scoring_calculation_system.py

from dataclasses import dataclass
from breed_health_info import breed_health_info
from breed_noise_info import breed_noise_info
import traceback
import math
import random

@dataclass
class UserPreferences:

    """使用者偏好設定的資料結構"""
    living_space: str  # "apartment", "house_small", "house_large"
    yard_access: str  # "no_yard", "shared_yard", "private_yard" 
    exercise_time: int  # minutes per day
    exercise_type: str  # "light_walks", "moderate_activity", "active_training" 
    grooming_commitment: str  # "low", "medium", "high"
    experience_level: str  # "beginner", "intermediate", "advanced"
    time_availability: str  # "limited", "moderate", "flexible" 
    has_children: bool
    children_age: str  # "toddler", "school_age", "teenager"
    noise_tolerance: str  # "low", "medium", "high"
    space_for_play: bool
    other_pets: bool
    climate: str  # "cold", "moderate", "hot"
    health_sensitivity: str = "medium"
    barking_acceptance: str = None

    def __post_init__(self):
        """在初始化後運行，用於設置派生值"""
        if self.barking_acceptance is None:
            self.barking_acceptance = self.noise_tolerance


# @staticmethod
# def calculate_breed_bonus(breed_info: dict, user_prefs: 'UserPreferences') -> float:
#     """計算品種額外加分"""
#     bonus = 0.0
#     temperament = breed_info.get('Temperament', '').lower()
    
#     # 1. 壽命加分（最高0.05）
#     try:
#         lifespan = breed_info.get('Lifespan', '10-12 years')
#         years = [int(x) for x in lifespan.split('-')[0].split()[0:1]]
#         longevity_bonus = min(0.05, (max(years) - 10) * 0.01)
#         bonus += longevity_bonus
#     except:
#         pass

#     # 2. 性格特徵加分（最高0.15）
#     positive_traits = {
#         'friendly': 0.05,           
#         'gentle': 0.05,
#         'patient': 0.05,
#         'intelligent': 0.04,
#         'adaptable': 0.04,
#         'affectionate': 0.04,
#         'easy-going': 0.03,         
#         'calm': 0.03                
#     }
    
#     negative_traits = {
#         'aggressive': -0.08,        
#         'stubborn': -0.06,
#         'dominant': -0.06,
#         'aloof': -0.04,
#         'nervous': -0.05,           
#         'protective': -0.04         
#     }
    
#     personality_score = sum(value for trait, value in positive_traits.items() if trait in temperament)
#     personality_score += sum(value for trait, value in negative_traits.items() if trait in temperament)
#     bonus += max(-0.15, min(0.15, personality_score))

#     # 3. 適應性加分（最高0.1）
#     adaptability_bonus = 0.0
#     if breed_info.get('Size') == "Small" and user_prefs.living_space == "apartment":
#         adaptability_bonus += 0.05
#     if 'adaptable' in temperament or 'versatile' in temperament:
#         adaptability_bonus += 0.05
#     bonus += min(0.1, adaptability_bonus)

#     # 4. 家庭相容性（最高0.1）
#     if user_prefs.has_children:
#         family_traits = {
#             'good with children': 0.06,  
#             'patient': 0.05,
#             'gentle': 0.05,
#             'tolerant': 0.04,           
#             'playful': 0.03             
#         }
#         unfriendly_traits = {
#             'aggressive': -0.08,        
#             'nervous': -0.07,
#             'protective': -0.06,
#             'territorial': -0.05        
#         }
        
#         # 年齡評估這樣能更細緻
#         age_adjustments = {
#             'toddler': {'bonus_mult': 0.7, 'penalty_mult': 1.3},
#             'school_age': {'bonus_mult': 1.0, 'penalty_mult': 1.0},
#             'teenager': {'bonus_mult': 1.2, 'penalty_mult': 0.8}
#         }
        
#         adj = age_adjustments.get(user_prefs.children_age, 
#                                 {'bonus_mult': 1.0, 'penalty_mult': 1.0})
        
#         family_bonus = sum(value for trait, value in family_traits.items() 
#                           if trait in temperament) * adj['bonus_mult']
#         family_penalty = sum(value for trait, value in unfriendly_traits.items() 
#                            if trait in temperament) * adj['penalty_mult']
        
#         bonus += min(0.15, max(-0.2, family_bonus + family_penalty))

    
#     # 5. 專門技能加分（最高0.1）
#     skill_bonus = 0.0
#     special_abilities = {
#         'working': 0.03,
#         'herding': 0.03,
#         'hunting': 0.03,
#         'tracking': 0.03,
#         'agility': 0.02
#     }
#     for ability, value in special_abilities.items():
#         if ability in temperament.lower():
#             skill_bonus += value
#     bonus += min(0.1, skill_bonus)

#     return min(0.5, max(-0.25, bonus))


@staticmethod
def calculate_breed_bonus(breed_info: dict, user_prefs: UserPreferences) -> float:
    """
    計算品種的額外加分，評估品種的特殊特徵對使用者需求的適配性。
    
    這個函數考慮四個主要面向：
    1. 壽命評估：考慮飼養的長期承諾
    2. 性格特徵評估：評估品種性格與使用者需求的匹配度
    3. 環境適應性：評估品種在特定生活環境中的表現
    4. 家庭相容性：特別關注品種與家庭成員的互動
    """
    bonus = 0.0
    temperament = breed_info.get('Temperament', '').lower()
    description = breed_info.get('Description', '').lower()
    
    # 壽命評估 - 重新設計以反映更實際的考量
    try:
        lifespan = breed_info.get('Lifespan', '10-12 years')
        years = [int(x) for x in lifespan.split('-')[0].split()[0:1]]
        avg_years = float(years[0])
        
        # 根據壽命長短給予不同程度的獎勵或懲罰
        if avg_years < 8:
            bonus -= 0.08  # 短壽命可能帶來情感負擔
        elif avg_years < 10:
            bonus -= 0.04  # 稍短壽命輕微降低評分
        elif avg_years > 13:
            bonus += 0.06  # 長壽命適度加分
        elif avg_years > 15:
            bonus += 0.08  # 特別長壽的品種獲得更多加分
    except:
        pass

    # 性格特徵評估 - 擴充並細化評分標準
    positive_traits = {
        'friendly': 0.08,           # 提高友善性的重要性
        'gentle': 0.08,            # 溫和性格更受歡迎
        'patient': 0.07,           # 耐心是重要特質
        'intelligent': 0.06,        # 聰明但不過分重要
        'adaptable': 0.06,         # 適應性佳的特質
        'affectionate': 0.06,      # 親密性很重要
        'easy-going': 0.05,        # 容易相處的性格
        'calm': 0.05              # 冷靜的特質
    }
    
    negative_traits = {
        'aggressive': -0.15,       # 嚴重懲罰攻擊性
        'stubborn': -0.10,        # 固執性格不易處理
        'dominant': -0.10,        # 支配性可能造成問題
        'aloof': -0.08,          # 冷漠性格影響互動
        'nervous': -0.08,         # 緊張性格需要更多關注
        'protective': -0.06       # 過度保護可能有風險
    }
    
    # 性格評分計算 - 加入累積效應
    personality_score = 0
    positive_count = 0
    negative_count = 0
    
    for trait, value in positive_traits.items():
        if trait in temperament:
            personality_score += value
            positive_count += 1
            
    for trait, value in negative_traits.items():
        if trait in temperament:
            personality_score += value
            negative_count += 1
    
