await startup_task
         if reconcile_task is not None:
             reconcile_task.cancel()
             with suppress(asyncio.CancelledError):
@@ -102,7 +105,7 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
 app = FastAPI(
     title="SillyHome Next API",
     description="Lokales Smart-Home-Intelligenzsystem für Home Assistant.",
-    version="0.7.12",
+    version="1.4.0",
     lifespan=lifespan,
 )
 app.state.settings = load_settings()
@@ -147,10 +150,39 @@ async def _periodic_reconciliation(app: FastAPI) -> None:
         service = getattr(app.state, "actuator_service", None)
         if not isinstance(service, ActuatorReconciliationService):
             continue
-        await asyncio.to_thread(service.reconcile_all, "scheduled")
+        try:
+            await asyncio.to_thread(service.reconcile_all, "scheduled")
+            engine = getattr(app.state, "behavior_engine", None)
+            if isinstance(engine, BehaviorEngine):
+                await asyncio.to_thread(engine.train_all)
+        except Exception:
+            logger.exception("Geplante Reconciliation fehlgeschlagen; nächster Lauf versucht es erneut.")
+
+
+async def _startup_reconciliation(app: FastAPI) -> None:
+    delay_seconds = 5
+    while True:
+        service = getattr(app.state, "actuator_service", None)
         engine = getattr(app.state, "behavior_engine", None)
-        if isinstance(engine, BehaviorEngine):
+        if not isinstance(service, ActuatorReconciliationService) or not isinstance(
+            engine,
+            BehaviorEngine,
+        ):
+            return
+        try:
+            await asyncio.to_thread(service.reconcile_all, "startup")
             await asyncio.to_thread(engine.train_all)
+            await asyncio.to_thread(engine.evaluate_all)
+            logger.info("Startup-Reconciliation erfolgreich abgeschlossen.")
+            return
+        except Exception as exc:
+            logger.warning(
+                "Startup-Reconciliation verschoben: %s. Neuer Versuch in %ss.",
+                exc,
+                delay_seconds,
+            )
+            await asyncio.sleep(delay_seconds)
+            delay_seconds = min(delay_seconds * 2, 60)
 
 
 async def _ha_event_listener(app: FastAPI, client: HaClient) -> None:
@@ -179,7 +211,11 @@ async def _ha_event_listener(app: FastAPI, client: HaClient) -> None:
         if ws_status is not None:
             ws_status.status = "connecting"
         try:
-            async with websockets.connect(ws_url, ping_interval=None) as websocket:
+            async with websockets.connect(
+                ws_url,
+                ping_interval=20,
+                ping_timeout=10,
+            ) as websocket:
                 auth_required_msg = await websocket.recv()
                 auth_required_data = json.loads(auth_required_msg)
                 if auth_required_data.get("type") != "auth_required":
@@ -231,6 +267,8 @@ async def _ha_event_listener(app: FastAPI, client: HaClient) -> None:
                             continue
                         new_state = event_data.get("new_state")
                         _update_ha_state_cache(state_cache, entity_id, new_state)
+                        if not _is_relevant_state_change(store, str(entity_id)):
+                            continue
                         # Prüfe, ob Entity ein Aktor oder relevanter Kontext ist
                         # Sofortige Vorhersage für betroffene Aktoren auslösen
                         await asyncio.to_thread(
@@ -243,18 +281,22 @@ async def _ha_event_listener(app: FastAPI, client: HaClient) -> None:
                         logger.warning("Ungültige JSON-Nachricht von HA-WebSocket")
                     except Exception as exc:
                         logger.exception("Fehler bei Event-Verarbeitung: %s", exc)
-        except (websockets.exceptions.ConnectionClosed, OSError) as exc:
-            logger.warning("WebSocket-Verbindung unterbrochen: %s. Wiederholung in 5s...", exc)
+        except (
+            websockets.exceptions.ConnectionClosed,
+            websockets.exceptions.InvalidStatus,
+            OSError,
+        ) as exc:
+            logger.warning("WebSocket-Verbindung unterbrochen: %s. Wiederholung in 1s...", exc)
             if ws_status is not None:
                 ws_status.status = "reconnecting"
                 ws_status.error = str(exc)
-            await asyncio.sleep(5)
+            await asyncio.sleep(1)
         except Exception as exc:
             logger.exception("Unerwarteter Fehler im Event-Listener: %s", exc)
             if ws_status is not None:
                 ws_status.status = "error"
                 ws_status.error = str(exc)
-            await asyncio.sleep(5)
+            await asyncio.sleep(1)
 
