import traceback from typing import Dict, Any from breed_health_info import breed_health_info from breed_noise_info import breed_noise_info class DimensionScoreCalculator: """ 維度評分計算器類別 負責計算各個維度的具體評分，包含空間、運動、美容、經驗、健康和噪音等維度 """ def __init__(self): """初始化維度評分計算器""" pass def calculate_space_score(self, size: str, living_space: str, has_yard: bool, exercise_needs: str) -> float: """ 計算空間適配性評分 完整實現原始版本的空間計算邏輯，包含： 1. 動態的基礎分數矩陣 2. 強化空間品質評估 3. 增加極端情況處理 4. 考慮不同空間組合的協同效應 """ def get_base_score(): # 基礎分數矩陣 - 更極端的分數分配 base_matrix = { "Small": { "apartment": { "no_yard": 0.85, # 小型犬在公寓仍然適合 "shared_yard": 0.90, # 共享院子提供額外活動空間 "private_yard": 0.95 # 私人院子最理想 }, "house_small": { "no_yard": 0.80, "shared_yard": 0.85, "private_yard": 0.90 }, "house_large": { "no_yard": 0.75, "shared_yard": 0.80, "private_yard": 0.85 } }, "Medium": { "apartment": { "no_yard": 0.75, "shared_yard": 0.85, "private_yard": 0.90 }, "house_small": { "no_yard": 0.80, "shared_yard": 0.90, "private_yard": 0.90 }, "house_large": { "no_yard": 0.85, "shared_yard": 0.90, "private_yard": 0.95 } }, "Large": { "apartment": { "no_yard": 0.70, "shared_yard": 0.80, "private_yard": 0.85 }, "house_small": { "no_yard": 0.75, "shared_yard": 0.85, "private_yard": 0.90 }, "house_large": { "no_yard": 0.85, "shared_yard": 0.90, "private_yard": 1.0 } }, "Giant": { "apartment": { "no_yard": 0.65, "shared_yard": 0.75, "private_yard": 0.80 }, "house_small": { "no_yard": 0.70, "shared_yard": 0.80, "private_yard": 0.85 }, "house_large": { "no_yard": 0.80, "shared_yard": 0.90, "private_yard": 1.0 } } } yard_type = "private_yard" if has_yard else "no_yard" return base_matrix.get(size, base_matrix["Medium"])[living_space][yard_type] def calculate_exercise_adjustment(): # 運動需求對空間評分的影響 exercise_impact = { "Very High": { "apartment": -0.10, "house_small": -0.05, "house_large": 0 }, "High": { "apartment": -0.08, "house_small": -0.05, "house_large": 0 }, "Moderate": { "apartment": -0.05, "house_small": -0.02, "house_large": 0 }, "Low": { "apartment": 0.10, "house_small": 0.05, "house_large": 0 } } return exercise_impact.get(exercise_needs, exercise_impact["Moderate"])[living_space] def calculate_yard_bonus(): # 院子效益評估更加細緻 if not has_yard: return 0 yard_benefits = { "Giant": { "Very High": 0.25, "High": 0.20, "Moderate": 0.15, "Low": 0.10 }, "Large": { "Very High": 0.20, "High": 0.15, "Moderate": 0.10, "Low": 0.05 }, "Medium": { "Very High": 0.15, "High": 0.10, "Moderate": 0.08, "Low": 0.05 }, "Small": { "Very High": 0.10, "High": 0.08, "Moderate": 0.05, "Low": 0.03 } } size_benefits = yard_benefits.get(size, yard_benefits["Medium"]) return size_benefits.get(exercise_needs, size_benefits["Moderate"]) def apply_extreme_case_adjustments(score): # 處理極端情況 if size == "Giant" and living_space == "apartment": return score * 0.85 if size == "Large" and living_space == "apartment" and exercise_needs == "Very High": return score * 0.85 if size == "Small" and living_space == "house_large" and exercise_needs == "Low": return score * 0.9 # 低運動需求的小型犬在大房子可能過於寬敞 return score # 計算最終分數 base_score = get_base_score() exercise_adj = calculate_exercise_adjustment() yard_bonus = calculate_yard_bonus() # 整合所有評分因素 initial_score = base_score + exercise_adj + yard_bonus # 應用極端情況調整 final_score = apply_extreme_case_adjustments(initial_score) # 確保分數在有效範圍內，但允許更極端的結果 return max(0.05, min(1.0, final_score)) def calculate_exercise_score(self, breed_needs: str, exercise_time: int, exercise_type: str, breed_size: str, living_space: str, breed_info: dict = None) -> float: """ 計算品種運動需求與使用者運動條件的匹配度。