feat(ai): integrate Dynamic Ensemble Blending & simplify user output

2026-02-27 01:43:17 +08:00
parent ae59fc8278
commit f60f5601d9
4 changed files with 242 additions and 16 deletions
@@ -0,0 +1,123 @@
+import os
+import json
+import logging
+from datetime import datetime, timedelta
+
+def load_history(filepath):
+    if os.path.exists(filepath):
+        try:
+            with open(filepath, 'r', encoding='utf-8') as f:
+                return json.load(f)
+        except:
+            return {}
+    return {}
+
+def save_history(filepath, data):
+    with open(filepath, 'w', encoding='utf-8') as f:
+        json.dump(data, f, ensure_ascii=False, indent=2)
+
+def update_daily_record(city_name, date_str, forecasts, actual_high):
+    """
+    保存/更新某城市某天的各个模型预报与最终实测值
+    forecasts: dict, 例如 {"ECMWF": 28.5, "GFS": 30.0, ...}
+    actual_high: float, 最终实测最高温
+    """
+    project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+    history_file = os.path.join(project_root, 'data', 'daily_records.json')
+    
+    data = load_history(history_file)
+    if city_name not in data:
+        data[city_name] = {}
+        
+    if date_str not in data[city_name]:
+        data[city_name][date_str] = {}
+    
+    data[city_name][date_str]['forecasts'] = forecasts
+    # 只要仍在更新或者已经结束，都记录最新高点
+    data[city_name][date_str]['actual_high'] = actual_high
+    
+    save_history(history_file, data)
+
+def calculate_dynamic_weights(city_name, current_forecasts, lookback_days=7):
+    """
+    计算动态权重融合 (Dynamic Ensemble Blending, DEB)
+    根据过去 N 天各模型的 Mean Absolute Error (MAE) 计算倒数权重
+    返回: blended_high (融合预报值), weights_info (权重展示字符串)
+    """
+    project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+    history_file = os.path.join(project_root, 'data', 'daily_records.json')
+    data = load_history(history_file)
+    
+    if city_name not in data or not data[city_name]:
+        # 没有历史数据，返回简单的平均/中位数
+        valid_vals = [v for v in current_forecasts.values() if v is not None]
+        if not valid_vals: return None, "暂无模型数据"
+        avg = sum(valid_vals) / len(valid_vals)
+        return round(avg, 1), "等权平均(历史数据不足)"
+        
+    # 获取过去 lookback_days 天的有 actual_high 的记录
+    city_data = data[city_name]
+    sorted_dates = sorted(city_data.keys(), reverse=True)
+    
+    # 我们只用真正结清（或者有比较准确最高温）的历史来算误差
+    # 这边简化：凡是有 actual_high 的都算进去
+    errors = {model: [] for model in current_forecasts.keys()}
+    
+    days_used = 0
+    for date_str in sorted_dates:
+        # 跳过今天，今天还没出最终结果
+        if date_str == datetime.now().strftime("%Y-%m-%d"):
+            continue
+            
+        record = city_data[date_str]
+        actual = record.get('actual_high')
+        past_forecasts = record.get('forecasts', {})
+        
+        if actual is None:
+            continue
+            
+        for model in current_forecasts.keys():
+            if model in past_forecasts and past_forecasts[model] is not None:
+                errors[model].append(abs(past_forecasts[model] - actual))
+                
+        days_used += 1
+        if days_used >= lookback_days:
+            break
+            
+    # 如果有效历史天数 < 2 天，还是使用等权
+    if days_used < 2:
+        valid_vals = [v for v in current_forecasts.values() if v is not None]
+        avg = sum(valid_vals) / len(valid_vals)
+        return round(avg, 1), f"等权平均(由于仅{days_used}天历史)"
+        
+    # 计算 MAE
+    maes = {}
+    for model, err_list in errors.items():
+        if err_list:
+            maes[model] = sum(err_list) / len(err_list)
+        else:
+            # 如果某个新模型没有历史数据，给它一个平均误差
+            maes[model] = 2.0 
+            
+    # 计算权重（用 MAE 的倒数，误差越小权重越大；加 0.1 防止除以0）
+    inverse_errors = {m: 1.0 / (mae + 0.1) for m, mae in maes.items() if current_forecasts.get(m) is not None}
+    
+    total_inv = sum(inverse_errors.values())
+    if total_inv == 0:
+        return None, "权重计算异常"
+        
+    weights = {m: inv / total_inv for m, inv in inverse_errors.items()}
+    
+    # 计算加权最高温
+    blended_high = 0.0
+    for m in weights.keys():
+        blended_high += current_forecasts[m] * weights[m]
+        
+    # 格式化权重信息，挑选前权重最高的2-3个模型展示
+    sorted_models = sorted(weights.items(), key=lambda x: x[1], reverse=True)
+    weight_str_parts = []
+    for m, w in sorted_models[:3]:
+         weight_str_parts.append(f"{m}({w*100:.0f}%,MAE:{maes[m]:.1f}°)")
+         
+    return round(blended_high, 1), " | ".join(weight_str_parts)
+