 
 # Fallback: periodische Vorhersage falls Event-Stream ausfällt
@@ -280,7 +322,10 @@ async def _fallback_prediction(app: FastAPI) -> None:
                     "Fallback-Vorhersage aktiv (WebSocket-Status: %s)",
                     ws_status.status if ws_status else "unavailable",
                 )
-                await asyncio.to_thread(engine.evaluate_all)
+                try:
+                    await asyncio.to_thread(engine.evaluate_all)
+                except Exception:
+                    logger.exception("Fallback-Vorhersage fehlgeschlagen.")
 
 
 def _load_ha_state_cache(reader: HaReader) -> dict[str, HaEntitySummary]:
@@ -302,6 +347,17 @@ def _update_ha_state_cache(
     )
 
 
+def _is_relevant_state_change(store: ActuatorStore, entity_id: str) -> bool:
+    for record in store.list():
+        if record.actuator_entity_id == entity_id:
+            return True
+        if record.assignment.selected_numeric_entity_id == entity_id:
+            return True
+        if entity_id in record.assignment.selected_context_entity_ids:
+            return True
+    return False
+
+
 def _ha_entity_from_event(
     entity_id: str,
     new_state: dict[str, object],

docs/V1_0_0_OPERATING_GUIDE.md

@@ -0,0 +1,126 @@
+# SillyHome Next 1.0.0 Operating Guide
+
+Diese Version stabilisiert den produktiven Kern: schnelle Dashboard-Nutzung,
+lokales Caching, klare Aktor-/Sensor-Kategorien und nachvollziehbare Freigabe
+gelernter Aktionen.
+
+Die detaillierte Abnahme steht in
+[`V1_0_ACCEPTANCE.md`](V1_0_ACCEPTANCE.md). Dort sind erledigte, teilweise
+erledigte und fuer v1.0.x offene Punkte getrennt dokumentiert.
+
+## Grundprinzip
+
+- Home Assistant bleibt die Quelle fuer aktuelle States und Services.
+- SillyHome cached schwere Entity-/Discovery-Metadaten lokal als JSON.
+- Die Startansicht liest nur lokale Store-/Cache-Daten.
+- Vollstaendige Discovery, Vorschlaege und Detailanalysen laden blockweise nach.
+- Es gibt keine externen Pings oder Cloud-Abfragen im Dashboard-Startpfad.
+
+## Wichtige Endpunkte
+
+- `GET /health`  
+  Lokaler API-Status ohne externe Abfrage.
+- `GET /health/websocket`  
+  Status des Home-Assistant-WebSocket-Listeners.
+- `GET /v1/actuators/dashboard`  
+  Schnelle Dashboard-Startdaten aus Store und JSON-Cache.
+- `GET /v1/actuators/summary`  
+  Schlanke Liste beobachteter Aktoren ohne Lernmuster-Payload.
+- `GET /v1/actuators/discovery`  
+  Aktor-Auswahl aus gecachten oder frisch geladenen HA-Entities.
+- `GET /v1/actuators/context-options?actuator_entity_id=...`  
+  Sensor-/Kontextvorschlaege fuer einen konkreten Aktor.
+- `POST /v1/actuators/{entity_id}/assignment`  
+  Manuelle Sensor-/Kontextzuordnung speichern.
+- `POST /v1/actuators/{entity_id}/activation`  
+  Freigabe oder Stop des automatischen Schaltens.
+
+## Cache
+
+Der Entity-Cache liegt neben dem Aktor-Store als `ha_entity_cache.json`.
+Er enthaelt HA-Entity-Metadaten wie Friendly Name, Bereich, Device und
+Kategoriegrundlagen.
+
+Der Cache wird geschrieben, wenn Discovery frische HA-Entities liest. Danach
+koennen Dashboard und Summary ohne erneute HA-Vollabfrage Namen, Raeume und
+Gruppen anzeigen.
+
+## Dashboard-Nutzung
+
+1. Startansicht oeffnen.
+2. `System & Cache` zeigt API, WebSocket, Cache-Groesse und geladene
+   Discovery-Gruppen.
+3. `Geraet zum Lernen auswaehlen` nutzt Suche, Typfilter und direkte
+   Entity-ID-Eingabe.
+4. `Beobachtete Geraete` zeigt gelernte Aktoren nach Raum oder Typ gruppiert.
+5. `Details` zeigt Lernfortschritt, Freigabe, Vorhersage, verwendete
+   Sensoren/Zustaende und Entscheidungsgruende.
+
+## Kategorien
+
+Aktoren:
+
+- Licht, LED, Lampen
+- Schalter, Steckdosen, Helper
+- Lueftung, Ventilatoren, Befeuchter/Entfeuchter
+- Heizungen/Klima
+- Rolllaeden/Cover
+- TV/Medien/Fernbedienungen
+- Szenen, Buttons, Schloesser, Ventile
+
+Sensoren und Kontext:
+
+- Luftfeuchtigkeit und Feuchte
+- Temperatur
+- Wetter
+- Helligkeit/Lux
+- Bewegung, Praesenz, Anwesenheit
+- Tuer/Fenster/Oeffnung
+- Licht-/Schalter-/Steckdosenstatus
+- Strom, Leistung, Energie, Einspeisung
+- PV, Akku, Wechselrichter
+- Helper und Szenen
+
+## Qualitaetspruefung
+
+Vor Release:
+
+```bash
+.venv/bin/pytest -q
+.venv/bin/ruff check .
+.venv/bin/mypy app backend tests
+git diff --check
+```
+
+Live nach Installation:
+
+```bash
+wget -qO- http://58adbe1e-sillyhome-next:8000/health
+wget -qO- http://58adbe1e-sillyhome-next:8000/health/websocket
+wget -qO /tmp/summary.json http://58adbe1e-sillyhome-next:8000/v1/actuators/summary
+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
+Git-Bundle-Backup erstellt:
+
+`/root/.openclaw/workspace/backups/sillyhome-next/`
+
+Bei Problemen kann auf `v0.7.21` zurueck installiert werden.

