Merge remote-tracking branch 'origin/dev-ltx' into develop

# Conflicts:
#	app/api/api_recommendation.py
#	app/service/design_fast/utils/organize.py

app/api/api_brand_dna_initialize.py

@@ -9,7 +9,6 @@ from apscheduler.schedulers.background import BackgroundScheduler
 from apscheduler.triggers.cron import CronTrigger
 from fastapi import HTTPException, APIRouter
 
-from app.service.recommend.service import load_resources, matrix_data
 import pymysql
 from app.core.config import DB_CONFIG, TABLE_CATEGORIES, RECOMMEND_PATH_PREFIX
 from minio import Minio

app/api/api_import_sys_sketch.py

@@ -0,0 +1,116 @@
+import logging
+import sys
+from typing import Optional
+from fastapi import APIRouter, HTTPException, Query
+from concurrent.futures import ThreadPoolExecutor
+import threading
+
+from app.schemas.response_template import ResponseModel
+from app.service.recommendation_system.import_sys_sketch_to_milvus import main as import_main
+
+logger = logging.getLogger()
+router = APIRouter()
+
+# 使用线程池执行器来运行长时间任务
+executor = ThreadPoolExecutor(max_workers=1)
+# 用于跟踪任务状态
+task_status = {"running": False}
+
+
+def run_import_task(batch_size: int, retry_times: int, limit: Optional[int], offset: int, skip_create_collection: bool):
+    """在后台线程中运行导入任务"""
+    original_argv = None
+    try:
+        task_status["running"] = True
+        # 保存原始 sys.argv
+        original_argv = sys.argv.copy()
+        
+        # 模拟命令行参数
+        sys.argv = [
+            "import_sys_sketch_to_milvus.py",
+            "--batch-size", str(batch_size),
+            "--retry-times", str(retry_times),
+        ]
+        if limit is not None:
+            sys.argv.extend(["--limit", str(limit)])
+        if offset > 0:
+            sys.argv.extend(["--offset", str(offset)])
+        if skip_create_collection:
+            sys.argv.append("--skip-create-collection")
+        
+        import_main()
+        task_status["running"] = False
+        logger.info("导入任务完成")
+    except Exception as e:
+        task_status["running"] = False
+        logger.error(f"导入任务失败: {e}", exc_info=True)
+        raise
+    finally:
+        # 恢复原始 sys.argv
+        if original_argv is not None:
+            sys.argv = original_argv
+
+
+@router.post("/import-sys-sketch", response_model=ResponseModel)
+async def import_sys_sketch(
+    batch_size: int = Query(1000, description="批量处理大小（默认：1000）"),
+    retry_times: int = Query(3, description="失败重试次数（默认：3）"),
+    limit: Optional[int] = Query(None, description="限制处理数量（用于测试，默认：不限制）"),
+    offset: int = Query(0, description="起始偏移量（默认：0）"),
+    skip_create_collection: bool = Query(False, description="跳过创建集合（如果集合已存在）"),
+):
+    """
+    从 t_sys_file 导入系统图向量到 Milvus
+    
+    该接口会异步执行导入任务，任务在后台运行。
+    """
+    try:
+        # 检查是否有任务正在运行
+        if task_status["running"]:
+            raise HTTPException(
+                status_code=409,
+                detail="已有导入任务正在运行，请等待完成后再试"
+            )
+        
+        # 在后台线程中执行任务
+        executor.submit(
+            run_import_task,
+            batch_size,
+            retry_times,
+            limit,
+            offset,
+            skip_create_collection
+        )
+        
+        return ResponseModel(
+            code=200,
+            msg="导入任务已启动，正在后台执行",
+            data={
+                "status": "started",
+                "batch_size": batch_size,
+                "retry_times": retry_times,
+                "limit": limit,
+                "offset": offset,
+                "skip_create_collection": skip_create_collection
+            }
+        )
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"启动导入任务失败: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=f"启动导入任务失败: {str(e)}")
+
+
+@router.get("/import-sys-sketch/status", response_model=ResponseModel)
+async def get_import_status():
+    """
+    获取导入任务状态
+    """
+    return ResponseModel(
+        code=200,
+        msg="OK",
+        data={
+            "running": task_status["running"]
+        }
+    )
+

app/api/api_precompute.py

@@ -0,0 +1,85 @@
+import logging
+from fastapi import APIRouter, HTTPException
+from concurrent.futures import ThreadPoolExecutor
+
+from app.schemas.response_template import ResponseModel
+from app.service.recommendation_system.precompute import run_precompute
+
+logger = logging.getLogger()
+router = APIRouter()
+
+# 使用线程池执行器来运行长时间任务
+executor = ThreadPoolExecutor(max_workers=1)
+# 用于跟踪任务状态
+task_status = {"running": False}
+
+
+def run_precompute_task():
+    """在后台线程中运行预计算任务"""
+    try:
+        task_status["running"] = True
+        logger.info("开始执行预计算任务...")
+        run_precompute()
+        task_status["running"] = False
+        logger.info("预计算任务完成")
+    except Exception as e:
+        task_status["running"] = False
+        logger.error(f"预计算任务失败: {e}", exc_info=True)
+        raise
+
+
+@router.post("/precompute", response_model=ResponseModel)
+async def precompute():
+    """
+    运行预计算任务
+    
+    该接口会异步执行预计算任务，包括：
+    1. 优化数据库表结构
+    2. 历史数据迁移
+    3. 初始用户偏好向量生成
+    
+    任务在后台运行。
+    """
+    try:
+        # 检查是否有任务正在运行
+        if task_status["running"]:
+            raise HTTPException(
+                status_code=409,
+                detail="已有预计算任务正在运行，请等待完成后再试"
+            )
+        
+        # 在后台线程中执行任务
+        executor.submit(run_precompute_task)
+        
+        return ResponseModel(
+            code=200,
+            msg="预计算任务已启动，正在后台执行",
+            data={
+                "status": "started",
+                "tasks": [
+                    "优化数据库表结构",
+                    "历史数据迁移",
+                    "初始用户偏好向量生成"
+                ]
+            }
+        )
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"启动预计算任务失败: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=f"启动预计算任务失败: {str(e)}")
+
+
+@router.get("/precompute/status", response_model=ResponseModel)
+async def get_precompute_status():
+    """
+    获取预计算任务状态
+    """
+    return ResponseModel(
+        code=200,
+        msg="OK",
+        data={
+            "running": task_status["running"]
+        }
+    )
+

app/api/api_recommendation.py

@@ -1,207 +1,175 @@
 import io
 import logging
 import sys
-import time
+from typing import List, Optional
-from typing import List
+from fastapi import HTTPException, APIRouter, Query
-import os
-import json
-import math
-import random
-import numpy as np
 from apscheduler.schedulers.background import BackgroundScheduler
-from apscheduler.triggers.cron import CronTrigger
-from fastapi import HTTPException, APIRouter
 
