278 lines
9.8 KiB
Python
278 lines
9.8 KiB
Python
from __future__ import annotations
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from datetime import datetime, timezone
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from enum import StrEnum
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from pydantic import BaseModel, Field
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from app.ha.discovery import EntityRole
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class AssignmentSource(StrEnum):
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NONE = "none"
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AUTOMATIC = "automatic"
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MANUAL = "manual"
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class LifecycleStatus(StrEnum):
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PENDING_ASSIGNMENT = "pending_assignment"
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REVIEW_REQUIRED = "review_required"
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PENDING_HISTORY = "pending_history"
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TRAINED = "trained"
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STALE = "stale"
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INVALID = "invalid"
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ORPHANED = "orphaned"
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ARCHIVED = "archived"
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class BehaviorMode(StrEnum):
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SHADOW = "shadow"
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ACTIVE = "active"
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PAUSED = "paused"
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class BehaviorStatus(StrEnum):
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COLLECTING = "collecting"
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TRAINED = "trained"
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BLOCKED = "blocked"
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class SafetyStage(StrEnum):
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OBSERVE = "observe"
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SUGGEST = "suggest"
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SHADOW = "shadow"
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PARTIAL = "partial"
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ACTIVE = "active"
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class JobStatus(StrEnum):
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PENDING = "pending"
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RUNNING = "running"
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COMPLETED = "completed"
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FAILED = "failed"
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class AssignmentCandidate(BaseModel):
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entity_id: str
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domain: str
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role: EntityRole
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device_class: str | None = None
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state_class: str | None = None
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unit_of_measurement: str | None = None
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friendly_name: str | None = None
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area_name: str | None = None
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device_name: str | None = None
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score: float = Field(ge=0.0)
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confidence: float = Field(ge=0.0, le=1.0)
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manual_weight: float | None = Field(default=None, ge=0.0, le=1.0)
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effective_weight: float = Field(default=1.0, ge=0.0, le=1.0)
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auto_accepted: bool = False
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evidence: list[str] = Field(default_factory=list)
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class AssignmentSelection(BaseModel):
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selected_numeric_entity_id: str | None = None
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selected_context_entity_ids: list[str] = Field(default_factory=list)
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source: AssignmentSource = AssignmentSource.NONE
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confidence: float = Field(default=0.0, ge=0.0, le=1.0)
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review_required: bool = True
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reason: str = "Noch keine Zuordnung vorhanden."
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class SensorWeightGroup(BaseModel):
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group_id: str = Field(pattern=r"^[a-z0-9_-]{1,64}$")
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name: str = Field(min_length=1, max_length=120)
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entity_ids: list[str] = Field(default_factory=list)
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weight: float = Field(default=1.0, ge=0.0, le=1.0)
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class ManualOverride(BaseModel):
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numeric_entity_id: str | None = None
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context_entity_ids: list[str] = Field(default_factory=list)
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sensor_weights: dict[str, float] = Field(default_factory=dict)
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sensor_weight_groups: list[SensorWeightGroup] = Field(default_factory=list)
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updated_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
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note: str | None = None
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class LifecycleAuditEntry(BaseModel):
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at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
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action: str = Field(min_length=1, max_length=120)
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reason: str = Field(min_length=1, max_length=500)
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class ModelLifecycleState(BaseModel):
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model_id: str
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status: LifecycleStatus = LifecycleStatus.PENDING_ASSIGNMENT
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last_reconciled_at: datetime | None = None
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last_trained_at: datetime | None = None
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last_history_signature: str | None = None
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last_history_point_count: int = Field(default=0, ge=0)
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reason: str = "Noch keine Trainingsdaten ausgewertet."
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next_action: str = "Aktor auswählen; Kontext und Historie werden automatisch geprüft."
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audit: list[LifecycleAuditEntry] = Field(default_factory=list)
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class BehaviorPattern(BaseModel):
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target_state: str = Field(min_length=1, max_length=100)
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minute_of_day: int = Field(ge=0, le=1439)
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weekday: int = Field(ge=0, le=6)
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context_states: dict[str, str] = Field(default_factory=dict)
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trigger_entity_id: str | None = None
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trigger_from_state: str | None = None
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trigger_to_state: str | None = None
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source: str = Field(default="observed", max_length=40)
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weight: float = Field(default=1.0, ge=0.1, le=1.0)
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observed_at: datetime
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class BehaviorPrediction(BaseModel):
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target_state: str
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confidence: float = Field(ge=0.0, le=1.0)
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generated_at: datetime
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reason: str
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matching_patterns: int = Field(default=0, ge=0)
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executed: bool = False
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execution_reason: str = "Vorhersage wurde noch nicht ausgeführt."