docs/V1_0_ACCEPTANCE.md

@@ -0,0 +1,82 @@
+# SillyHome Next v1.0 Acceptance
+
+Stand: 2026-06-17
+
+Diese Abnahme trennt belegte Umsetzung von offenen v1.0.x-Nacharbeiten. Der
+Funktionskern bleibt aktorzentriert: Nutzer waehlen Aktoren, SillyHome lernt
+Kontext und Verhalten, laeuft zuerst im Shadow-Modus und schaltet erst nach
+expliziter Freigabe.
+
+## Erfuellt
+
+- Versioniert, gepusht und installiert:
+  - `v1.0.0`: API-/Cache-Umbau
+  - `v1.0.1`: Dashboard-/Performance-Korrektur
+- Startpfad:
+  - `/v1/actuators/dashboard` liefert lokale Startdaten aus Store und Cache.
+  - Dashboard blockiert nicht mehr auf Discovery, Vorschlaegen oder
+    Automation-Refresh.
+  - Frontend bricht den Startdaten-Request nach 4,5 Sekunden ab und bleibt
+    bedienbar.
+- Cache:
+  - HA-Entity-Metadaten werden als `ha_entity_cache.json` gespeichert.
+  - Summary und Dashboard verwenden Friendly Name, Area und Device aus Cache.
+- Keine externen Abfragen im Dashboard-Startpfad:
+  - Kein Cloud-Ping, keine Fremd-API.
+  - HA-Zugriffe bleiben lokal gegen Home Assistant.
+- Dashboard:
+  - Orange ist Primaerfarbe.
+  - Cyan ist sichtbare Komplementaerfarbe.
+  - Rote UI-Flaechen wurden entfernt.
+  - Steuerung, beobachtete Geraete, Lernfortschritt/Freigabe und Systemstatus
+    sind getrennte Bereiche.
+  - Discovery, Vorschlaege und Automation-Suche laden erst bei Nutzeraktion.
+- Lernfortschritt und Freigabe:
+  - Karten zeigen Modus, Status, Handlungen, Vorhersage und Freigabestatus.
+  - Detailansicht zeigt Zuordnung, Sicherheit, Lernstand, Vorhersage,
+    Feedback, passende HA-Automationen und verwendete Sensoren/Zustaende.
+- Direkte HA-Nutzung:
+  - Aktor-Schaltungen laufen ueber Home-Assistant-Serviceaufrufe.
+  - Automation-Steuerung nutzt Home-Assistant-Endpunkte und gecachte
+    Automation-Metadaten.
+- Qualitaet:
+  - `pytest -q`
+  - `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
+
+- Bessere Statistik:
+  - Startbereich zeigt Aktoren, Freigabebereitschaft, Aktiv/Shadow,
+    Gelernt/Wartet, gelernte Handlungen, Discovery-Gruppen und Cache-Zeitpunkt.
+  - Noch offen: Verlaufsgrafiken, p95-Latenzen und Trendstatistik je Aktor.
+- Kontrollierte Abarbeitung und Queue:
+  - Reconciliation/Training laufen kontrolliert im Prozess und sind testbar.
+  - Noch offen: sichtbare Job-Queue mit Laufzeit, Fehlern und Retry-Status im
+    Dashboard.
+- Saubere Issues:
+  - v1.0.0-Issues #41 bis #47 wurden geschlossen.
+  - Rueckblickend waren sie zu grob; v1.0.x bekommt feinere Folgeissues fuer
+    Statistik, Queue-Sichtbarkeit und Performance-Budgets.
+
+## Offen Fuer v1.0.x
+
+- 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.
+
+## Rollback
+
+- Git-Bundle-Backups liegen unter
+  `/root/.openclaw/workspace/backups/sillyhome-next/`.
+- Vor `v1.0.1` wurde ein Home-Assistant-Teilbackup des Add-ons angelegt.
+  Referenz: `18a5b387`.
+- Letzter Vor-1.0-Stand: `v0.7.21`.