-from app.service.recommend.service import load_resources, matrix_data
+from app.service.recommendation_system.recommendation_api import get_recommendations as get_new_recommendations
+from app.service.recommendation_system.incremental_listener import start_background_listener
+from app.service.recommendation_system.milvus_client import create_collection
 
 sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
 logger = logging.getLogger()
 router = APIRouter()
 
 
-@router.on_event("startup")
+# ========== 旧版推荐接口（基于 npy 矩阵，已废弃）==========
-async def startup_event():
+# @router.get("/recommend/{user_id}/{category}/{num_recommendations}/{brand_id}/{brand_scale}", response_model=List[str])
-    # 初始加载
+# async def get_recommendations(user_id: int, category: str, brand_id: int, brand_scale: float, num_recommendations: int = 10):
-    load_resources()
+#     """
-
+#     :param user_id: 4
-    # 配置定时任务
+#     :param category: female_skirt
-    scheduler = BackgroundScheduler()
+#     :param num_recommendations: 1
-    scheduler.add_job(
+#     :return:
-        load_resources,
+#             [
-        trigger=CronTrigger(hour=0, minute=30),
+#                 "aida-sys-image/images/female/skirt/903000017.jpg"
-        name="每日资源刷新"
+#             ]
-    )
+#     """
-    scheduler.start()
-    logger.info("定时任务已启动")
-
-
-def softmax(scores):
-    max_score = max(scores)
-    exp_scores = [math.exp(s - max_score) for s in scores]
-    sum_exp = sum(exp_scores)
-    return [s / sum_exp for s in exp_scores]
-
-
-# def get_random_recommendations(category: str, num: int) -> List[str]:
-#     """根据预加载热度向量推荐（冷启动）"""
 #     try:
-#         heat_data = matrix_data.get("heat_data", {})
+#         start_time = time.time()
+#         cache_key = (user_id, category)
+#         # === 新增：用户存在性检查 ===
+#         user_exists_inter = user_id in matrix_data["user_index_interaction"]
+#         user_exists_feat = user_id in matrix_data["user_index_feature"]
 #
-#         if category not in heat_data:
+#         # 任一矩阵不存在用户则返回随机推荐
-#             raise ValueError(f"热度数据缺少类别 {category}，使用随机推荐")
+#         if not (user_exists_inter and user_exists_feat):
+#             logger.info(f"用户 {user_id} 数据不完整，触发随机推荐")
+#             return get_random_recommendations(category, num_recommendations)
 #
-#         heat_dict = heat_data[category]  # {url: score}
+#         # 检查缓存
-#         urls = list(heat_dict.keys())
+#         if cache_key in matrix_data["cached_scores"]:
-#         scores = list(heat_dict.values())
+#             processed_inter, processed_feat = matrix_data["cached_scores"][cache_key]
+#             valid_sketch_idxs_inter = matrix_data["cached_valid_idxs"][cache_key]
+#         else:
+#             # 实时计算逻辑（同原代码）
+#             user_idx_inter = matrix_data["user_index_interaction"].get(user_id)
+#             user_idx_feature = matrix_data["user_index_feature"].get(user_id)
 #
-#         if not urls:
+#             category_iids = matrix_data["category_to_iids"].get(category, [])
-#             raise ValueError("该类别下无热度记录，使用随机推荐")
+#             valid_sketch_idxs_inter = [
+#                 idx for iid, idx in matrix_data["sketch_index_interaction"].items()
+#                 if iid in category_iids
+#             ]
 #
-#         probs = softmax(scores)
+#             # 处理交互分数
-#         sample_size = min(num, len(urls))
+#             raw_inter_scores = []
-#         sampled_urls = random.choices(urls, weights=probs, k=sample_size)
+#             if user_idx_inter is not None and valid_sketch_idxs_inter:
+#                 raw_inter_scores = matrix_data["interaction_matrix"][user_idx_inter, valid_sketch_idxs_inter]
+#             processed_inter = raw_inter_scores * 0.7
 #
-#         return sampled_urls
+#             # 处理特征分数
+#             valid_sketch_idxs_feature = [
+#                 idx for iid, idx in matrix_data["sketch_index_feature"].items()
+#                 if iid in category_iids
+#             ]
+#             raw_feat_scores = []
+#             if user_idx_feature is not None and valid_sketch_idxs_feature:
+#                 raw_feat_scores = matrix_data["feature_matrix"][user_idx_feature, valid_sketch_idxs_feature]
+#                 raw_feat_scores = (raw_feat_scores - np.min(raw_feat_scores)) / (
+#                         np.max(raw_feat_scores) - np.min(raw_feat_scores) + 1e-8)
+#                 processed_feat = raw_feat_scores
+#             else:
+#                 processed_feat = np.array([])
 #
+#             # 更新缓存
+#             matrix_data["cached_scores"][cache_key] = (processed_inter, processed_feat)
+#             matrix_data["cached_valid_idxs"][cache_key] = valid_sketch_idxs_inter
+#
+#         # 合并分数
+#         if brand_id is not None:
+#             brand_idx_feature = matrix_data["brand_index_map"].get(brand_id)
+#
+#             brand_feat_valid = (
+#                     matrix_data["brand_feature_matrix"].size > 0 and  # 矩阵非空
+#                     brand_idx_feature is not None and
+#                     valid_sketch_idxs_feature  # 有可用索引
+#             )
+#
+#             if brand_feat_valid:
+#                 raw_brand_feat_scores = matrix_data["brand_feature_matrix"][
+#                     brand_idx_feature, valid_sketch_idxs_feature
+#                 ]
+#                 raw_brand_feat_scores = (raw_brand_feat_scores - np.min(raw_brand_feat_scores)) / (
+#                         np.max(raw_brand_feat_scores) - np.min(raw_brand_feat_scores) + 1e-8
+#                 )
+#                 processed_brand_feat = raw_brand_feat_scores
+#
+#                 # 如果 processed_feat 是空的，替换为全 0，避免 shape 不一致
+#                 if processed_feat.size == 0:
+#                     processed_feat = np.zeros_like(processed_brand_feat)
+#
+#                 final_scores = processed_inter + 0.3 * (
+#                         (1 - brand_scale) * processed_feat + brand_scale * processed_brand_feat
+#                 )
+#             else:
+#                 # brand 信息不可用
+#                 final_scores = processed_inter + 0.3 * processed_feat if processed_feat.size > 0 else processed_inter
+#         else:
+#             final_scores = processed_inter + 0.3 * processed_feat if processed_feat.size > 0 else processed_inter
+#
+#         valid_sketch_idxs = matrix_data["cached_valid_idxs"][cache_key]
+#
+#         # 概率采样
+#         scores = np.array(final_scores)
+#
+#         # 调整后的概率转换（带温度控制的softmax）
+#         def calibrated_softmax(scores, temperature=1.0):
+#             scores = scores / temperature
+#             scale = scores - max(scores)
+#             exps = np.exp(scale)
+#             return exps / np.sum(exps)
+#
+#         probs = calibrated_softmax(scores, 0.09)
+#
+#         chosen_indices = np.random.choice(
+#             len(valid_sketch_idxs),
+#             size=min(num_recommendations, len(valid_sketch_idxs)),
+#             p=probs,
+#             replace=False
+#         )
+#         recommendations = [matrix_data["iid_to_sketch"][valid_sketch_idxs[idx]] for idx in chosen_indices]
+#
+#         logger.info(f"推荐生成完成，耗时: {time.time() - start_time:.2f}秒")
+#         return recommendations
 #     except Exception as e:
-#         # 回退：完全随机推荐
+#         logger.error(f"推荐失败: {str(e)}", exc_info=True)
-#         all_iids = list(matrix_data["iid_to_sketch"].keys())
+#         raise HTTPException(status_code=500, detail=str(e))
-#         category_iids = matrix_data["category_to_iids"].get(category, all_iids)
-#         sample_size = min(num, len(category_iids))
-#         sampled = np.random.choice(category_iids, size=sample_size, replace=False)
-#         return [matrix_data["iid_to_sketch"][iid] for iid in sampled]
 
