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a72d6d8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | """CultivarStabilityScore + EvolutionGate — gate evolutivo de culturas (d3).
MARS_SYSTEM_CONTRACT.yaml · operator_decisions.d3_gate_evolution
Decisão do operador (2026-09-28): transições de cultura são irreversíveis
(batata/trigo consomem solo não-reciclável) — só prossiga por GATE DE DADOS:
- algae_to_potato: stability >= 0.90 por 55.7 sols, epsilon < 0.15,
reservas energia >= 50%, água >= 60%, nutrientes >= 30%
- potato_to_wheat: stability >= 0.95 por 111.4 sols, epsilon < 0.10,
reservas energia >= 70%, água >= 80%, nutrientes >= 50%, solo >= 100 m³
Score ponderado: yield_consistency 0.30, contamination_resistance 0.25,
energy_efficiency 0.20, nutrient_uptake 0.15, stress_tolerance 0.10.
"""
from __future__ import annotations
from dataclasses import dataclass, field
from typing import Dict, List, Tuple
import numpy as np
STABILITY_WEIGHTS = {
"yield_consistency": 0.30,
"contamination_resistance": 0.25,
"energy_efficiency": 0.20,
"nutrient_uptake": 0.15,
"stress_tolerance": 0.10,
}
GATE_THRESHOLDS = {
"algae_to_potato": {
"stability_score_min": 0.90,
"duration_min_sols": 55.7,
"epsilon_max": 0.15,
"energy_reserve_min": 0.60, # ação E5 (margem black swan)
"water_reserve_min": 0.60,
"nutrient_reserve_min": 0.30,
"max_sectors_per_cycle": 2,
"rollback_possible": False,
},
"potato_to_wheat": {
"stability_score_min": 0.95,
"duration_min_sols": 111.4,
"epsilon_max": 0.10,
"energy_reserve_min": 0.70,
"water_reserve_min": 0.80,
"nutrient_reserve_min": 0.50,
"soil_volume_min_m3": 100.0,
"max_sectors_per_cycle": 3,
"rollback_possible": False,
},
}
class CultivarStabilityScore:
"""Calcula o score de estabilidade [0,1] de uma cultivar para gate."""
def __init__(self, weights: Dict[str, float] | None = None) -> None:
self.weights = dict(weights or STABILITY_WEIGHTS)
def calculate(self, cultivar_data: Dict) -> Tuple[float, Dict[str, float]]:
"""Score ponderado a partir de dados históricos da cultivar."""
yield_var = float(np.var(cultivar_data.get("yield_kg_m2_day", [0.0])))
yield_score = 1.0 - min(1.0, yield_var / 0.10)
contamination_loss = float(cultivar_data.get("contamination_loss_pct", 100.0))
contamination_score = 1.0 - min(1.0, contamination_loss / 5.0)
energy_eff = float(cultivar_data.get("energy_efficiency_pct", 0.0)) / 80.0
energy_score = min(1.0, energy_eff)
nutrient_eff = float(cultivar_data.get("nutrient_uptake_efficiency", 0.0))
nutrient_score = min(1.0, nutrient_eff / 0.70)
stress_recovery = float(cultivar_data.get("post_storm_recovery_days", 10.0))
stress_score = max(0.0, 1.0 - stress_recovery / 10.0)
scores = {
"yield_consistency": yield_score,
"contamination_resistance": contamination_score,
"energy_efficiency": energy_score,
"nutrient_uptake": nutrient_score,
"stress_tolerance": stress_score,
}
total = sum(scores[k] * self.weights[k] for k in scores)
return float(total), scores
@dataclass
class EvolutionGate:
"""Gate de dados para transição de cultura dominante (irreversível)."""
stability: CultivarStabilityScore = field(default_factory=CultivarStabilityScore)
def check(self, transition: str, data: Dict) -> Dict:
"""Avalia o gate para uma transição. data carrega métricas reais."""
if transition not in GATE_THRESHOLDS:
raise ValueError(f"transição desconhecida: {transition}")
thr = GATE_THRESHOLDS[transition]
score, breakdown = self.stability.calculate(data.get("cultivar", {}))
checks = {
"stability_score": score >= thr["stability_score_min"],
"duration_sols": data.get("duration_sols", 0) >= thr["duration_min_sols"],
"epsilon": data.get("epsilon", 1.0) < thr["epsilon_max"],
"energy_reserve": data.get("energy_reserve", 0.0) >= thr["energy_reserve_min"],
"water_reserve": data.get("water_reserve", 0.0) >= thr["water_reserve_min"],
"nutrient_reserve": data.get("nutrient_reserve", 0.0) >= thr["nutrient_reserve_min"],
}
if "soil_volume_min_m3" in thr:
checks["soil_volume_m3"] = data.get("soil_volume_m3", 0.0) >= thr["soil_volume_min_m3"]
passed = all(checks.values())
return {
"transition": transition,
"score": round(score, 4),
"score_breakdown": {k: round(v, 4) for k, v in breakdown.items()},
"thresholds": thr,
"checks": checks,
"passed": passed,
"irreversible": not thr["rollback_possible"],
"max_sectors_per_cycle": thr["max_sectors_per_cycle"],
}
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