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.

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "sillyhome-next"
-version = "0.7.12"
+version = "1.4.0"
 description = "Lokales Smart-Home-Intelligenzsystem für Home Assistant"
 requires-python = ">=3.11"
 dependencies = [

tests/actuators/test_lifecycle.py

@@ -277,6 +277,81 @@ def test_reconciliation_ignores_generic_monitoring_area_for_automatic_context(
     assert record.lifecycle.status is LifecycleStatus.ARCHIVED
 
 
+def test_reconciliation_does_not_auto_select_overload_sensors_by_power_area(
+    tmp_path: Path,
+) -> None:
+    entities = [
+        HaEntitySummary(
+            entity_id="light.treppe_unten",
+            domain="light",
+            friendly_name="Licht Treppe Unten",
+            area_name="Strom",
+        ),
+        HaEntitySummary(
+            entity_id="binary_sensor.shelly_schrank_channel_1_overload",
+            domain="binary_sensor",
+            device_class="problem",
+            friendly_name="Shelly Schrank Channel 1 Überlast",
+            area_name="Strom",
+        ),
+        HaEntitySummary(
+            entity_id="binary_sensor.terrasse_terasse_overheating",
+            domain="binary_sensor",
+            device_class="problem",
+            friendly_name="Terrasse Terasse Überhitzung",
+            area_name="Strom",
+        ),
+    ]
+    service = _service(tmp_path, entities, {})
+
+    record = service.configure_actuator("light.treppe_unten")
+
+    assert record.assignment.selected_context_entity_ids == []
+    assert all(candidate.auto_accepted is False for candidate in record.context_candidates)
+
+
+def test_fan_prefers_humidity_over_power_sensor(tmp_path: Path) -> None:
+    start = datetime(2026, 6, 1, tzinfo=timezone.utc)
+    entities = [
+        HaEntitySummary(
+            entity_id="fan.bad_lueftung",
+            domain="fan",
+            friendly_name="Bad Lüftung",
+            area_name="Bad",
+        ),
+        HaEntitySummary(
+            entity_id="sensor.bad_luftfeuchtigkeit",
+            domain="sensor",
+            device_class="humidity",
+            state_class="measurement",
+            unit_of_measurement="%",
+            friendly_name="Bad Luftfeuchtigkeit",
+            area_name="Bad",
+        ),
+        HaEntitySummary(
+            entity_id="sensor.bad_power",
+            domain="sensor",
+            device_class="power",
+            state_class="measurement",
+            unit_of_measurement="W",
+            friendly_name="Bad Leistung",
+            area_name="Bad",
+        ),
+    ]
+    service = _service(
+        tmp_path,
+        entities,
+        {
+            "sensor.bad_luftfeuchtigkeit": _points(8, start, 55.0),
+            "sensor.bad_power": _points(8, start, 5.0),
+        },
+    )
+
+    record = service.configure_actuator("fan.bad_lueftung")
+
+    assert record.assignment.selected_numeric_entity_id == "sensor.bad_luftfeuchtigkeit"
+
+
 def test_manual_assignment_persists_and_wins_over_automatic_mapping(tmp_path: Path) -> None:
     start = datetime(2026, 6, 1, tzinfo=timezone.utc)
     entities = [
@@ -325,3 +400,39 @@ def test_manual_assignment_persists_and_wins_over_automatic_mapping(tmp_path: Pa
     assert record.assignment.selected_context_entity_ids == ["sensor.abstellkammer_illuminance"]
     assert record.assignment.source is AssignmentSource.MANUAL
     assert record.manual_override is not None
+
+
+def test_manual_assignment_evidence_is_not_duplicated(tmp_path: Path) -> None:
+    entities = [
+        HaEntitySummary(
+            entity_id="light.abstellkammer",
+            domain="light",
+            friendly_name="Abstellkammer Licht",
+            area_name="Abstellkammer",
+        ),
+        HaEntitySummary(
+            entity_id="binary_sensor.abstellkammer_motion",
+            domain="binary_sensor",
+            device_class="motion",
+            friendly_name="Abstellkammer Bewegung",
+            area_name="Abstellkammer",
+        ),
+    ]
+    service = _service(tmp_path, entities, {})
+    service.configure_actuator("light.abstellkammer")
+    for _ in range(3):
+        service.set_manual_assignment(
+            "light.abstellkammer",
+            numeric_entity_id=None,
+            context_entity_ids=["binary_sensor.abstellkammer_motion"],
+            note="Manuell gesetzt",
+        )
+
+    record = service.get_actuator("light.abstellkammer")
+    candidate = next(
+        item
+        for item in record.context_candidates
+        if item.entity_id == "binary_sensor.abstellkammer_motion"
+    )
+
+    assert candidate.evidence.count("Manuell vom Nutzer als relevant festgelegt.") == 1