-def get_random_recommendations(category: str, num: int) -> List[str]:
+# @router.on_event("startup")
-    """全品类随机推荐"""
+async def startup_event():
-    all_iids = list(matrix_data["iid_to_sketch"].keys())
+    """启动时初始化增量监听任务"""
-    # 优先从当前品类选择
-    category_iids = matrix_data["category_to_iids"].get(category, all_iids)
-    # 确保不超出实际数量
-    sample_size = min(num, len(category_iids))
-    sampled = np.random.choice(category_iids, size=sample_size, replace=False)
-    return [matrix_data["iid_to_sketch"][iid] for iid in sampled]
-
-
-@router.get("/recommend/{user_id}/{category}/{num_recommendations}/{brand_id}/{brand_scale}", response_model=List[str])
-async def get_recommendations(user_id: int, category: str, brand_id: int, brand_scale: float, num_recommendations: int = 10):
-    """
-    :param user_id: 4
-    :param category: female_skirt
-    :param num_recommendations: 1
-    :return:
-            [
-                "aida-sys-image/images/female/skirt/903000017.jpg"
-            ]
-    """
     try:
-        logger.info(f"user_id:{user_id}-----category:{category}-----brand_id:{brand_id}-----brand_scale:{brand_scale}-----num_recommendations:{num_recommendations}")
+        # 确保 Milvus 集合已创建（若已存在则直接返回）
-        start_time = time.time()
+        try:
-        cache_key = (user_id, category)
+            create_collection()
-        # === 新增：用户存在性检查 ===
+        except Exception as exc:
-        user_exists_inter = user_id in matrix_data["user_index_interaction"]
+            logger.error("Milvus 集合创建/检查失败，不影响服务继续启动: %s", exc, exc_info=True)
-        user_exists_feat = user_id in matrix_data["user_index_feature"]
+        
-
+        # 配置定时任务
-        # 任一矩阵不存在用户则返回随机推荐
+        scheduler = BackgroundScheduler()
-        if not (user_exists_inter and user_exists_feat):
+        start_background_listener(scheduler)
-            logger.info(f"用户 {user_id} 数据不完整，触发随机推荐")
+        scheduler.start()
-            return get_random_recommendations(category, num_recommendations)
+        logger.info("增量监听定时任务已启动")
-
-        # 检查缓存
-        if cache_key in matrix_data["cached_scores"]:
-            processed_inter, processed_feat = matrix_data["cached_scores"][cache_key]
-            valid_sketch_idxs_inter = matrix_data["cached_valid_idxs"][cache_key]
-        else:
-            # 实时计算逻辑（同原代码）
-            user_idx_inter = matrix_data["user_index_interaction"].get(user_id)
-            user_idx_feature = matrix_data["user_index_feature"].get(user_id)
-
-            category_iids = matrix_data["category_to_iids"].get(category, [])
-            valid_sketch_idxs_inter = [
-                idx for iid, idx in matrix_data["sketch_index_interaction"].items()
-                if iid in category_iids
-            ]
-
-            # 处理交互分数
-            raw_inter_scores = []
-            if user_idx_inter is not None and valid_sketch_idxs_inter:
-                raw_inter_scores = matrix_data["interaction_matrix"][user_idx_inter, valid_sketch_idxs_inter]
-            processed_inter = raw_inter_scores * 0.7
-
-            # 处理特征分数
-            valid_sketch_idxs_feature = [
-                idx for iid, idx in matrix_data["sketch_index_feature"].items()
-                if iid in category_iids
-            ]
-            raw_feat_scores = []
-            if user_idx_feature is not None and valid_sketch_idxs_feature:
-                raw_feat_scores = matrix_data["feature_matrix"][user_idx_feature, valid_sketch_idxs_feature]
-                raw_feat_scores = (raw_feat_scores - np.min(raw_feat_scores)) / (
-                        np.max(raw_feat_scores) - np.min(raw_feat_scores) + 1e-8)
-                processed_feat = raw_feat_scores
-            else:
-                processed_feat = np.array([])
-
-            # 更新缓存
-            matrix_data["cached_scores"][cache_key] = (processed_inter, processed_feat)
-            matrix_data["cached_valid_idxs"][cache_key] = valid_sketch_idxs_inter
-
-        # 合并分数
-        if brand_id is not None:
-            brand_idx_feature = matrix_data["brand_index_map"].get(brand_id)
-
-            brand_feat_valid = (
-                    matrix_data["brand_feature_matrix"].size > 0 and  # 矩阵非空
-                    brand_idx_feature is not None and
-                    valid_sketch_idxs_feature  # 有可用索引
-            )
-
-            if brand_feat_valid:
-                raw_brand_feat_scores = matrix_data["brand_feature_matrix"][
-                    brand_idx_feature, valid_sketch_idxs_feature
-                ]
-                raw_brand_feat_scores = (raw_brand_feat_scores - np.min(raw_brand_feat_scores)) / (
-                        np.max(raw_brand_feat_scores) - np.min(raw_brand_feat_scores) + 1e-8
-                )
-                processed_brand_feat = raw_brand_feat_scores
-
-                # 如果 processed_feat 是空的，替换为全 0，避免 shape 不一致
-                if processed_feat.size == 0:
-                    processed_feat = np.zeros_like(processed_brand_feat)
-
-                final_scores = processed_inter + 0.3 * (
-                        (1 - brand_scale) * processed_feat + brand_scale * processed_brand_feat
-                )
-            else:
-                # brand 信息不可用
-                final_scores = processed_inter + 0.3 * processed_feat if processed_feat.size > 0 else processed_inter
-        else:
-            final_scores = processed_inter + 0.3 * processed_feat if processed_feat.size > 0 else processed_inter
-
-        valid_sketch_idxs = matrix_data["cached_valid_idxs"][cache_key]
-
-        # 概率采样
-        scores = np.array(final_scores)
-
-        # 调整后的概率转换（带温度控制的softmax）
-        def calibrated_softmax(scores, temperature=1.0):
-            scores = scores / temperature
-            scale = scores - max(scores)
-            exps = np.exp(scale)
-            return exps / np.sum(exps)
-
-        probs = calibrated_softmax(scores, 0.09)
-
-        chosen_indices = np.random.choice(
-            len(valid_sketch_idxs),
-            size=min(num_recommendations, len(valid_sketch_idxs)),
-            p=probs,
-            replace=False
-        )
-        recommendations = [matrix_data["iid_to_sketch"][valid_sketch_idxs[idx]] for idx in chosen_indices]
-
-        logger.info(f"推荐生成完成，耗时: {time.time() - start_time:.2f}秒")
-        return recommendations
-
     except Exception as e:
-        logger.error(f"推荐失败: {str(e)}", exc_info=True)
+        logger.error(f"启动增量监听任务失败: {e}", exc_info=True)
-        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/recommend/{user_id}/{category}", response_model=List[str])
+async def recommend(
+    user_id: int,
+    category: str,
+    style: Optional[str] = Query(
+        None,
+        description="风格样式（可选）：若传入，则在利用分支对同 style 的候选进行加分",
+    ),
+):
+    """新版推荐接口（Milvus + Redis 偏好向量）。"""
+    try:
+        results = get_new_recommendations(user_id, category, style)
+        path = results[0] if results else ""
+        return [path]
+    except Exception as e:
+        logger.error("新版推荐接口失败 [user=%s, category=%s]: %s", user_id, category, e, exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))