    # 多重特徵的累積效應
    if positive_count > 2:
        personality_score *= (1 + (positive_count - 2) * 0.1)
    if negative_count > 1:
        personality_score *= (1 - (negative_count - 1) * 0.15)
    
    bonus += max(-0.25, min(0.25, personality_score))

      exercise_match = calculate_exercise_match(
        breed_info.get('Exercise_Needs', 'MODERATE'),
        user_prefs.exercise_time,
        user_prefs.exercise_type
    )
    bonus += exercise_match
    
    # 運動類型特性評估
    exercise_traits = {
        'active_training': {
            'athletic': 0.10,
            'energetic': 0.08,
            'working': 0.08,
            'intelligent': 0.06
        },
        'moderate_activity': {
            'adaptable': 0.08,
            'balanced': 0.06,
            'versatile': 0.06,
            'steady': 0.04
        },
        'light_walks': {
            'calm': 0.08,
            'gentle': 0.06,
            'easy-going': 0.06,
            'patient': 0.04
        }
    }
    
    # 計算運動類型特性匹配度
    if user_prefs.exercise_type in exercise_traits:
        trait_score = 0
        matched_traits = 0
        for trait, value in exercise_traits[user_prefs.exercise_type].items():
            if trait in temperament:
                trait_score += value
                matched_traits += 1
        if matched_traits > 0:
            bonus += min(0.15, trait_score * (1 + (matched_traits - 1) * 0.1))
    

    # 適應性評估 - 根據具體環境給予更細緻的評分
    adaptability_bonus = 0.0
    if breed_info.get('Size') == "Small" and user_prefs.living_space == "apartment":
        adaptability_bonus += 0.08  # 小型犬更適合公寓
    
    # 環境適應性評估
    if 'adaptable' in temperament or 'versatile' in temperament:
        if user_prefs.living_space == "apartment":
            adaptability_bonus += 0.10  # 適應性在公寓環境更重要
        else:
            adaptability_bonus += 0.05  # 其他環境仍有加分
            
    # 氣候適應性
    description = breed_info.get('Description', '').lower()
    climate = user_prefs.climate
    if climate == 'hot':
        if 'heat tolerant' in description or 'warm climate' in description:
            adaptability_bonus += 0.08
        elif 'thick coat' in description or 'cold climate' in description:
            adaptability_bonus -= 0.10
    elif climate == 'cold':
        if 'thick coat' in description or 'cold climate' in description:
            adaptability_bonus += 0.08
        elif 'heat tolerant' in description or 'short coat' in description:
            adaptability_bonus -= 0.10
            
    bonus += min(0.15, adaptability_bonus)

    # 家庭相容性評估 - 特別關注有孩童的家庭
    if user_prefs.has_children:
        family_traits = {
            'good with children': 0.12,  # 提高與孩童相處的重要性
            'patient': 0.10,
            'gentle': 0.10,
            'tolerant': 0.08,
            'playful': 0.06
        }
        
        unfriendly_traits = {
            'aggressive': -0.15,       # 加重攻擊性的懲罰
            'nervous': -0.12,         # 緊張特質可能有風險
            'protective': -0.10,      # 過度保護性需要注意
            'territorial': -0.08      # 地域性可能造成問題
        }
        
        # 根據孩童年齡調整評分權重
        age_adjustments = {
            'toddler': {
                'bonus_mult': 0.6,    # 降低正面特質的獎勵
                'penalty_mult': 1.5    # 加重負面特質的懲罰
            },
            'school_age': {
                'bonus_mult': 1.0,
                'penalty_mult': 1.0
            },
            'teenager': {
                'bonus_mult': 1.2,    # 提高正面特質的獎勵
                'penalty_mult': 0.8    # 降低負面特質的懲罰
            }
        }
        
        adj = age_adjustments.get(user_prefs.children_age, 
                                {'bonus_mult': 1.0, 'penalty_mult': 1.0})
        
        # 計算家庭相容性分數
        family_score = 0
        for trait, value in family_traits.items():
            if trait in temperament:
                family_score += value * adj['bonus_mult']
                
        for trait, value in unfriendly_traits.items():
            if trait in temperament:
                family_score += value * adj['penalty_mult']
        
        bonus += min(0.20, max(-0.30, family_score))

    # 確保總體加分在合理範圍內，但允許更大的變化
    return min(0.5, max(-0.35, bonus))


# @staticmethod
# def calculate_additional_factors(breed_info: dict, user_prefs: 'UserPreferences') -> dict:
#     """計算額外的評估因素"""
#     factors = {
#         'versatility': 0.0,        # 多功能性
#         'trainability': 0.0,       # 可訓練度
#         'energy_level': 0.0,       # 能量水平
#         'grooming_needs': 0.0,     # 美容需求
#         'social_needs': 0.0,       # 社交需求
#         'weather_adaptability': 0.0 # 氣候適應性
#     }
    
#     temperament = breed_info.get('Temperament', '').lower()
#     size = breed_info.get('Size', 'Medium')
    
#     # 1. 多功能性評估
#     versatile_traits = ['intelligent', 'adaptable', 'trainable', 'athletic']
#     working_roles = ['working', 'herding', 'hunting', 'sporting', 'companion']
    
#     trait_score = sum(0.2 for trait in versatile_traits if trait in temperament)
#     role_score = sum(0.2 for role in working_roles if role in breed_info.get('Description', '').lower())
    
#     factors['versatility'] = min(1.0, trait_score + role_score)
    
#     # 2. 可訓練度評估
#     trainable_traits = {
#         'intelligent': 0.3,
#         'eager to please': 0.3,
#         'trainable': 0.2,
#         'quick learner': 0.2
#     }
#     factors['trainability'] = min(1.0, sum(value for trait, value in trainable_traits.items() 
#                                          if trait in temperament))
    
#     # 3. 能量水平評估
#     exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper()
#     energy_levels = {
#         'VERY HIGH': 1.0,
#         'HIGH': 0.8,
#         'MODERATE': 0.6,
#         'LOW': 0.4,
#         'VARIES': 0.6
#     }
#     factors['energy_level'] = energy_levels.get(exercise_needs, 0.6)
    
#     # 4. 美容需求評估
#     grooming_needs = breed_info.get('Grooming Needs', 'MODERATE').upper()
#     grooming_levels = {
#         'HIGH': 1.0,
#         'MODERATE': 0.6,
#         'LOW': 0.3
#     }
#     coat_penalty = 0.2 if any(term in breed_info.get('Description', '').lower() 
#                              for term in ['long coat', 'double coat']) else 0
#     factors['grooming_needs'] = min(1.0, grooming_levels.get(grooming_needs, 0.6) + coat_penalty)
    
#     # 5. 社交需求評估
#     social_traits = ['friendly', 'social', 'affectionate', 'people-oriented']
#     antisocial_traits = ['independent', 'aloof', 'reserved']
    
#     social_score = sum(0.25 for trait in social_traits if trait in temperament)
#     antisocial_score = sum(-0.2 for trait in antisocial_traits if trait in temperament)
#     factors['social_needs'] = min(1.0, max(0.0, social_score + antisocial_score))
    
#     # 6. 氣候適應性評估
#     climate_terms = {
#         'cold': ['thick coat', 'winter', 'cold climate'],
#         'hot': ['short coat', 'warm climate', 'heat tolerant'],
#         'moderate': ['adaptable', 'all climate']
#     }
    
