Ai/soile/health_summary.py

from __future__ import annotations

from typing import Any


DEFAULT_HEALTH_PROFILE = {
    "moisture": {"ideal_value": 65.0, "min_range": 45.0, "max_range": 75.0, "weight": 0.45},
    "ph": {"ideal_value": 6.6, "min_range": 6.0, "max_range": 7.5, "weight": 0.30},
    "ec": {"ideal_value": 1.2, "min_range": 0.2, "max_range": 3.0, "weight": 0.25},
}

METRIC_SPECS = {
    "moisture": {"sensor_field": "soil_moisture", "label": "رطوبت خاک", "unit": "%"},
    "ph": {"sensor_field": "soil_ph", "label": "pH خاک", "unit": "pH"},
    "ec": {"sensor_field": "electrical_conductivity", "label": "هدایت الکتریکی", "unit": "dS/m"},
}


def _safe_number(value: Any, default: float = 0.0) -> float:
    return default if value is None else float(value)


def _normalize_metric(value: float, ideal_value: float, min_range: float, max_range: float) -> float:
    if max_range <= min_range:
        return 0.0
    if value <= min_range or value >= max_range:
        return 0.0
    if value == ideal_value:
        return 1.0
    if value < ideal_value:
        span = ideal_value - min_range
        if span <= 0:
            return 0.0
        return max(0.0, min(1.0, (value - min_range) / span))
    span = max_range - ideal_value
    if span <= 0:
        return 0.0
    return max(0.0, min(1.0, (max_range - value) / span))


def resolve_plant_profile(plants: list[Any]) -> tuple[dict[str, dict[str, float]], str]:
    for plant in plants:
        profile = getattr(plant, "health_profile", None) or {}
        if profile:
            merged = {
                metric: {
                    **DEFAULT_HEALTH_PROFILE.get(metric, {}),
                    **profile.get(metric, {}),
                }
                for metric in set(DEFAULT_HEALTH_PROFILE) | set(profile)
            }
            return merged, getattr(plant, "name", "گیاه")
    return DEFAULT_HEALTH_PROFILE, (plants[0].name if plants else "پروفایل پیش‌فرض")


def compute_health_score(sensor: Any, profile: dict[str, dict[str, float]]) -> tuple[int, list[dict[str, Any]]]:
    weighted_sum = 0.0
    total_weight = 0.0
    components: list[dict[str, Any]] = []

    for metric_type, config in profile.items():
        spec = METRIC_SPECS.get(metric_type)
        if spec is None:
            continue

        sensor_value = getattr(sensor, spec["sensor_field"], None)
        if sensor_value is None:
            continue

        current_value = _safe_number(sensor_value, 0)
        defaults = DEFAULT_HEALTH_PROFILE.get(metric_type, {})
        ideal_value = float(config.get("ideal_value", defaults.get("ideal_value", 0)))
        min_range = float(config.get("min_range", defaults.get("min_range", 0)))
        max_range = float(config.get("max_range", defaults.get("max_range", 0)))
        weight = float(config.get("weight", defaults.get("weight", 0)))
        if weight <= 0:
            continue

        normalized_value = _normalize_metric(current_value, ideal_value, min_range, max_range)
        weighted_sum += weight * normalized_value
        total_weight += weight
        components.append(
            {
                "metricType": metric_type,
                "label": spec["label"],
                "unit": spec["unit"],
                "currentValue": round(current_value, 2),
                "idealValue": round(ideal_value, 2),
                "minRange": round(min_range, 2),
                "maxRange": round(max_range, 2),
                "weight": round(weight, 3),
                "normalizedValue": round(normalized_value, 4),
                "weightedContribution": round(weight * normalized_value, 4),
            }
        )

    if total_weight <= 0:
        return 0, components

    score = round((weighted_sum / total_weight) * 100)
    return max(0, min(100, score)), components


def health_language(health_score: int) -> dict[str, str]:
    if health_score >= 85:
        return {
            "short_chip_text": "بسیار خوب",
            "action_hint": "برنامه فعلی پایش و نگهداری حفظ شود.",
            "explanation": "بیشتر شاخص های کلیدی نزدیک به پروفایل ایده آل گیاه هستند.",
        }
    if health_score >= 70:
        return {
            "short_chip_text": "پایدار",
            "action_hint": "تنظیمات فعلی حفظ و فقط شاخص های مرزی پایش شوند.",
            "explanation": "وضعیت کلی مزرعه قابل قبول است اما بعضی شاخص ها هنوز جای بهبود دارند.",
        }
    if health_score >= 50:
        return {
            "short_chip_text": "نیازمند تنظیم",
            "action_hint": "پارامترهای دور از محدوده ایده آل در اولویت اصلاح قرار گیرند.",
            "explanation": "بخشی از شرایط محیطی از پروفایل مطلوب گیاه فاصله گرفته است.",
        }
    return {
        "short_chip_text": "تنش بالا",
        "action_hint": "اصلاح فوری رطوبت، تغذیه يا شوری بر اساس اجزای امتیاز انجام شود.",
        "explanation": "چند شاخص اصلی خارج از بازه قابل قبول گیاه هستند.",
    }


def build_soil_health_summary(sensor: Any, plants: list[Any]) -> dict[str, Any]:
    profile, profile_source = resolve_plant_profile(plants)
    health_score, health_components = compute_health_score(sensor, profile)
    moisture = _safe_number(getattr(sensor, "soil_moisture", None), 0)
    language = health_language(health_score)
    return {
        "healthScore": health_score,
        "profileSource": profile_source,
        "healthScoreDetails": {
            "method": "normalized_weighted_average",
            "profileSource": profile_source,
            "components": health_components,
        },
        "healthLanguage": language,
        "avgSoilMoisture": round(moisture),
        "avgSoilMoistureRaw": round(moisture, 2),
        "avgSoilMoistureStatus": "بهینه" if 45 <= moisture <= 75 else "نیازمند بررسی",
    }