app/api/api_route.py

@@ -10,8 +10,10 @@ from app.api import api_design_pre_processing
 from app.api import api_extraction_project_info
 from app.api import api_generate_image
 from app.api import api_image2sketch
+from app.api import api_import_sys_sketch
 from app.api import api_mannequins_edit
 from app.api import api_pose_transform
+from app.api import api_precompute
 from app.api import api_prompt_generation
 from app.api import api_recommendation
 from app.api import api_super_resolution
@@ -36,3 +38,5 @@ router.include_router(api_mannequins_edit.router, tags=['api_mannequins_edit'],
 router.include_router(api_pose_transform.router, tags=['api_pose_transform'], prefix="/api")
 router.include_router(api_clothing_seg.router, tags=['api_clothing_seg'], prefix="/api")
 router.include_router(api_extraction_project_info.router, tags=['api_extraction_project_info'], prefix="/api")
+router.include_router(api_import_sys_sketch.router, tags=['api_import_sys_sketch'], prefix="/api")
+router.include_router(api_precompute.router, tags=['api_precompute'], prefix="/api")

app/core/config.py

@@ -82,9 +82,9 @@ MILVUS_TABLE_SEG = "seg_cache"
 DB_HOST = '18.167.251.121'  # 数据库主机地址
 # DB_PORT = int( 33006)
 DB_PORT = 33008  # 数据库端口
-DB_USERNAME = 'aida_con_python'  # 数据库用户名
+DB_USERNAME = 'aida_con'  # 数据库用户名
 DB_PASSWORD = '123456'  # 数据库密码
-DB_NAME = 'aida'  # 数据库库名
+DB_NAME = 'aida_back'  # 数据库库名
 
 # openai
 os.environ['SERPAPI_API_KEY'] = "a793513017b0718db7966207c31703d280d12435c982f1e67bbcbffa52e7632c"

app/main.py

@@ -11,7 +11,6 @@ from app.api.api_route import router
 from app.core.config import settings
 from app.core.record_api_count import count_api_calls
 from app.schemas.response_template import ResponseModel
-from app.service.recommend.service import load_resources
 from logging_env import LOGGER_CONFIG_DICT
 
 logging.config.dictConfig(LOGGER_CONFIG_DICT)