#     climate_matches = sum(1 for term in climate_terms[user_prefs.climate] 
#                         if term in breed_info.get('Description', '').lower())
#     factors['weather_adaptability'] = min(1.0, climate_matches * 0.3 + 0.4)  # 基礎分0.4

#     return factors


@staticmethod
def calculate_additional_factors(breed_info: dict, user_prefs: 'UserPreferences') -> dict:
    """
    計算額外的評估因素，結合品種特性與使用者需求的全面評估系統
    
    此函數整合了：
    1. 多功能性評估 - 品種的多樣化能力
    2. 訓練性評估 - 學習和服從能力
    3. 能量水平評估 - 活力和運動需求
    4. 美容需求評估 - 護理和維護需求
    5. 社交需求評估 - 與人互動的需求程度
    6. 氣候適應性 - 對環境的適應能力
    7. 運動類型匹配 - 與使用者運動習慣的契合度
    8. 生活方式適配 - 與使用者日常生活的匹配度
    """
    factors = {
        'versatility': 0.0,        # 多功能性
        'trainability': 0.0,       # 可訓練度
        'energy_level': 0.0,       # 能量水平
        'grooming_needs': 0.0,     # 美容需求
        'social_needs': 0.0,       # 社交需求
        'weather_adaptability': 0.0,# 氣候適應性
        'exercise_match': 0.0,     # 運動匹配度
        'lifestyle_fit': 0.0       # 生活方式適配度
    }
    
    temperament = breed_info.get('Temperament', '').lower()
    description = breed_info.get('Description', '').lower()
    size = breed_info.get('Size', 'Medium')
    
    # 1. 多功能性評估 - 加強品種用途評估
    versatile_traits = {
        'intelligent': 0.25,
        'adaptable': 0.25,
        'trainable': 0.20,
        'athletic': 0.15,
        'versatile': 0.15
    }
    
    working_roles = {
        'working': 0.20,
        'herding': 0.15,
        'hunting': 0.15,
        'sporting': 0.15,
        'companion': 0.10
    }
    
    # 計算特質分數
    trait_score = sum(value for trait, value in versatile_traits.items() 
                     if trait in temperament)
    
    # 計算角色分數
    role_score = sum(value for role, value in working_roles.items() 
                    if role in description)
    
    # 根據使用者需求調整多功能性評分
    purpose_traits = {
        'light_walks': ['calm', 'gentle', 'easy-going'],
        'moderate_activity': ['adaptable', 'balanced', 'versatile'],
        'active_training': ['intelligent', 'trainable', 'working']
    }
    
    if user_prefs.exercise_type in purpose_traits:
        matching_traits = sum(1 for trait in purpose_traits[user_prefs.exercise_type] 
                            if trait in temperament)
        trait_score += matching_traits * 0.15
    
    factors['versatility'] = min(1.0, trait_score + role_score)
    
    # 2. 訓練性評估 - 考慮使用者經驗
    trainable_traits = {
        'intelligent': 0.3,
        'eager to please': 0.3,
        'trainable': 0.2,
        'quick learner': 0.2,
        'obedient': 0.2
    }
    
    base_trainability = sum(value for trait, value in trainable_traits.items() 
                          if trait in temperament)
    
    # 根據使用者經驗調整訓練性評分
    experience_multipliers = {
        'beginner': 1.2,    # 新手更需要容易訓練的狗
        'intermediate': 1.0,
        'advanced': 0.8     # 專家能處理較難訓練的狗
    }
    
    factors['trainability'] = min(1.0, base_trainability * 
                                experience_multipliers.get(user_prefs.experience_level, 1.0))
    
    # 3. 能量水平評估 - 強化運動需求匹配
    exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper()
    energy_levels = {
        'VERY HIGH': {
            'score': 1.0,
            'min_exercise': 120,
            'ideal_exercise': 150
        },
        'HIGH': {
            'score': 0.8,
            'min_exercise': 90,
            'ideal_exercise': 120
        },
        'MODERATE': {
            'score': 0.6,
            'min_exercise': 60,
            'ideal_exercise': 90
        },
        'LOW': {
            'score': 0.4,
            'min_exercise': 30,
            'ideal_exercise': 60
        }
    }
    
    breed_energy = energy_levels.get(exercise_needs, energy_levels['MODERATE'])
    
    # 計算運動時間匹配度
    if user_prefs.exercise_time >= breed_energy['ideal_exercise']:
        energy_score = breed_energy['score']
    else:
        # 如果運動時間不足，按比例降低分數
        deficit_ratio = max(0.4, user_prefs.exercise_time / breed_energy['ideal_exercise'])
        energy_score = breed_energy['score'] * deficit_ratio
    
    factors['energy_level'] = energy_score
    
    # 4. 美容需求評估 - 加入更多毛髮類型考量
    grooming_needs = breed_info.get('Grooming Needs', 'MODERATE').upper()
    grooming_levels = {
        'HIGH': 1.0,
        'MODERATE': 0.6,
        'LOW': 0.3
    }
    
    # 特殊毛髮類型評估
    coat_adjustments = 0
    if 'long coat' in description:
        coat_adjustments += 0.2
    if 'double coat' in description:
        coat_adjustments += 0.15
    if 'curly' in description:
        coat_adjustments += 0.15
        
    # 根據使用者承諾度調整
    commitment_multipliers = {
        'low': 1.5,     # 低承諾度時加重美容需求的影響
        'medium': 1.0,
        'high': 0.8     # 高承諾度時降低美容需求的影響
    }
    
    base_grooming = grooming_levels.get(grooming_needs, 0.6) + coat_adjustments
    factors['grooming_needs'] = min(1.0, base_grooming * 
                                  commitment_multipliers.get(user_prefs.grooming_commitment, 1.0))
    
    # 5. 社交需求評估 - 加強家庭情況考量
    social_traits = {
        'friendly': 0.25,
        'social': 0.25,
        'affectionate': 0.20,
        'people-oriented': 0.20
    }
    
    antisocial_traits = {
        'independent': -0.20,
        'aloof': -0.20,
        'reserved': -0.15
    }
    
    social_score = sum(value for trait, value in social_traits.items() 
                      if trait in temperament)
    antisocial_score = sum(value for trait, value in antisocial_traits.items() 
                          if trait in temperament)
    
    # 家庭情況調整
    if user_prefs.has_children:
        child_friendly_bonus = 0.2 if 'good with children' in temperament else 0
        social_score += child_friendly_bonus
    
    factors['social_needs'] = min(1.0, max(0.0, social_score + antisocial_score))
    
    # 6. 氣候適應性評估 - 更細緻的環境適應評估
    climate_traits = {
        'cold': {
            'positive': ['thick coat', 'winter', 'cold climate'],
            'negative': ['short coat', 'heat sensitive']
        },
        'hot': {
            'positive': ['short coat', 'heat tolerant', 'warm climate'],
            'negative': ['thick coat', 'cold climate']
        },
        'moderate': {
            'positive': ['adaptable', 'all climate'],
            'negative': []
        }
    }
    
    climate_score = 0.4  # 基礎分數
    if user_prefs.climate in climate_traits:
        # 正面特質加分
        climate_score += sum(0.2 for term in climate_traits[user_prefs.climate]['positive'] 
                           if term in description)
        # 負面特質減分
        climate_score -= sum(0.2 for term in climate_traits[user_prefs.climate]['negative'] 
                           if term in description)
    
    factors['weather_adaptability'] = min(1.0, max(0.0, climate_score))
    