此函數特別著重： 1. 不同品種的運動耐受度差異 2. 運動時間與類型的匹配度 3. 極端運動量的嚴格限制 Parameters: breed_needs: 品種的運動需求等級 exercise_time: 使用者計劃的運動時間（分鐘） exercise_type: 運動類型（輕度/中度/高度） Returns: float: 0.1到1.0之間的匹配分數 """ # 定義每個運動需求等級的具體參數 exercise_levels = { 'VERY HIGH': { 'min': 120, # 最低需求 'ideal': 150, # 理想運動量 'max': 180, # 最大建議量 'type_weights': { # 不同運動類型的權重 'active_training': 1.0, 'moderate_activity': 0.6, 'light_walks': 0.3 } }, 'HIGH': { 'min': 90, 'ideal': 120, 'max': 150, 'type_weights': { 'active_training': 0.9, 'moderate_activity': 0.8, 'light_walks': 0.4 } }, 'MODERATE': { 'min': 45, 'ideal': 60, 'max': 90, 'type_weights': { 'active_training': 0.7, 'moderate_activity': 1.0, 'light_walks': 0.8 } }, 'LOW': { 'min': 15, 'ideal': 30, 'max': 45, 'type_weights': { 'active_training': 0.5, 'moderate_activity': 0.8, 'light_walks': 1.0 } } } # 獲取品種的運動參數 breed_level = exercise_levels.get(breed_needs.upper(), exercise_levels['MODERATE']) # 計算時間匹配度 def calculate_time_score(): """計算運動時間的匹配度，特別處理過度運動的情況""" if exercise_time < breed_level['min']: # 運動不足的嚴格懲罰 deficit_ratio = exercise_time / breed_level['min'] return max(0.1, deficit_ratio * 0.4) elif exercise_time <= breed_level['ideal']: # 理想範圍內的漸進提升 progress = (exercise_time - breed_level['min']) / (breed_level['ideal'] - breed_level['min']) return 0.6 + (progress * 0.4) elif exercise_time <= breed_level['max']: # 理想到最大範圍的平緩下降 excess_ratio = (exercise_time - breed_level['ideal']) / (breed_level['max'] - breed_level['ideal']) return 1.0 - (excess_ratio * 0.2) else: # 過度運動的顯著懲罰 excess = (exercise_time - breed_level['max']) / breed_level['max'] # 低運動需求品種的過度運動懲罰更嚴重 penalty_factor = 1.5 if breed_needs.upper() == 'LOW' else 1.0 return max(0.1, 0.8 - (excess * 0.5 * penalty_factor)) # 計算運動類型匹配度 def calculate_type_score(): """評估運動類型的適合度，考慮品種特性""" base_type_score = breed_level['type_weights'].get(exercise_type, 0.5) # 特殊情況處理 if breed_needs.upper() == 'LOW' and exercise_type == 'active_training': # 低運動需求品種不適合高強度運動 base_type_score *= 0.5 elif breed_needs.upper() == 'VERY HIGH' and exercise_type == 'light_walks': # 高運動需求品種需要更多強度 base_type_score *= 0.6 return base_type_score # 計算最終分數 time_score = calculate_time_score() type_score = calculate_type_score() # 根據運動需求等級調整權重 if breed_needs.upper() == 'LOW': # 低運動需求品種更重視運動類型的合適性 final_score = (time_score * 0.6) + (type_score * 0.4) elif breed_needs.upper() == 'VERY HIGH': # 高運動需求品種更重視運動時間的充足性 final_score = (time_score * 0.7) + (type_score * 0.3) else: final_score = (time_score * 0.65) + (type_score * 0.35) if breed_size in ['Large', 'Giant'] and living_space == 'apartment': if exercise_time >= 120: final_score = min(1.0, final_score * 1.2) # 極端情況的最終調整 if breed_needs.upper() == 'LOW' and exercise_time > breed_level['max'] * 2: # 低運動需求品種的過度運動顯著降分 final_score *= 0.6 elif breed_needs.upper() == 'VERY HIGH' and exercise_time < breed_level['min'] * 0.5: # 高運動需求品種運動嚴重不足降分 final_score *= 0.5 return max(0.1, min(1.0, final_score)) def calculate_grooming_score(self, 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": -0.20, # 大型犬的美容工作量顯著增加 "medium": -0.10, "high": -0.05 }, "Large": { "low": -0.15, "medium": -0.05, "high": 0 }, "Medium": { "low": -0.10, "medium": -0.05, "high": 0 }, "Small": { "low": -0.05, "medium": 0, "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 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(self, 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，更平衡的經驗等級影響 改進重點： 1. 