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class DecisionFactor(BaseModel):
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entity_id: str | None = None
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label: str
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factor_type: str = Field(max_length=40)
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state: str | None = None
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weight: float = Field(default=1.0, ge=0.0, le=1.0)
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contribution: float = Field(default=0.0, ge=0.0, le=1.0)
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evidence: list[str] = Field(default_factory=list)
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class SafetyRule(BaseModel):
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rule_id: str = Field(pattern=r"^[a-z0-9_-]{1,64}$")
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label: str = Field(min_length=1, max_length=160)
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enabled: bool = True
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blocking: bool = True
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reason: str = Field(default="", max_length=300)
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def default_safety_rules() -> list[SafetyRule]:
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return [
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SafetyRule(
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rule_id="activation_ready",
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label="Nur nach Lernfreigabe aktiv schalten",
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reason="Der Aktor muss genug eindeutiges Verhalten gelernt haben.",
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),
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SafetyRule(
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rule_id="confidence_threshold",
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label="Mindest-Sicherheit einhalten",
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reason="Vorhersagen unter der Schaltschwelle bleiben im Shadow-Modus.",
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),
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SafetyRule(
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rule_id="cooldown",
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label="Sicherheits-Cooldown gegen Hin-und-her-Schalten",
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reason="Gleiche Zielzustände werden nicht zu schnell wiederholt.",
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),
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SafetyRule(
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rule_id="manual_block",
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label="Manuelle Sperre respektieren",
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reason="Nutzer können jeden Aktor sofort blockieren.",
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),
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]
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class SafetyProfile(BaseModel):
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stage: SafetyStage = SafetyStage.SHADOW
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manual_block: bool = False
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min_confidence: float = Field(default=0.82, ge=0.0, le=1.0)
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min_confidence_on: float | None = Field(default=None, ge=0.0, le=1.0)
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min_confidence_off: float | None = Field(default=None, ge=0.0, le=1.0)
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cooldown_seconds: int | None = Field(default=None, ge=0)
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rules: list[SafetyRule] = Field(default_factory=default_safety_rules)
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updated_at: datetime | None = None
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note: str | None = Field(default=None, max_length=500)
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class ExecutionEvent(BaseModel):
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target_state: str
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executed_at: datetime
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class RelatedAutomation(BaseModel):
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entity_id: str = Field(pattern=r"^automation\.[a-z0-9_]+$")
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config_id: str = Field(min_length=1, max_length=120)
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friendly_name: str = Field(min_length=1, max_length=200)
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enabled: bool
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class BehaviorState(BaseModel):
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mode: BehaviorMode = BehaviorMode.SHADOW
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status: BehaviorStatus = BehaviorStatus.COLLECTING
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approved_at: datetime | None = None
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sample_count: int = Field(default=0, ge=0)
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high_confidence_sample_count: int = Field(default=0, ge=0)
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patterns: list[BehaviorPattern] = Field(default_factory=list)
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prediction: BehaviorPrediction | None = None
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last_trained_at: datetime | None = None
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last_evaluated_at: datetime | None = None
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last_executed_at: datetime | None = None
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execution_events: list[ExecutionEvent] = Field(default_factory=list)
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activation_ready: bool = False
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activation_reason: str = "Noch nicht genügend Verhalten für eine Freigabe gelernt."
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related_automations: list[RelatedAutomation] = Field(default_factory=list)
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paused_automation_entity_ids: list[str] = Field(default_factory=list)
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reason: str = "Historische Aktorhandlungen werden analysiert."
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safety: SafetyProfile = Field(default_factory=SafetyProfile)
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decision_factors: list[DecisionFactor] = Field(default_factory=list)
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knowledge: list[str] = Field(default_factory=list)
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assumptions: list[str] = Field(default_factory=list)
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uncertainties: list[str] = Field(default_factory=list)
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safety_blockers: list[str] = Field(default_factory=list)
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sample_trend: list[int] = Field(default_factory=list)
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confidence_trend: list[float] = Field(default_factory=list)
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correct_feedback_count: int = Field(default=0, ge=0)
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incorrect_feedback_count: int = Field(default=0, ge=0)
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class ActuatorRecord(BaseModel):
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actuator_entity_id: str = Field(pattern=r"^[a-z0-9_]+\.[a-z0-9_]+$")
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enabled: bool = True
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created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
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updated_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
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assignment: AssignmentSelection = Field(default_factory=AssignmentSelection)
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manual_override: ManualOverride | None = None
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numeric_candidates: list[AssignmentCandidate] = Field(default_factory=list)
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context_candidates: list[AssignmentCandidate] = Field(default_factory=list)
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lifecycle: ModelLifecycleState
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behavior: BehaviorState = Field(default_factory=BehaviorState)
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class ReconciliationState(BaseModel):
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last_started_at: datetime | None = None
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last_completed_at: datetime | None = None
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last_trigger: str | None = None
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running: bool = False
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configured_actuators: int = Field(default=0, ge=0)
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review_required: int = Field(default=0, ge=0)
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trained_models: int = Field(default=0, ge=0)
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last_summary: str = "Noch keine Reconciliation ausgeführt."
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class JobQueueItem(BaseModel):
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job_id: str = Field(min_length=1, max_length=120)
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kind: str = Field(min_length=1, max_length=40)
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target: str | None = Field(default=None, max_length=160)
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trigger: str = Field(default="manual", max_length=40)
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status: JobStatus = JobStatus.PENDING
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started_at: datetime | None = None
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completed_at: datetime | None = None
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duration_ms: int | None = Field(default=None, ge=0)
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error: str | None = Field(default=None, max_length=500)
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summary: str = Field(default="", max_length=500)
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class JobQueueState(BaseModel):
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jobs: list[JobQueueItem] = Field(default_factory=list)
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def model_id_for_actuator(actuator_entity_id: str) -> str:
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return f"actuator.{actuator_entity_id}"
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