    # 7. 運動類型匹配評估
    exercise_type_traits = {
        'light_walks': ['calm', 'gentle'],
        'moderate_activity': ['adaptable', 'balanced'],
        'active_training': ['athletic', 'energetic']
    }
    
    if user_prefs.exercise_type in exercise_type_traits:
        match_score = sum(0.25 for trait in exercise_type_traits[user_prefs.exercise_type] 
                         if trait in temperament)
        factors['exercise_match'] = min(1.0, match_score + 0.5)  # 基礎分0.5
    
    # 8. 生活方式適配評估
    lifestyle_score = 0.5  # 基礎分數
    
    # 空間適配
    if user_prefs.living_space == 'apartment':
        if size == 'Small':
            lifestyle_score += 0.2
        elif size == 'Large':
            lifestyle_score -= 0.2
    elif user_prefs.living_space == 'house_large':
        if size in ['Large', 'Giant']:
            lifestyle_score += 0.2
    
    # 時間可用性適配
    time_availability_bonus = {
        'limited': -0.1,
        'moderate': 0,
        'flexible': 0.1
    }
    lifestyle_score += time_availability_bonus.get(user_prefs.time_availability, 0)
    
    factors['lifestyle_fit'] = min(1.0, max(0.0, lifestyle_score))
    
    return factors


def calculate_compatibility_score(breed_info: dict, user_prefs: UserPreferences) -> dict:
    """計算品種與使用者條件的相容性分數的優化版本"""
    try:
        print(f"Processing breed: {breed_info.get('Breed', 'Unknown')}")
        print(f"Breed info keys: {breed_info.keys()}")
        
        if 'Size' not in breed_info:
            print("Missing Size information")
            raise KeyError("Size information missing")
            

        # def calculate_space_score(size: str, living_space: str, has_yard: bool, exercise_needs: str) -> float:
        #     # 重新設計基礎分數矩陣
        #     base_scores = {
        #         "Small": {
        #             "apartment": 1.0,      # 小型犬最適合公寓
        #             "house_small": 0.95,   # 在大房子反而稍微降分
        #             "house_large": 0.85    # 可能浪費空間
        #         },
        #         "Medium": {
        #             "apartment": 0.45,     # 中型犬在公寓明顯受限
        #             "house_small": 0.85,
        #             "house_large": 1.0
        #         },
        #         "Large": {
        #             "apartment": 0.15,     # 大型犬在公寓極不適合
        #             "house_small": 0.60,   # 在小房子仍然受限
        #             "house_large": 1.0
        #         },
        #         "Giant": {
        #             "apartment": 0.1,      # 更嚴格的限制
        #             "house_small": 0.45,
        #             "house_large": 1.0
        #         }
        #     }
            
        #     # 取得基礎分數
        #     base_score = base_scores.get(size, base_scores["Medium"])[living_space]
            
        #     # 運動需求調整更明顯
        #     exercise_adjustments = {
        #         "Very High": {
        #             "apartment": -0.25,    # 在公寓更嚴重的懲罰
        #             "house_small": -0.15,
        #             "house_large": -0.05
        #         },
        #         "High": {
        #             "apartment": -0.20,
        #             "house_small": -0.10,
        #             "house_large": 0
        #         },
        #         "Moderate": {
        #             "apartment": -0.10,
        #             "house_small": -0.05,
        #             "house_large": 0
        #         },
        #         "Low": {
        #             "apartment": 0.05,
        #             "house_small": 0,
        #             "house_large": 0
        #         }
        #     }
            
        #     # 根據空間類型獲取對應的運動調整
        #     adjustment = exercise_adjustments.get(exercise_needs, 
        #                                         exercise_adjustments["Moderate"])[living_space]
            
        #     # 院子獎勵也要根據犬種大小調整
        #     yard_bonus = 0
        #     if has_yard:
        #         if size in ["Large", "Giant"]:
        #             yard_bonus = 0.20 if living_space != "apartment" else 0.10
        #         elif size == "Medium":
        #             yard_bonus = 0.15 if living_space != "apartment" else 0.08
        #         else:
        #             yard_bonus = 0.10 if living_space != "apartment" else 0.05
                    
        #     final_score = base_score + adjustment + yard_bonus
        #     return min(1.0, max(0.1, final_score))


        def calculate_space_score(size: str, living_space: str, has_yard: bool, exercise_needs: str) -> float:
            """
            優化的空間分數計算函數
            
            主要改進：
            1. 更均衡的基礎分數分配
            2. 更細緻的空間需求評估
            3. 強化運動需求與空間的關聯性
            """
            # 重新設計基礎分數矩陣，降低普遍分數以增加區別度
            base_scores = {
                "Small": {
                    "apartment": 0.85,    # 降低滿分機會
                    "house_small": 0.80,  # 小型犬不應在大空間得到太高分數
                    "house_large": 0.75   # 避免小型犬總是得到最高分
                },
                "Medium": {
                    "apartment": 0.45,    # 維持對公寓環境的限制
                    "house_small": 0.75,  # 適中的分數
                    "house_large": 0.85   # 給予合理的獎勵
                },
                "Large": {
                    "apartment": 0.15,    # 加重對大型犬在公寓的限制
                    "house_small": 0.65,  # 中等適合度
                    "house_large": 0.90   # 最適合的環境
                },
                "Giant": {
                    "apartment": 0.10,    # 更嚴格的限制
                    "house_small": 0.45,  # 顯著的空間限制
                    "house_large": 0.95   # 最理想的配對
                }
            }
            
            # 取得基礎分數
            base_score = base_scores.get(size, base_scores["Medium"])[living_space]
            
            # 運動需求相關的調整更加動態
            exercise_adjustments = {
                "Very High": {
                    "apartment": -0.25,    # 加重在受限空間的懲罰
                    "house_small": -0.15,
                    "house_large": -0.05
                },
                "High": {
                    "apartment": -0.20,
                    "house_small": -0.10,
                    "house_large": 0
                },
                "Moderate": {
                    "apartment": -0.10,
                    "house_small": -0.05,
                    "house_large": 0
                },
                "Low": {
                    "apartment": 0.05,     # 低運動需求在小空間反而有優勢
                    "house_small": 0,
                    "house_large": -0.05   # 輕微降低評分，因為空間可能過大
                }
            }
            
            # 根據空間類型獲取運動需求調整
            adjustment = exercise_adjustments.get(exercise_needs, 
                                                exercise_adjustments["Moderate"])[living_space]
            
            # 院子效益根據品種大小和運動需求動態調整
            if has_yard:
                yard_bonus = {
                    "Giant": 0.20,
                    "Large": 0.15,
                    "Medium": 0.10,
                    "Small": 0.05
                }.get(size, 0.10)
                
                # 運動需求會影響院子的重要性
                if exercise_needs in ["Very High", "High"]:
                    yard_bonus *= 1.2
                elif exercise_needs == "Low":
                    yard_bonus *= 0.8
                    
                current_score = base_score + adjustment + yard_bonus
            else:
                current_score = base_score + adjustment
                
            # 確保分數在合理範圍內，但避免極端值
            return min(0.95, max(0.15, current_score))
            

        # def calculate_exercise_score(breed_needs: str, exercise_time: int) -> float:
        #     """
        #     優化的運動需求評分系統
            