提高初學者的基礎分數 2. 縮小經驗等級間的差距 3. 保持適度的區分度 """ # 基礎分數矩陣 - 更合理的分數分配 base_scores = { "High": { "beginner": 0.55, # 提高起始分，讓新手也有機會 "intermediate": 0.80, # 中級玩家有不錯的勝任能力 "advanced": 0.95 # 資深者幾乎完全勝任 }, "Moderate": { "beginner": 0.65, # 適中難度對新手更友善 "intermediate": 0.85, # 中級玩家相當適合 "advanced": 0.90 # 資深者完全勝任 }, "Low": { "beginner": 0.85, # 新手友善品種維持高分 "intermediate": 0.90, # 中級玩家幾乎完全勝任 "advanced": 0.90 # 資深者完全勝任 } } # 取得基礎分數 score = base_scores.get(care_level, base_scores["Moderate"])[user_experience] # 性格評估的權重也需要調整 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，降低懲罰程度 if user_experience == "beginner": difficult_traits = { 'stubborn': -0.15, # 降低懲罰程度 'independent': -0.12, 'dominant': -0.12, 'strong-willed': -0.10, 'protective': -0.10, 'aloof': -0.08, 'energetic': -0.08, 'aggressive': -0.20 # 保持較高懲罰，因為安全考慮 } easy_traits = { 'gentle': 0.08, # 提高獎勵以平衡 'friendly': 0.08, 'eager to please': 0.10, 'patient': 0.08, 'adaptable': 0.08, 'calm': 0.08 } # 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，降低懲罰程度 if 'terrier' in temperament_lower: temperament_adjustments -= 0.10 # 降低懲罰 elif 'working' in temperament_lower: temperament_adjustments -= 0.12 elif 'guard' in temperament_lower: temperament_adjustments -= 0.12 elif user_experience == "intermediate": moderate_traits = { 'stubborn': -0.08, 'independent': -0.05, 'intelligent': 0.10, 'athletic': 0.08, 'versatile': 0.08, 'protective': -0.05 } for trait, adjustment in moderate_traits.items(): if trait in temperament_lower: temperament_adjustments += adjustment else: # advanced advanced_traits = { 'stubborn': 0.05, '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 # 確保最終分數範圍合理 final_score = max(0.15, min(1.0, score + temperament_adjustments)) return final_score def calculate_health_score(self, breed_name: str, health_sensitivity: str) -> 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 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 """ try: if breed_name not in breed_health_info: return 0.5 except ImportError: 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': -0.20, # 髖關節發育不良，影響生活品質 'heart disease': -0.15, # 心臟疾病，需要長期治療 'progressive retinal atrophy': -0.15, # 進行性視網膜萎縮，導致失明 'bloat': -0.18, # 胃扭轉，致命風險 'epilepsy': -0.15, # 癲癇，需要長期藥物控制 'degenerative myelopathy': -0.15, # 脊髓退化，影響行動能力 'von willebrand disease': -0.12 # 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(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(self, breed_name: str, noise_tolerance: str, living_space: str, has_children: bool, children_age: str) -> 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，很多人棄養就是因為叫聲 """ try: if breed_name not in breed_noise_info: return 0.5 except ImportError: 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})[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 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 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(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 living_space == 'apartment': special_adjustments -= 0.15 elif 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))