app/service/recommend/service.py

@@ -1,240 +1,240 @@
-# 预加载资源
+# # 预加载资源
-import logging
+# import logging
-import time
+# import time
-from collections import defaultdict
+# from collections import defaultdict
-import os
+# import os
-import json
+# import json
-import numpy as np
+# import numpy as np
-
+#
-from app.core.config import DB_CONFIG, RECOMMEND_PATH_PREFIX
+# from app.core.config import DB_CONFIG, RECOMMEND_PATH_PREFIX
-
+#
-logger = logging.getLogger()
+# logger = logging.getLogger()
-import pymysql
+# import pymysql
-from concurrent.futures import ThreadPoolExecutor
+# from concurrent.futures import ThreadPoolExecutor
-
+#
-HEAT_VECTOR_FILE = 'heat_vectors_data/heat_vectors.json'  # 可动态加载或配置
+# HEAT_VECTOR_FILE = 'heat_vectors_data/heat_vectors.json'  # 可动态加载或配置
-
+#
-matrix_data = {
+# matrix_data = {
-    "interaction_matrix": None,
+#     "interaction_matrix": None,
-    "feature_matrix": None,
+#     "feature_matrix": None,
-    "user_index_interaction": None,
+#     "user_index_interaction": None,
-    "sketch_index_interaction": None,
+#     "sketch_index_interaction": None,
-    "user_index_feature": None,
+#     "user_index_feature": None,
-    "sketch_index_feature": None,
+#     "sketch_index_feature": None,
-    "iid_to_sketch": None,
+#     "iid_to_sketch": None,
-    "category_to_iids": None,
+#     "category_to_iids": None,
-    "cached_scores": {},
+#     "cached_scores": {},
-    "cached_valid_idxs": {},
+#     "cached_valid_idxs": {},
-    "category_sketch_idxs_inter": None,
+#     "category_sketch_idxs_inter": None,
-    "category_sketch_idxs_feature": None,
+#     "category_sketch_idxs_feature": None,
-    "user_inter_full": dict(),
+#     "user_inter_full": dict(),
-    "user_feat_full": dict(),
+#     "user_feat_full": dict(),
-    "brand_feature_matrix": None,
+#     "brand_feature_matrix": None,
-    "brand_index_map": None,
+#     "brand_index_map": None,
-    "heat_data": {},
+#     "heat_data": {},
-}
+# }
-
+#
-
+#
-def load_resources():
+# def load_resources():
-    """加载所有矩阵和映射关系，并触发预缓存"""
+#     """加载所有矩阵和映射关系，并触发预缓存"""
-    try:
+#     try:
-        start_time = time.time()
+#         start_time = time.time()
-
+#
-        # 清空缓存
+#         # 清空缓存
-        matrix_data["cached_scores"].clear()
+#         matrix_data["cached_scores"].clear()
-        matrix_data["cached_valid_idxs"].clear()
+#         matrix_data["cached_valid_idxs"].clear()
-
+#
-        # 加载数据
+#         # 加载数据
-        sketch_to_iid = np.load(f'{RECOMMEND_PATH_PREFIX}sketch_to_iid.npy', allow_pickle=True).item()
+#         sketch_to_iid = np.load(f'{RECOMMEND_PATH_PREFIX}sketch_to_iid.npy', allow_pickle=True).item()
-        matrix_data["iid_to_sketch"] = {v: k for k, v in sketch_to_iid.items()}
+#         matrix_data["iid_to_sketch"] = {v: k for k, v in sketch_to_iid.items()}
-
+#
-        matrix_data["interaction_matrix"] = np.load(f"{RECOMMEND_PATH_PREFIX}interaction_matrix.npy", allow_pickle=True)
+#         matrix_data["interaction_matrix"] = np.load(f"{RECOMMEND_PATH_PREFIX}interaction_matrix.npy", allow_pickle=True)
-        matrix_data["user_index_interaction"] = np.load(f"{RECOMMEND_PATH_PREFIX}user_index_interaction_matrix.npy", allow_pickle=True).item()
+#         matrix_data["user_index_interaction"] = np.load(f"{RECOMMEND_PATH_PREFIX}user_index_interaction_matrix.npy", allow_pickle=True).item()
-        matrix_data["sketch_index_interaction"] = np.load(f"{RECOMMEND_PATH_PREFIX}sketch_index_interaction_matrix.npy",
+#         matrix_data["sketch_index_interaction"] = np.load(f"{RECOMMEND_PATH_PREFIX}sketch_index_interaction_matrix.npy",
-                                                          allow_pickle=True).item()
+#                                                           allow_pickle=True).item()
-
+#
-        matrix_data["feature_matrix"] = np.load(f"{RECOMMEND_PATH_PREFIX}feature_matrix.npy", allow_pickle=True)
+#         matrix_data["feature_matrix"] = np.load(f"{RECOMMEND_PATH_PREFIX}feature_matrix.npy", allow_pickle=True)
-
+#
-        brand_feature_path = f"{RECOMMEND_PATH_PREFIX}brand_feature_matrix.npy"
+#         brand_feature_path = f"{RECOMMEND_PATH_PREFIX}brand_feature_matrix.npy"
-        if os.path.exists(brand_feature_path):
+#         if os.path.exists(brand_feature_path):
-            matrix_data["brand_feature_matrix"] = np.load(brand_feature_path, allow_pickle=True)
+#             matrix_data["brand_feature_matrix"] = np.load(brand_feature_path, allow_pickle=True)
-        else:
+#         else:
-            logger.warning("brand_feature_matrix 文件不存在，使用空数组")
+#             logger.warning("brand_feature_matrix 文件不存在，使用空数组")
-            matrix_data["brand_feature_matrix"] = np.array([])
+#             matrix_data["brand_feature_matrix"] = np.array([])
-
+#
-        # brand_index_map
+#         # brand_index_map
-        brand_index_path = f"{RECOMMEND_PATH_PREFIX}brand_index_map.npy"
+#         brand_index_path = f"{RECOMMEND_PATH_PREFIX}brand_index_map.npy"
-        if os.path.exists(brand_index_path):
+#         if os.path.exists(brand_index_path):
-            matrix_data["brand_index_map"] = np.load(brand_index_path, allow_pickle=True).item()
+#             matrix_data["brand_index_map"] = np.load(brand_index_path, allow_pickle=True).item()
-        else:
+#         else:
-            logger.warning("brand_index_map 文件不存在，使用空字典")
+#             logger.warning("brand_index_map 文件不存在，使用空字典")
-            matrix_data["brand_index_map"] = {}
+#             matrix_data["brand_index_map"] = {}
-
+#
-        matrix_data["user_index_feature"] = np.load(f"{RECOMMEND_PATH_PREFIX}user_index_feature_matrix.npy", allow_pickle=True).item()
+#         matrix_data["user_index_feature"] = np.load(f"{RECOMMEND_PATH_PREFIX}user_index_feature_matrix.npy", allow_pickle=True).item()
-
+#
-        matrix_data["sketch_index_feature"] = np.load(f"{RECOMMEND_PATH_PREFIX}sketch_index_feature_matrix.npy", allow_pickle=True).item()
+#         matrix_data["sketch_index_feature"] = np.load(f"{RECOMMEND_PATH_PREFIX}sketch_index_feature_matrix.npy", allow_pickle=True).item()
-
+#
-        category_to_iid_map = np.load(f"{RECOMMEND_PATH_PREFIX}iid_to_category_interaction_matrix.npy", allow_pickle=True).item()
+#         category_to_iid_map = np.load(f"{RECOMMEND_PATH_PREFIX}iid_to_category_interaction_matrix.npy", allow_pickle=True).item()
-        matrix_data["category_to_iids"] = defaultdict(list)