        #     Parameters:
        #     breed_needs: str - 品種的運動需求等級
        #     exercise_time: int - 使用者可提供的運動時間（分鐘）
            
        #     改進：
        #     1. 更細緻的運動需求評估
        #     2. 更合理的時間匹配計算
        #     3. 避免極端評分
        #     """
        #     # 基礎運動需求評估
        #     exercise_needs = {
        #         'VERY HIGH': {'min': 120, 'ideal': 150, 'max': 180},
        #         'HIGH': {'min': 90, 'ideal': 120, 'max': 150},
        #         'MODERATE': {'min': 45, 'ideal': 60, 'max': 90},
        #         'LOW': {'min': 20, 'ideal': 30, 'max': 45},
        #         'VARIES': {'min': 30, 'ideal': 60, 'max': 90}
        #     }
            
        #     breed_need = exercise_needs.get(breed_needs.strip().upper(), exercise_needs['MODERATE'])
            
        #     # 基礎時間匹配度計算
        #     if exercise_time >= breed_need['ideal']:
        #         if exercise_time > breed_need['max']:
        #             # 運動時間過長，稍微降低分數
        #             time_score = 0.9
        #         else:
        #             time_score = 1.0
        #     elif exercise_time >= breed_need['min']:
        #         # 在最小需求和理想需求之間，線性計算分數
        #         time_score = 0.7 + (exercise_time - breed_need['min']) / (breed_need['ideal'] - breed_need['min']) * 0.3
        #     else:
        #         # 運動時間不足，但仍根據比例給予分數
        #         time_score = max(0.3, 0.7 * (exercise_time / breed_need['min']))
            
        #     # 確保分數在合理範圍內
        #     return min(1.0, max(0.3, time_score))


        def calculate_exercise_match(breed_needs: str, exercise_time: int, exercise_type: str) -> float:
            """
            精確評估品種運動需求與使用者運動條件的匹配度
            
            Parameters:
            breed_needs: 品種的運動需求等級
            exercise_time: 使用者能提供的運動時間（分鐘）
            exercise_type: 使用者偏好的運動類型
            
            Returns:
            float: -0.2 到 0.2 之間的匹配分數
            """
            # 定義更細緻的運動需求等級
            exercise_levels = {
                'VERY HIGH': {
                    'min': 120,
                    'ideal': 150,
                    'max': 180,
                    'intensity': 'high',
                    'sessions': 'multiple',
                    'preferred_types': ['active_training', 'intensive_exercise']
                },
                'HIGH': {
                    'min': 90,
                    'ideal': 120,
                    'max': 150,
                    'intensity': 'moderate_high',
                    'sessions': 'multiple',
                    'preferred_types': ['active_training', 'moderate_activity']
                },
                'MODERATE HIGH': {
                    'min': 70,
                    'ideal': 90,
                    'max': 120,
                    'intensity': 'moderate',
                    'sessions': 'flexible',
                    'preferred_types': ['moderate_activity', 'active_training']
                },
                'MODERATE': {
                    'min': 45,
                    'ideal': 60,
                    'max': 90,
                    'intensity': 'moderate',
                    'sessions': 'flexible',
                    'preferred_types': ['moderate_activity', 'light_walks']
                },
                'MODERATE LOW': {
                    'min': 30,
                    'ideal': 45,
                    'max': 70,
                    'intensity': 'light_moderate',
                    'sessions': 'flexible',
                    'preferred_types': ['light_walks', 'moderate_activity']
                },
                'LOW': {
                    'min': 15,
                    'ideal': 30,
                    'max': 45,
                    'intensity': 'light',
                    'sessions': 'single',
                    'preferred_types': ['light_walks']
                }
            }
            
            # 獲取品種的運動需求配置
            breed_level = exercise_levels.get(breed_needs.upper(), exercise_levels['MODERATE'])
            
            # 計算時間匹配度（使用更平滑的評分曲線）
            if exercise_time >= breed_level['ideal']:
                if exercise_time > breed_level['max']:
                    # 運動時間過長，適度降分
                    time_score = 0.15 - (0.05 * (exercise_time - breed_level['max']) / 30)
                else:
                    time_score = 0.15
            elif exercise_time >= breed_level['min']:
                # 在最小需求和理想需求之間，線性計算分數
                time_ratio = (exercise_time - breed_level['min']) / (breed_level['ideal'] - breed_level['min'])
                time_score = 0.05 + (time_ratio * 0.10)
            else:
                # 運動時間不足，根據差距程度扣分
                time_ratio = max(0, exercise_time / breed_level['min'])
                time_score = -0.15 * (1 - time_ratio)
            
            # 運動類型匹配度評估
            type_score = 0.0
            if exercise_type in breed_level['preferred_types']:
                type_score = 0.05
                if exercise_type == breed_level['preferred_types'][0]:
                    type_score = 0.08  # 最佳匹配類型給予更高分數
            
            return max(-0.2, min(0.2, time_score + type_score))


        def calculate_grooming_score(breed_needs: str, user_commitment: str, breed_size: str) -> float:
            """
            計算美容需求分數，強化美容維護需求與使用者承諾度的匹配評估。
            這個函數特別注意品種大小對美容工作的影響，以及不同程度的美容需求對時間投入的要求。
            """
            # 重新設計基礎分數矩陣，讓美容需求的差異更加明顯
            base_scores = {
                "High": {
                    "low": 0.20,      # 高需求對低承諾極不合適，顯著降低初始分數
                    "medium": 0.65,   # 中等承諾仍有挑戰
                    "high": 1.0       # 高承諾最適合
                },
                "Moderate": {
                    "low": 0.45,      # 中等需求對低承諾有困難
                    "medium": 0.85,   # 較好的匹配
                    "high": 0.95      # 高承諾會有餘力
                },
                "Low": {
                    "low": 0.90,      # 低需求對低承諾很合適
                    "medium": 0.85,   # 略微降低以反映可能過度投入
                    "high": 0.80      # 可能造成資源浪費
                }
            }
        
            # 取得基礎分數
            base_score = base_scores.get(breed_needs, base_scores["Moderate"])[user_commitment]
        
            # 根據品種大小調整美容工作量
            size_adjustments = {
                "Giant": {
                    "low": -0.35,     # 大型犬的美容工作量顯著增加
                    "medium": -0.20,
                    "high": -0.10
                },
                "Large": {
                    "low": -0.25,
                    "medium": -0.15,
                    "high": -0.05
                },
                "Medium": {
                    "low": -0.15,
                    "medium": -0.10,
                    "high": 0
                },
                "Small": {
                    "low": -0.10,
                    "medium": -0.05,
                    "high": 0
                }
            }
        
            # 應用體型調整
            size_adjustment = size_adjustments.get(breed_size, size_adjustments["Medium"])[user_commitment]
            current_score = base_score + size_adjustment
        
            # 特殊毛髮類型的額外調整
            def get_coat_adjustment(breed_description: str, commitment: str) -> float:
                """
                評估特殊毛髮類型所需的額外維護工作
                """
                adjustments = 0
                
                # 長毛品種需要更多維護
                if 'long coat' in breed_description.lower():
                    coat_penalties = {
                        'low': -0.20,
                        'medium': -0.15,
                        'high': -0.05
                    }
                    adjustments += coat_penalties[commitment]
                    
                # 雙層毛的品種掉毛量更大
                if 'double coat' in breed_description.lower():
                    double_coat_penalties = {
                        'low': -0.15,
                        'medium': -0.10,
                        'high': -0.05
                    }
                    adjustments += double_coat_penalties[commitment]
                    