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
@@ -28,8 +30,11 @@ class FakeHaReader(HaReader):
     def __init__(self, entities: list[HaEntitySummary], history: dict[str, list[float]]) -> None:
         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
         return list(self._entities)
 
     def discover(
@@ -83,6 +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(
@@ -108,6 +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",
@@ -115,6 +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",
@@ -213,6 +221,258 @@ def test_manual_assignment_endpoint_updates_context(tmp_path: Path) -> None:
     ]
 
 
+def test_weight_override_endpoint_updates_sensor_relevance(tmp_path: Path) -> None:
+    with TestClient(app) as client:
+        _install_service(tmp_path)
+        client.post("/v1/actuators", json={"actuator_entity_id": "light.abstellkammer"})
+        client.post(
+            "/v1/actuators/light.abstellkammer/assignment",
+            json={
+                "numeric_entity_id": "sensor.abstellkammer_illuminance",
+                "context_entity_ids": ["binary_sensor.abstellkammer_motion"],
+            },
+        )
+
+        response = client.post(
+            "/v1/actuators/light.abstellkammer/weights",
+            json={
+                "sensor_weights": {
+                    "sensor.abstellkammer_illuminance": 0.75,
+                    "binary_sensor.abstellkammer_motion": 0.5,
+                },
+                "sensor_weight_groups": [
+                    {
+                        "group_id": "abstellkammer_context",
+                        "name": "Abstellkammer Kontext",
+                        "entity_ids": [
+                            "sensor.abstellkammer_illuminance",
+                            "binary_sensor.abstellkammer_motion",
+                        ],
+                        "weight": 0.8,
+                    }
+                ],
+                "note": "Gewichtung korrigiert",
+            },
+        )
+
+    assert response.status_code == 200
+    payload = response.json()
+    assert payload["manual_override"]["sensor_weights"]["sensor.abstellkammer_illuminance"] == 0.75
+    assert payload["manual_override"]["sensor_weight_groups"][0]["group_id"] == (
+        "abstellkammer_context"
+    )
+    numeric = {
+        candidate["entity_id"]: candidate
+        for candidate in payload["numeric_candidates"]
+    }
+    assert numeric["sensor.abstellkammer_illuminance"]["manual_weight"] == 0.75
+    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)
+        client.get("/v1/actuators/discovery")
+        client.post("/v1/actuators", json={"actuator_entity_id": "light.abstellkammer"})
+
+        response = client.get("/v1/actuators/summary")
+
+    assert response.status_code == 200
+    payload = response.json()
+    assert payload[0]["actuator_entity_id"] == "light.abstellkammer"
+    assert payload[0]["friendly_name"] == "Abstellkammer Licht"
+    assert payload[0]["area_name"] == "Abstellkammer"
+    assert "behavior" not in payload[0]
+    assert "numeric_candidates" not in payload[0]
+
+
+def test_dashboard_overview_uses_cache_without_ha_roundtrip(tmp_path: Path) -> None:
+    with TestClient(app) as client:
+        _install_service(tmp_path)
+        reader = app.state.ha_reader
+        client.get("/v1/actuators/discovery")
+        client.post("/v1/actuators", json={"actuator_entity_id": "light.abstellkammer"})
+        calls_before = reader.read_entities_calls
+
+        response = client.get("/v1/actuators/dashboard")
+
+    assert response.status_code == 200
+    assert reader.read_entities_calls == calls_before
+    payload = response.json()
+    assert payload["cache"]["available"] is True
+    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")
+        anomalies_response = client.get("/v1/actuators/anomalies")
+
+    assert dashboard_response.status_code == 200
+    assert start_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["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_discovery_reads_entities_once_and_reuses_them(tmp_path: Path) -> None:
+    with TestClient(app) as client:
+        _install_service(tmp_path)
+        reader = app.state.ha_reader
+
+        response = client.get("/v1/actuators/discovery", params={"refresh": True})
+
+    assert response.status_code == 200
+    assert reader.read_entities_calls == 1
+
+
 def test_context_options_returns_learnable_entities(tmp_path: Path) -> None:
     with TestClient(app) as client:
         _install_service(tmp_path)

