AI UPDATE

This commit is contained in:
2026-03-22 03:08:27 +03:30
parent 3ee14ca977
commit d977a583c6
37 changed files with 3525 additions and 263 deletions
+174 -8
View File
@@ -1,6 +1,160 @@
from __future__ import annotations
from typing import Any
from dashboard_data.card_utils import average, safe_number
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 _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(context: dict[str, Any]) -> tuple[dict[str, dict[str, float]], str]:
plants = context.get("plants", [])
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 = float(safe_number(sensor_value, 0))
ideal_value = float(config.get("ideal_value", DEFAULT_HEALTH_PROFILE.get(metric_type, {}).get("ideal_value", 0)))
min_range = float(config.get("min_range", DEFAULT_HEALTH_PROFILE.get(metric_type, {}).get("min_range", 0)))
max_range = float(config.get("max_range", DEFAULT_HEALTH_PROFILE.get(metric_type, {}).get("max_range", 0)))
weight = float(config.get("weight", DEFAULT_HEALTH_PROFILE.get(metric_type, {}).get("weight", 0)))
if weight <= 0:
continue
normalized_value = _normalize_metric(
value=current_value,
ideal_value=ideal_value,
min_range=min_range,
max_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, ai_bundle: dict | None = None) -> dict[str, str]:
ai_bundle = ai_bundle or {}
ai_health = ai_bundle.get("farmOverviewKpis", {}) if isinstance(ai_bundle, dict) else {}
short_chip_text = ai_health.get("short_chip_text")
action_hint = ai_health.get("action_hint")
explanation = ai_health.get("explanation")
if isinstance(short_chip_text, str) and short_chip_text.strip() and isinstance(action_hint, str) and action_hint.strip() and isinstance(explanation, str) and explanation.strip():
return {
"short_chip_text": short_chip_text.strip(),
"action_hint": action_hint.strip(),
"explanation": explanation.strip(),
}
if health_score >= 85:
return {
"short_chip_text": "بسیار خوب",
"action_hint": "برنامه فعلی پایش و نگهداری حفظ شود.",
"explanation": "شاخص سلامت مزرعه به محدوده بسیار خوب رسیده و بیشتر پارامترهای کلیدی نزدیک به پروفایل ایده‌آل گیاه هستند.",
}
if health_score >= 70:
return {
"short_chip_text": "پایدار",
"action_hint": "تنظیمات فعلی حفظ و فقط شاخص‌های مرزی پایش شوند.",
"explanation": "سلامت مزرعه در محدوده قابل قبول است، اما برخی پارامترها هنوز با مقدار ایده‌آل فاصله دارند.",
}
if health_score >= 50:
return {
"short_chip_text": "نیازمند تنظیم",
"action_hint": "پارامترهای دور از محدوده ایده‌آل در اولویت اصلاح قرار گیرند.",
"explanation": "امتیاز سلامت نشان می‌دهد بخشی از شرایط محیطی از پروفایل مطلوب گیاه فاصله گرفته و باید تنظیم شود.",
}
return {
"short_chip_text": "تنش بالا",
"action_hint": "اصلاح فوری رطوبت، تغذیه یا شوری بر اساس اجزای امتیاز انجام شود.",
"explanation": "سلامت مزرعه در محدوده ضعیف قرار دارد و چند شاخص اصلی خارج از بازه قابل قبول گیاه هستند.",
}
def build_farm_overview_kpis(sensor_id: str, context: dict | None = None, ai_bundle: dict | None = None) -> dict:
context = context or {}
sensor = context.get("sensor")
@@ -8,26 +162,36 @@ def build_farm_overview_kpis(sensor_id: str, context: dict | None = None, ai_bun
if sensor is None:
return {"kpis": []}
profile, profile_source = _resolve_plant_profile(context)
health_score, health_components = _compute_health_score(sensor, profile)
health_language = _health_language(health_score, ai_bundle=ai_bundle)
moisture = safe_number(sensor.soil_moisture, 0)
ph = safe_number(sensor.soil_ph, 7)
ec = safe_number(sensor.electrical_conductivity, 0)
humidity = average([forecast.humidity_mean for forecast in forecasts[:3]], default=45)
health_score = max(0, min(100, round(100 - abs(65 - moisture) - (abs(6.8 - ph) * 10) - (ec * 5))))
water_stress = max(0, min(100, round(35 - (moisture / 2))))
disease_risk = max(0, min(100, round((humidity * 0.4) + (safe_number(sensor.soil_temperature, 0) * 0.6) - 20)))
yield_prediction = round(max(5, (health_score / 2.1)), 1)
primary_gap = min(health_components, key=lambda item: item["normalizedValue"], default=None)
return {
"kpis": [
{
"id": "farm_health_score",
"title": "امتیاز سلامت مزرعه",
"subtitle": "تحلیل هوشمند",
"subtitle": f"پروفایل {profile_source}",
"stats": f"{health_score}%",
"avatarColor": "success" if health_score >= 70 else "warning",
"avatarColor": "success" if health_score >= 70 else "warning" if health_score >= 50 else "error",
"avatarIcon": "tabler-heartbeat",
"chipText": "خوب" if health_score >= 70 else "متوسط",
"chipColor": "success" if health_score >= 70 else "warning",
"chipText": health_language["short_chip_text"],
"chipColor": "success" if health_score >= 70 else "warning" if health_score >= 50 else "error",
"actionHint": health_language["action_hint"],
"explanation": health_language["explanation"],
"healthScoreDetails": {
"method": "normalized_weighted_average",
"profileSource": profile_source,
"components": health_components,
},
},
{
"id": "water_stress_index",
@@ -66,8 +230,10 @@ def build_farm_overview_kpis(sensor_id: str, context: dict | None = None, ai_bun
"stats": f"{yield_prediction} تن",
"avatarColor": "secondary",
"avatarIcon": "tabler-chart-bar",
"chipText": f"+{max(0, health_score - 50)}%",
"chipColor": "success",
"chipText": (
primary_gap["label"] if primary_gap else "پایدار"
),
"chipColor": "warning" if primary_gap and primary_gap["normalizedValue"] < 0.6 else "success",
},
{
"id": "pest_risk",