                # 捲毛品種需要定期專業修剪
                if 'curly' in breed_description.lower():
                    curly_penalties = {
                        'low': -0.15,
                        'medium': -0.10,
                        'high': -0.05
                    }
                    adjustments += curly_penalties[commitment]
                    
                return adjustments
        
            # 季節性考量
            def get_seasonal_adjustment(breed_description: str, commitment: str) -> float:
                """
                評估季節性掉毛對美容需求的影響
                """
                if 'seasonal shedding' in breed_description.lower():
                    seasonal_penalties = {
                        'low': -0.15,
                        'medium': -0.10,
                        'high': -0.05
                    }
                    return seasonal_penalties[commitment]
                return 0
        
            # 專業美容需求評估
            def get_professional_grooming_adjustment(breed_description: str, commitment: str) -> float:
                """
                評估需要專業美容服務的影響
                """
                if 'professional grooming' in breed_description.lower():
                    grooming_penalties = {
                        'low': -0.20,
                        'medium': -0.15,
                        'high': -0.05
                    }
                    return grooming_penalties[commitment]
                return 0
        
            # 應用所有額外調整
            # 由於這些是示例調整，實際使用時需要根據品種描述信息進行調整
            coat_adjustment = get_coat_adjustment("", user_commitment)
            seasonal_adjustment = get_seasonal_adjustment("", user_commitment)
            professional_adjustment = get_professional_grooming_adjustment("", user_commitment)
            
            final_score = current_score + coat_adjustment + seasonal_adjustment + professional_adjustment
        
            # 確保分數在有意義的範圍內，但允許更大的差異
            return max(0.1, min(1.0, final_score))


        def calculate_experience_score(care_level: str, user_experience: str, temperament: str) -> float:
            """
            計算使用者經驗與品種需求的匹配分數，加強經驗等級的影響力
            
            重要改進：
            1. 擴大基礎分數差異
            2. 加重困難特徵的懲罰
            3. 更細緻的品種特性評估
            """
            # 基礎分數矩陣 - 大幅擴大不同經驗等級的分數差異
            base_scores = {
                "High": {
                    "beginner": 0.10,      # 降低起始分，高難度品種對新手幾乎不推薦
                    "intermediate": 0.60,   # 中級玩家仍需謹慎
                    "advanced": 1.0        # 資深者能完全勝任
                },
                "Moderate": {
                    "beginner": 0.35,      # 適中難度對新手仍具挑戰
                    "intermediate": 0.80,   # 中級玩家較適合
                    "advanced": 1.0        # 資深者完全勝任
                },
                "Low": {
                    "beginner": 0.90,      # 新手友善品種
                    "intermediate": 0.95,   # 中級玩家幾乎完全勝任
                    "advanced": 1.0        # 資深者完全勝任
                }
            }
            
            # 取得基礎分數
            score = base_scores.get(care_level, base_scores["Moderate"])[user_experience]
            
            temperament_lower = temperament.lower()
            temperament_adjustments = 0.0
            
            # 根據經驗等級設定不同的特徵評估標準
            if user_experience == "beginner":
                # 新手不適合的特徵 - 更嚴格的懲罰
                difficult_traits = {
                    'stubborn': -0.30,        # 固執性格嚴重影響新手
                    'independent': -0.25,      # 獨立性高的品種不適合新手
                    'dominant': -0.25,         # 支配性強的品種需要經驗處理
                    'strong-willed': -0.20,    # 強勢性格需要技巧管理
                    'protective': -0.20,       # 保護性強需要適當訓練
                    'aloof': -0.15,           # 冷漠性格需要耐心培養
                    'energetic': -0.15,       # 活潑好動需要經驗引導
                    'aggressive': -0.35        # 攻擊傾向極不適合新手
                }
                
                # 新手友善的特徵 - 適度的獎勵
                easy_traits = {
                    'gentle': 0.05,            # 溫和性格適合新手
                    'friendly': 0.05,          # 友善性格容易相處
                    'eager to please': 0.08,   # 願意服從較容易訓練
                    'patient': 0.05,           # 耐心的特質有助於建立關係
                    'adaptable': 0.05,         # 適應性強較容易照顧
                    'calm': 0.06              # 冷靜的性格較好掌握
                }
                
                # 計算特徵調整
                for trait, penalty in difficult_traits.items():
                    if trait in temperament_lower:
                        temperament_adjustments += penalty
                
                for trait, bonus in easy_traits.items():
                    if trait in temperament_lower:
                        temperament_adjustments += bonus
                        
                # 品種類型特殊評估
                if 'terrier' in temperament_lower:
                    temperament_adjustments -= 0.20  # 梗類犬種通常不適合新手
                elif 'working' in temperament_lower:
                    temperament_adjustments -= 0.25  # 工作犬需要經驗豐富的主人
                elif 'guard' in temperament_lower:
                    temperament_adjustments -= 0.25  # 護衛犬需要專業訓練
                    
            elif user_experience == "intermediate":
                # 中級玩家的特徵評估
                moderate_traits = {
                    'stubborn': -0.15,        # 仍然需要注意，但懲罰較輕
                    'independent': -0.10,
                    'intelligent': 0.08,      # 聰明的特質可以好好發揮
                    'athletic': 0.06,         # 運動能力可以適當訓練
                    'versatile': 0.07,        # 多功能性可以開發
                    'protective': -0.08       # 保護性仍需注意
                }
                
                for trait, adjustment in moderate_traits.items():
                    if trait in temperament_lower:
                        temperament_adjustments += adjustment
                        
            else:  # advanced
                # 資深玩家能夠應對挑戰性特徵
                advanced_traits = {
                    'stubborn': 0.05,         # 困難特徵反而成為優勢
                    'independent': 0.05,
                    'intelligent': 0.10,
                    'protective': 0.05,
                    'strong-willed': 0.05
                }
                
                for trait, bonus in advanced_traits.items():
                    if trait in temperament_lower:
                        temperament_adjustments += bonus
            
            # 確保最終分數範圍更大，讓差異更明顯
            final_score = max(0.05, min(1.0, score + temperament_adjustments))
            
            return final_score

        def calculate_health_score(breed_name: str, user_prefs: UserPreferences) -> float:
            """
            計算品種健康分數，加強健康問題的影響力和與使用者敏感度的連結
            
            重要改進：
            1. 根據使用者的健康敏感度調整分數
            2. 更嚴格的健康問題評估
            3. 考慮多重健康問題的累積效應
            4. 加入遺傳疾病的特別考量
            """
            if breed_name not in breed_health_info:
                return 0.5
        
            health_notes = breed_health_info[breed_name]['health_notes'].lower()
            
            # 嚴重健康問題 - 加重扣分
            severe_conditions = {
                'hip dysplasia': -0.25,           # 髖關節發育不良，影響生活品質
                'heart disease': -0.25,           # 心臟疾病，需要長期治療
                'progressive retinal atrophy': -0.20,  # 進行性視網膜萎縮，導致失明
                'bloat': -0.22,                   # 胃扭轉，致命風險
                'epilepsy': -0.20,                # 癲癇，需要長期藥物控制
                'degenerative myelopathy': -0.20,  # 脊髓退化，影響行動能力
                'von willebrand disease': -0.18    # 血液凝固障礙
            }
            
            # 中度健康問題 - 適度扣分
            moderate_conditions = {
                'allergies': -0.12,               # 過敏問題，需要持續關注
                'eye problems': -0.15,            # 眼睛問題，可能需要手術
                'joint problems': -0.15,          # 關節問題，影響運動能力
                'hypothyroidism': -0.12,          # 甲狀腺功能低下，需要藥物治療
                'ear infections': -0.10,          # 耳道感染，需要定期清理
                'skin issues': -0.12              # 皮膚問題，需要特殊護理
            }
            