+#         matrix_data["category_to_iids"] = defaultdict(list)
-        for iid, cat in category_to_iid_map.items():
+#         for iid, cat in category_to_iid_map.items():
-            matrix_data["category_to_iids"][cat].append(iid)
+#             matrix_data["category_to_iids"][cat].append(iid)
-
+#
-        logger.info(f"资源加载完成，耗时: {time.time() - start_time:.2f}秒")
+#         logger.info(f"资源加载完成，耗时: {time.time() - start_time:.2f}秒")
-
+#
-        # 触发预缓存
+#         # 触发预缓存
-        precache_user_category()
+#         precache_user_category()
-
+#
-        if os.path.exists(HEAT_VECTOR_FILE):
+#         if os.path.exists(HEAT_VECTOR_FILE):
-            with open(HEAT_VECTOR_FILE, 'r', encoding='utf-8') as f:
+#             with open(HEAT_VECTOR_FILE, 'r', encoding='utf-8') as f:
-                heat_json = json.load(f)
+#                 heat_json = json.load(f)
-                matrix_data["heat_data"] = heat_json.get("data", {})
+#                 matrix_data["heat_data"] = heat_json.get("data", {})
-            logger.info(f"热度向量数据加载完成，共加载 {len(matrix_data['heat_data'])} 个类别")
+#             logger.info(f"热度向量数据加载完成，共加载 {len(matrix_data['heat_data'])} 个类别")
-        else:
+#         else:
-            matrix_data["heat_data"] = {}
+#             matrix_data["heat_data"] = {}
-
+#
-    except Exception as e:
+#     except Exception as e:
-        logger.error(f"资源加载失败: {str(e)}")
+#         logger.error(f"资源加载失败: {str(e)}")
-        raise RuntimeError("初始化失败")
+#         raise RuntimeError("初始化失败")
-
+#
-
+#
-def precache_user_category():
+# def precache_user_category():
-    """优化后的用户分类预缓存（添加耗时统计）"""
+#     """优化后的用户分类预缓存（添加耗时统计）"""
-    if not all([
+#     if not all([
-        matrix_data["interaction_matrix"] is not None,
+#         matrix_data["interaction_matrix"] is not None,
-        matrix_data["feature_matrix"] is not None,
+#         matrix_data["feature_matrix"] is not None,
-        matrix_data["user_index_interaction"] is not None
+#         matrix_data["user_index_interaction"] is not None
-    ]):
+#     ]):
-        logger.warning("资源未加载完成，跳过预缓存")
+#         logger.warning("资源未加载完成，跳过预缓存")
-        return
+#         return
-
+#
-    start_time = time.perf_counter()
+#     start_time = time.perf_counter()
-    time_stats = {
+#     time_stats = {
-        "get_all_user_categories": 0,
+#         "get_all_user_categories": 0,
-        "process_user_category": 0,
+#         "process_user_category": 0,
-        "thread_execution": 0,
+#         "thread_execution": 0,
-        "cache_update": 0,
+#         "cache_update": 0,
-        "total": 0,
+#         "total": 0,
-    }
+#     }
-
+#
-    # 统计用户类别获取时间
+#     # 统计用户类别获取时间
-    t1 = time.perf_counter()
+#     t1 = time.perf_counter()
-    user_categories = get_all_user_categories()
+#     user_categories = get_all_user_categories()
-    time_stats["get_all_user_categories"] = time.perf_counter() - t1
+#     time_stats["get_all_user_categories"] = time.perf_counter() - t1
-
+#
-    precached_count = 0
+#     precached_count = 0
-
+#
-    def process_user_category(user_id, categories):
+#     def process_user_category(user_id, categories):
-        """单用户类别缓存计算（统计耗时）"""
+#         """单用户类别缓存计算（统计耗时）"""
-        local_cache = {}
+#         local_cache = {}
-        local_valid_idxs = {}
+#         local_valid_idxs = {}
-        t_start = time.perf_counter()
+#         t_start = time.perf_counter()
-
+#
-        for category in categories:
+#         for category in categories:
-            cache_key = (user_id, category)
+#             cache_key = (user_id, category)
-            if cache_key in matrix_data["cached_scores"]:
+#             if cache_key in matrix_data["cached_scores"]:
-                continue
+#                 continue
-
+#
-            try:
+#             try:
-                user_idx_inter = matrix_data["user_index_interaction"].get(user_id)
+#                 user_idx_inter = matrix_data["user_index_interaction"].get(user_id)
-                user_idx_feature = matrix_data["user_index_feature"].get(user_id)
+#                 user_idx_feature = matrix_data["user_index_feature"].get(user_id)
-
+#
-                # 统计获取类别 IID 耗时
+#                 # 统计获取类别 IID 耗时
-                t_iid = time.perf_counter()
+#                 t_iid = time.perf_counter()
-                category_iids = matrix_data["category_to_iids"].get(category, [])
+#                 category_iids = matrix_data["category_to_iids"].get(category, [])
-                valid_sketch_idxs_inter = [matrix_data["sketch_index_interaction"][iid]
+#                 valid_sketch_idxs_inter = [matrix_data["sketch_index_interaction"][iid]
-                                           for iid in category_iids if iid in matrix_data["sketch_index_interaction"]]
+#                                            for iid in category_iids if iid in matrix_data["sketch_index_interaction"]]
-                valid_sketch_idxs_feature = [matrix_data["sketch_index_feature"][iid]
+#                 valid_sketch_idxs_feature = [matrix_data["sketch_index_feature"][iid]
-                                             for iid in category_iids if iid in matrix_data["sketch_index_feature"]]
+#                                              for iid in category_iids if iid in matrix_data["sketch_index_feature"]]
-                time_stats["process_user_category"] += time.perf_counter() - t_iid
+#                 time_stats["process_user_category"] += time.perf_counter() - t_iid
-
+#
-                # 统计矩阵计算耗时
+#                 # 统计矩阵计算耗时
-                t_matrix = time.perf_counter()
+#                 t_matrix = time.perf_counter()
-                processed_inter = np.zeros(len(valid_sketch_idxs_inter))
+#                 processed_inter = np.zeros(len(valid_sketch_idxs_inter))
-                if user_idx_inter is not None and valid_sketch_idxs_inter:
+#                 if user_idx_inter is not None and valid_sketch_idxs_inter:
-                    raw_inter_scores = matrix_data["interaction_matrix"][user_idx_inter, valid_sketch_idxs_inter]
+#                     raw_inter_scores = matrix_data["interaction_matrix"][user_idx_inter, valid_sketch_idxs_inter]
-                    processed_inter = raw_inter_scores * 0.7
+#                     processed_inter = raw_inter_scores * 0.7
-
+#
-                processed_feat = np.zeros(len(valid_sketch_idxs_feature))
+#                 processed_feat = np.zeros(len(valid_sketch_idxs_feature))