tests/behavior/test_engine.py

@@ -558,6 +558,7 @@ def test_cooldown_allows_opposite_follow_up_action(tmp_path: Path) -> None:
     ("domain", "state", "service"),
     [
         ("light", "on", "turn_on"),
+        ("media_player", "off", "turn_off"),
         ("switch", "off", "turn_off"),
         ("cover", "open", "open_cover"),
         ("cover", "closed", "close_cover"),

tests/ha/test_discovery.py

@@ -91,6 +91,10 @@ def test_discovery_filters_domain_and_learnable() -> None:
             HaEntitySummary(entity_id="input_boolean.sleep_mode", domain="input_boolean"),
             "helper",
         ),
+        (
+            HaEntitySummary(entity_id="media_player.tv", domain="media_player"),
+            "media_tv",
+        ),
         (
             HaEntitySummary(
                 entity_id="sensor.brightness",

tests/test_dashboard.py

@@ -9,11 +9,14 @@ def test_dashboard_is_served_at_root() -> None:
 
     assert response.status_code == 200
     assert "SillyHome Next" in response.text
+    assert "Arbeitsdashboard für gelernte Home-Assistant-Bedienung" in response.text
     assert "So gehst du vor" in response.text
-    assert "Gerät zum Lernen auswählen" in response.text
+    assert "Steuerung" in response.text
     assert "Entitätsname oder Gerät aus Home Assistant" in response.text
     assert "Oder aus Liste wählen" in response.text
     assert "Liste durchsuchen" in response.text
+    assert "Geräteliste bei Bedarf laden" in response.text
+    assert "Vorschläge können Home Assistant stark abfragen" in response.text
     assert "Wie gewohnt bedienen" in response.text
     assert "Ohne deine spätere Freigabe wird nichts geschaltet" in response.text
     assert "Du wählst keine Sensoren und erstellst keine Regeln" in response.text
@@ -32,5 +35,12 @@ def test_dashboard_is_served_at_root() -> None:
     assert "Kein frischer passender Sensorwechsel erkannt" in response.text
     assert "Vorhersage jetzt prüfen" not in response.text
     assert "record.behavior.activation_ready" in response.text
+    assert "record.behavior.status ===" not in response.text
+    assert "record.behavior_status || record.behavior?.status" in response.text
+    assert 'api("v1/actuators")' not in response.text
+    assert 'api("v1/actuators/summary")' in response.text
+    assert 'api("v1/entities")' not in response.text
+    assert 'details class="collapsible"' in response.text
+    assert 'class="group-panel"' in response.text
     assert "Automation-Entwurf" not in response.text
     assert "Manuelle Overrides" not in response.text

tests/test_main.py

@@ -94,7 +94,8 @@ def test_ha_event_listener_processes_state_change(tmp_path: Path) -> None:
 
         connect.assert_called_once_with(
             "ws://homeassistant:8123/api/websocket",
-            ping_interval=None,
+            ping_interval=20,
+            ping_timeout=10,
         )
         assert fake_ws.sent == [
             {"type": "auth", "access_token": "test-token"},
@@ -110,6 +111,7 @@ def test_ha_event_listener_processes_state_change(tmp_path: Path) -> None:
     mock_app.state.behavior_engine = mock_engine
     mock_app.state.ha_reader = _FakeHaReader()
     mock_store = ActuatorStore(tmp_path / "store")
+    mock_store.configure("light.test")
     mock_app.state.actuator_store = mock_store
     mock_client = MagicMock()