            # 輕微健康問題 - 輕微扣分
            minor_conditions = {
                'dental issues': -0.08,           # 牙齒問題，需要定期護理
                'weight gain tendency': -0.08,     # 易胖體質，需要控制飲食
                'minor allergies': -0.06,         # 輕微過敏，可控制
                'seasonal allergies': -0.06       # 季節性過敏
            }
        
            # 計算基礎健康分數
            health_score = 1.0
            
            # 健康問題累積效應計算
            condition_counts = {
                'severe': 0,
                'moderate': 0,
                'minor': 0
            }
            
            # 計算各等級健康問題的數量和影響
            for condition, penalty in severe_conditions.items():
                if condition in health_notes:
                    health_score += penalty
                    condition_counts['severe'] += 1
                    
            for condition, penalty in moderate_conditions.items():
                if condition in health_notes:
                    health_score += penalty
                    condition_counts['moderate'] += 1
                    
            for condition, penalty in minor_conditions.items():
                if condition in health_notes:
                    health_score += penalty
                    condition_counts['minor'] += 1
            
            # 多重問題的額外懲罰（累積效應）
            if condition_counts['severe'] > 1:
                health_score *= (0.85 ** (condition_counts['severe'] - 1))
            if condition_counts['moderate'] > 2:
                health_score *= (0.90 ** (condition_counts['moderate'] - 2))
            
            # 根據使用者健康敏感度調整分數
            sensitivity_multipliers = {
                'low': 1.1,      # 較不在意健康問題
                'medium': 1.0,   # 標準評估
                'high': 0.85     # 非常注重健康問題
            }
            
            health_score *= sensitivity_multipliers.get(user_prefs.health_sensitivity, 1.0)
        
            # 壽命影響評估
            try:
                lifespan = breed_health_info[breed_name].get('average_lifespan', '10-12')
                years = float(lifespan.split('-')[0])
                if years < 8:
                    health_score *= 0.85   # 短壽命顯著降低分數
                elif years < 10:
                    health_score *= 0.92   # 較短壽命輕微降低分數
                elif years > 13:
                    health_score *= 1.1    # 長壽命適度加分
            except:
                pass
        
            # 特殊健康優勢
            if 'generally healthy' in health_notes or 'hardy breed' in health_notes:
                health_score *= 1.15
            elif 'robust health' in health_notes or 'few health issues' in health_notes:
                health_score *= 1.1
        
            # 確保分數在合理範圍內，但允許更大的分數差異
            return max(0.1, min(1.0, health_score))
            

        def calculate_noise_score(breed_name: str, user_prefs: UserPreferences) -> float:
            """
            計算品種噪音分數，特別加強噪音程度與生活環境的關聯性評估
            """
            if breed_name not in breed_noise_info:
                return 0.5
        
            noise_info = breed_noise_info[breed_name]
            noise_level = noise_info['noise_level'].lower()
            noise_notes = noise_info['noise_notes'].lower()
        
            # 重新設計基礎噪音分數矩陣，考慮不同情境下的接受度
            base_scores = {
                'low': {
                    'low': 1.0,       # 安靜的狗對低容忍完美匹配
                    'medium': 0.95,   # 安靜的狗對一般容忍很好
                    'high': 0.90      # 安靜的狗對高容忍當然可以
                },
                'medium': {
                    'low': 0.60,      # 一般吠叫對低容忍較困難
                    'medium': 0.90,   # 一般吠叫對一般容忍可接受
                    'high': 0.95      # 一般吠叫對高容忍很好
                },
                'high': {
                    'low': 0.25,      # 愛叫的狗對低容忍極不適合
                    'medium': 0.65,   # 愛叫的狗對一般容忍有挑戰
                    'high': 0.90      # 愛叫的狗對高容忍可以接受
                },
                'varies': {
                    'low': 0.50,      # 不確定的情況對低容忍風險較大
                    'medium': 0.75,   # 不確定的情況對一般容忍可嘗試
                    'high': 0.85      # 不確定的情況對高容忍問題較小
                }
            }
        
            # 取得基礎分數
            base_score = base_scores.get(noise_level, {'low': 0.6, 'medium': 0.75, 'high': 0.85})[user_prefs.noise_tolerance]
        
            # 吠叫原因評估，根據環境調整懲罰程度
            barking_penalties = {
                'separation anxiety': {
                    'apartment': -0.30,    # 在公寓對鄰居影響更大
                    'house_small': -0.25,
                    'house_large': -0.20
                },
                'excessive barking': {
                    'apartment': -0.25,
                    'house_small': -0.20,
                    'house_large': -0.15
                },
                'territorial': {
                    'apartment': -0.20,    # 在公寓更容易被觸發
                    'house_small': -0.15,
                    'house_large': -0.10
                },
                'alert barking': {
                    'apartment': -0.15,    # 公寓環境刺激較多
                    'house_small': -0.10,
                    'house_large': -0.08
                },
                'attention seeking': {
                    'apartment': -0.15,
                    'house_small': -0.12,
                    'house_large': -0.10
                }
            }
        
            # 計算環境相關的吠叫懲罰
            living_space = user_prefs.living_space
            barking_penalty = 0
            for trigger, penalties in barking_penalties.items():
                if trigger in noise_notes:
                    barking_penalty += penalties.get(living_space, -0.15)
        
            # 特殊情況評估
            special_adjustments = 0
            if user_prefs.has_children:
                # 孩童年齡相關調整
                child_age_adjustments = {
                    'toddler': {
                        'high': -0.20,     # 幼童對吵鬧更敏感
                        'medium': -0.15,
                        'low': -0.05
                    },
                    'school_age': {
                        'high': -0.15,
                        'medium': -0.10,
                        'low': -0.05
                    },
                    'teenager': {
                        'high': -0.10,
                        'medium': -0.05,
                        'low': -0.02
                    }
                }
                
                # 根據孩童年齡和噪音等級調整
                age_adj = child_age_adjustments.get(user_prefs.children_age, 
                                                  child_age_adjustments['school_age'])
                special_adjustments += age_adj.get(noise_level, -0.10)
        
            # 訓練性補償評估
            trainability_bonus = 0
            if 'responds well to training' in noise_notes:
                trainability_bonus = 0.12
            elif 'can be trained' in noise_notes:
                trainability_bonus = 0.08
            elif 'difficult to train' in noise_notes:
                trainability_bonus = 0.02
        
            # 夜間吠叫特別考量
            if 'night barking' in noise_notes or 'howls' in noise_notes:
                if user_prefs.living_space == 'apartment':
                    special_adjustments -= 0.15
                elif user_prefs.living_space == 'house_small':
                    special_adjustments -= 0.10
                else:
                    special_adjustments -= 0.05
        
            # 計算最終分數，確保更大的分數範圍
            final_score = base_score + barking_penalty + special_adjustments + trainability_bonus
            return max(0.1, min(1.0, final_score))
            

        # 1. 計算基礎分數
        print("\n=== 開始計算品種相容性分數 ===")
        print(f"處理品種: {breed_info.get('Breed', 'Unknown')}")
        print(f"品種信息: {breed_info}")
        print(f"使用者偏好: {vars(user_prefs)}")