-                if user_idx_feature is not None and valid_sketch_idxs_feature:
+#                 if user_idx_feature is not None and valid_sketch_idxs_feature:
-                    raw_feat_scores = matrix_data["feature_matrix"][user_idx_feature, valid_sketch_idxs_feature]
+#                     raw_feat_scores = matrix_data["feature_matrix"][user_idx_feature, valid_sketch_idxs_feature]
-                    raw_feat_scores = (raw_feat_scores - np.min(raw_feat_scores)) / (
+#                     raw_feat_scores = (raw_feat_scores - np.min(raw_feat_scores)) / (
-                            np.max(raw_feat_scores) - np.min(raw_feat_scores) + 1e-8)
+#                             np.max(raw_feat_scores) - np.min(raw_feat_scores) + 1e-8)
-                    processed_feat = raw_feat_scores * 0.3
+#                     processed_feat = raw_feat_scores * 0.3
-                time_stats["process_user_category"] += time.perf_counter() - t_matrix
+#                 time_stats["process_user_category"] += time.perf_counter() - t_matrix
-
+#
-                if len(processed_inter) == len(processed_feat):
+#                 if len(processed_inter) == len(processed_feat):
-                    local_cache[cache_key] = (processed_inter, processed_feat)
+#                     local_cache[cache_key] = (processed_inter, processed_feat)
-                    local_valid_idxs[cache_key] = valid_sketch_idxs_inter
+#                     local_valid_idxs[cache_key] = valid_sketch_idxs_inter
-
+#
-            except Exception as e:
+#             except Exception as e:
-                logger.error(f"预缓存失败 (user={user_id}, category={category}): {str(e)}")
+#                 logger.error(f"预缓存失败 (user={user_id}, category={category}): {str(e)}")
-
+#
-        return local_cache, local_valid_idxs
+#         return local_cache, local_valid_idxs
-
+#
-    # 统计线程执行时间
+#     # 统计线程执行时间
-    t2 = time.perf_counter()
+#     t2 = time.perf_counter()
-    with ThreadPoolExecutor(max_workers=8) as executor:
+#     with ThreadPoolExecutor(max_workers=8) as executor:
-        futures = {executor.submit(process_user_category, user_id, categories): user_id for user_id, categories in user_categories.items()}
+#         futures = {executor.submit(process_user_category, user_id, categories): user_id for user_id, categories in user_categories.items()}
-        for future in futures:
+#         for future in futures:
-            try:
+#             try:
-                t_cache = time.perf_counter()
+#                 t_cache = time.perf_counter()
-                cache_part, valid_idxs_part = future.result()
+#                 cache_part, valid_idxs_part = future.result()
-                matrix_data["cached_scores"].update(cache_part)
+#                 matrix_data["cached_scores"].update(cache_part)
-                matrix_data["cached_valid_idxs"].update(valid_idxs_part)
+#                 matrix_data["cached_valid_idxs"].update(valid_idxs_part)
-                time_stats["cache_update"] += time.perf_counter() - t_cache
+#                 time_stats["cache_update"] += time.perf_counter() - t_cache
-                precached_count += len(cache_part)
+#                 precached_count += len(cache_part)
-            except Exception as e:
+#             except Exception as e:
-                logger.error(f"线程执行错误: {str(e)}")
+#                 logger.error(f"线程执行错误: {str(e)}")
-    time_stats["thread_execution"] = time.perf_counter() - t2
+#     time_stats["thread_execution"] = time.perf_counter() - t2
-
+#
-    time_stats["total"] = time.perf_counter() - start_time
+#     time_stats["total"] = time.perf_counter() - start_time
-
+#
-    # 输出统计信息
+#     # 输出统计信息
-    logger.info(f"""
+#     logger.info(f"""
-    预缓存完成，共缓存 {precached_count} 组数据，耗时统计如下：
+#     预缓存完成，共缓存 {precached_count} 组数据，耗时统计如下：
-    - 获取用户类别数据: {time_stats["get_all_user_categories"]:.2f}s
+#     - 获取用户类别数据: {time_stats["get_all_user_categories"]:.2f}s
-    - 计算用户类别缓存: {time_stats["process_user_category"]:.2f}s
+#     - 计算用户类别缓存: {time_stats["process_user_category"]:.2f}s
-    - 线程任务执行: {time_stats["thread_execution"]:.2f}s
+#     - 线程任务执行: {time_stats["thread_execution"]:.2f}s
-    - 更新缓存数据: {time_stats["cache_update"]:.2f}s
+#     - 更新缓存数据: {time_stats["cache_update"]:.2f}s
-    - 总耗时: {time_stats["total"]:.2f}s
+#     - 总耗时: {time_stats["total"]:.2f}s
-    """)
+#     """)
-
+#
-
+#
-def get_all_user_categories():
+# def get_all_user_categories():
-    """获取所有用户及其对应的分类"""
+#     """获取所有用户及其对应的分类"""
-    conn = None
+#     conn = None
-    try:
+#     try:
-        conn = pymysql.connect(**DB_CONFIG)
+#         conn = pymysql.connect(**DB_CONFIG)
-        cursor = conn.cursor()
+#         cursor = conn.cursor()
-
+#
-        query = """
+#         query = """
-            SELECT DISTINCT account_id, path 
+#             SELECT DISTINCT account_id, path
-            FROM user_preference_log_prediction
+#             FROM user_preference_log_prediction
-        """
+#         """
-        cursor.execute(query)
+#         cursor.execute(query)
-        results = cursor.fetchall()
+#         results = cursor.fetchall()
-
+#
-        user_categories = defaultdict(set)
+#         user_categories = defaultdict(set)
-        for account_id, path in results:
+#         for account_id, path in results:
-            category = get_category_from_path(path)
+#             category = get_category_from_path(path)
-            user_categories[account_id].add(category)
+#             user_categories[account_id].add(category)
-
+#
-        return dict(user_categories)
+#         return dict(user_categories)
-
+#
-    except Exception as e:
+#     except Exception as e:
-        logger.error(f"数据库查询失败: {str(e)}")
+#         logger.error(f"数据库查询失败: {str(e)}")
-        return {}
+#         return {}
-    finally:
+#     finally:
-        if conn:
+#         if conn:
-            conn.close()
+#             conn.close()
-
+#
-
+#
-def get_category_from_path(path: str) -> str:
+# def get_category_from_path(path: str) -> str:
-    """从路径解析类别"""
+#     """从路径解析类别"""
-    try:
+#     try:
-        parts = path.split('/')
+#         parts = path.split('/')
-        if len(parts) >= 4:
+#         if len(parts) >= 4:
-            return f"{parts[2]}_{parts[3]}"
+#             return f"{parts[2]}_{parts[3]}"
-        return "unknown"
+#         return "unknown"
-    except:
+#     except:
-        return "unknown"
+#         return "unknown"