        # 計算所有基礎分數並整合到字典中
        scores = {
            'space': calculate_space_score(
                breed_info['Size'], 
                user_prefs.living_space,
                user_prefs.yard_access != 'no_yard',
                breed_info.get('Exercise Needs', 'Moderate')
            ),
            'exercise': calculate_exercise_score(
                breed_info.get('Exercise Needs', 'Moderate'),
                user_prefs.exercise_time
            ),
            'grooming': calculate_grooming_score(
                breed_info.get('Grooming Needs', 'Moderate'),
                user_prefs.grooming_commitment.lower(),
                breed_info['Size']
            ),
            'experience': calculate_experience_score(
                breed_info.get('Care Level', 'Moderate'),
                user_prefs.experience_level,
                breed_info.get('Temperament', '')
            ),
            'health': calculate_health_score(
                breed_info.get('Breed', ''),
                user_prefs
            ),
            'noise': calculate_noise_score(
                breed_info.get('Breed', ''),
                user_prefs
            )
        }

        # 檢查關鍵不適配情況
        critical_issues = check_critical_matches(scores, user_prefs)
        if critical_issues['has_critical']:
            return apply_critical_penalty(scores, critical_issues)

        # 計算環境適應性加成
        adaptability_bonus = calculate_environmental_fit(breed_info, user_prefs)
        
        # 計算最終加權分數
        final_score = calculate_final_weighted_score(
            scores=scores,
            user_prefs=user_prefs,
            breed_info=breed_info,
            adaptability_bonus=adaptability_bonus
        )

        # 更新最終結果
        scores.update({
            'overall': final_score,
            'adaptability_bonus': adaptability_bonus
        })

        return scores

    except Exception as e:
        print(f"\n!!!!! 發生嚴重錯誤 !!!!!")
        print(f"錯誤類型: {type(e).__name__}")
        print(f"錯誤訊息: {str(e)}")
        print(f"完整錯誤追蹤:")
        print(traceback.format_exc())
        return {k: 0.6 for k in ['space', 'exercise', 'grooming', 'experience', 'health', 'noise', 'overall']}

def check_critical_matches(scores: dict, user_prefs: UserPreferences) -> dict:
    """評估是否存在極端不適配的情況"""
    critical_issues = {
        'has_critical': False,
        'reasons': []
    }

    # 檢查極端不適配情況
    if scores['space'] < 0.3:
        critical_issues['has_critical'] = True
        critical_issues['reasons'].append('space_incompatible')
    
    if scores['noise'] < 0.3 and user_prefs.living_space == 'apartment':
        critical_issues['has_critical'] = True
        critical_issues['reasons'].append('noise_incompatible')
    
    if scores['experience'] < 0.3 and user_prefs.experience_level == 'beginner':
        critical_issues['has_critical'] = True
        critical_issues['reasons'].append('too_challenging')

    return critical_issues

def apply_critical_penalty(scores: dict, critical_issues: dict) -> dict:
    """
    當發現關鍵不適配時，調整分數
    
    首先計算基礎整體分數，然後根據不同的關鍵問題應用懲罰係數
    """
    penalized_scores = scores.copy()
    penalty_factor = 0.6  # 基礎懲罰因子
    
    # 先計算基礎整體分數（使用簡單平均）
    base_overall = sum(scores.values()) / len(scores)
    penalized_scores['overall'] = base_overall
    
    # 根據不同的關鍵問題應用懲罰
    for reason in critical_issues['reasons']:
        if reason == 'space_incompatible':
            penalized_scores['overall'] *= penalty_factor
            penalized_scores['space'] *= penalty_factor
        elif reason == 'noise_incompatible':
            penalized_scores['overall'] *= penalty_factor
            penalized_scores['noise'] *= penalty_factor
        elif reason == 'too_challenging':
            penalized_scores['overall'] *= penalty_factor
            penalized_scores['experience'] *= penalty_factor
    
    # 確保所有分數都在有效範圍內
    for key in penalized_scores:
        penalized_scores[key] = max(0.1, min(1.0, penalized_scores[key]))
    
    return penalized_scores

def calculate_environmental_fit(breed_info: dict, user_prefs: UserPreferences) -> float:
    """計算品種與環境的適應性加成"""
    adaptability_score = 0.0
    description = breed_info.get('Description', '').lower()
    temperament = breed_info.get('Temperament', '').lower()
    
    # 環境適應性評估
    if user_prefs.living_space == 'apartment':
        if 'adaptable' in temperament or 'apartment' in description:
            adaptability_score += 0.1
        if breed_info.get('Size') == 'Small':
            adaptability_score += 0.05
    elif user_prefs.living_space == 'house_large':
        if 'active' in temperament or 'energetic' in description:
            adaptability_score += 0.1
            
    # 氣候適應性
    if user_prefs.climate in description or user_prefs.climate in temperament:
        adaptability_score += 0.05
        
    return min(0.2, adaptability_score)


def calculate_dynamic_weights(user_prefs: UserPreferences, breed_info: dict) -> dict:
    """
    根據使用者條件動態計算權重
    這個系統會根據具體情況調整各個評分項目的重要性
    """
    weights = {
        'space': 0.25,      # 降低基礎空間權重
        'exercise': 0.20,
        'grooming': 0.15,
        'experience': 0.15,
        'health': 0.15,
        'noise': 0.10
    }
    
    # 運動時間對權重的影響
    if user_prefs.exercise_time > 150:
        weights['exercise'] *= 1.4
        weights['space'] *= 0.8
    elif user_prefs.exercise_time < 30:
        weights['exercise'] *= 0.8
        weights['health'] *= 1.2
        
    # 居住環境對權重的影響
    if user_prefs.living_space == 'apartment':
        weights['noise'] *= 1.3
        weights['space'] *= 1.2
    elif user_prefs.living_space == 'house_large':
        weights['exercise'] *= 1.2
        weights['space'] *= 0.8
        
    # 經驗等級對權重的影響
    if user_prefs.experience_level == 'beginner':
        weights['experience'] *= 1.3
        weights['health'] *= 1.2
    
    # 有孩童時的權重調整
    if user_prefs.has_children:
        if user_prefs.children_age == 'toddler':
            weights['temperament'] = 0.20  # 新增性格權重
            weights['space'] *= 0.8
    
    # 重新正規化權重
    total = sum(weights.values())
    return {k: v/total for k, v in weights.items()}


def calculate_final_weighted_score(
    scores: dict,
    user_prefs: UserPreferences,
    breed_info: dict,
    adaptability_bonus: float
) -> float:
    """
    整合動態權重的最終分數計算系統
    """
    # 第一步：計算動態權重
    weights = calculate_dynamic_weights(user_prefs, breed_info)  # 內部函數
    
    # 第二步：計算基礎加權分數
    weighted_base = sum(score * weights[category] for category, score in scores.items())
    
    # 第三步：計算品種特性加成
    breed_bonus = calculate_breed_bonus(breed_info, user_prefs)
    
    # 第四步：最終分數計算
    final_score = (weighted_base * 0.70) + (breed_bonus * 0.20) + (adaptability_bonus * 0.10)
    
    # 第五步：分數轉換
    return amplify_score_extreme(final_score)
    

def amplify_score_extreme(score: float) -> float:
    """
    使用S型曲線進行分數轉換，加大差異
    """
    # 基礎範圍
    base_min = 0.65
    base_max = 0.95
    
    # 正規化
    normalized = (score - 0.5) / 0.5
    
    # S型曲線轉換
    sigmoid = 1 / (1 + math.exp(-normalized * 4))
    
    # 映射到目標範圍
    final = base_min + (base_max - base_min) * sigmoid
    
    return round(min(base_max, max(base_min, final)), 4)