app/service/recommendation_system/__init__.py

@@ -0,0 +1 @@
+

app/service/recommendation_system/config.py

@@ -0,0 +1,73 @@
+"""
+推荐系统配置
+"""
+import os
+from app.core.config import (
+    DB_CONFIG, DB_HOST, DB_PORT, DB_USERNAME, DB_PASSWORD, DB_NAME,
+    REDIS_HOST, REDIS_PORT, REDIS_DB,
+    MILVUS_URL, MILVUS_TOKEN, MILVUS_ALIAS,
+    MINIO_URL, MINIO_ACCESS, MINIO_SECRET, MINIO_SECURE
+)
+
+# Milvus 集合名称
+MILVUS_COLLECTION_SKETCH_VECTORS = "sketch_vectors_norm"
+
+# Redis key 前缀
+REDIS_KEY_USER_PREF_PREFIX = "user_pref"
+
+# 推荐系统配置参数
+RECOMMENDATION_CONFIG = {
+    # 时间衰减半衰期（用于计算时间衰减权重）
+    # 值越小，最近的行为权重越大
+    "K_half": 20,
+    
+    # 探索与利用的比例 (0.0-1.0)
+    # - 值越大，使用探索分支（随机推荐）的几率越大，结果更随机
+    # - 值越小，使用利用分支（基于用户偏好）的几率越大，结果更精准
+    # - 建议范围: 0.3-0.7，要增加随机性可提高到 0.6-0.8
+    "explore_ratio": 0.5,
+    
+    # 向量检索返回的候选数量
+    # 值越大，候选池越大，但计算成本也越高
+    # 建议范围: 100-1000
+    "topk": 1000,
+    
+    # Style 加分系数（同 style 的候选进行加分）
+    # 值越大，匹配 style 的候选被选中的概率越大
+    # 要降低某个结果的重复率，可以降低此值（如 0.1 或 0.05）
+    "style_bonus": 0.2,
+    
+    # Softmax 抽样的温度参数
+    # - 温度越高（>1.0），概率分布越均匀，结果更随机，重复率更低
+    # - 温度越低（<1.0），高分项概率越大，结果更集中，重复率更高
+    # - 温度=1.0 为标准 Softmax
+    # - 建议范围: 1.0-3.0，要增加随机性可提高到 2.0-3.0
+    "softmax_temperature": 0.07,
+    
+    # 监听间隔（秒）
+    "listen_interval_sec": 30,
+    
+    # 批量处理大小
+    "batch_size": 1000,
+    
+    # Redis 过期时间（秒，30天）
+    "redis_expire_seconds": 2592000,
+    
+    # 向量维度
+    "vector_dim": 2048,
+}
+
+# 数据库表名
+TABLE_USER_PREFERENCE_LOG = "user_preference_log_test"
+TABLE_SYS_FILE = "t_sys_file"
+
+# MySQL 连接配置（用于推荐系统）
+MYSQL_CONFIG = {
+    "host": DB_HOST,
+    "port": DB_PORT,
+    "user": DB_USERNAME,
+    "password": DB_PASSWORD,
+    "database": DB_NAME,
+    "charset": "utf8mb4"
+}
+

app/service/recommendation_system/import_sys_sketch_to_milvus.py

@@ -0,0 +1,331 @@
+"""
+独立脚本：从 t_sys_file 导入系统图向量到 Milvus
+可以单独运行，不依赖整个项目启动
+
+使用方法：
+    python -m app.service.recommendation_system.import_sys_sketch_to_milvus
+    或
+    python app/service/recommendation_system/import_sys_sketch_to_milvus.py
+"""
+import sys
+import os
+import logging
+import argparse
+from pathlib import Path
+
+# 添加项目根目录到 Python 路径
+project_root = Path(__file__).parent.parent.parent.parent
+sys.path.insert(0, str(project_root))
+
+import numpy as np
+import pymysql
+from tqdm import tqdm
+
+from app.service.recommendation_system.config import (
+    MYSQL_CONFIG, TABLE_SYS_FILE,
+    RECOMMENDATION_CONFIG, MILVUS_COLLECTION_SKETCH_VECTORS
+)
+from app.service.recommendation_system.vector_utils import extract_feature_vector, normalize_vector
+from app.service.recommendation_system.milvus_client import create_collection, insert_vectors
+
+# 配置日志
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s',
+    handlers=[
+        logging.StreamHandler(),
+        logging.FileHandler('import_sys_sketch.log', encoding='utf-8')
+    ]
+)
+logger = logging.getLogger(__name__)
+
+
+def get_sys_file_records(conn, limit=None, offset=0):
+    """
+    从 t_sys_file 表获取系统图记录
+    
+    Args:
+        conn: 数据库连接
+        limit: 限制数量（None 表示不限制）
+        offset: 偏移量
+    
+    Returns:
+        记录列表，每个元素为 (id, url, style, level3_type, level2_type, deprecated)
+    """
+    cursor = conn.cursor()
+    
+    query = f"""
+        SELECT id, url, style, level3_type, level2_type, deprecated 
+        FROM {TABLE_SYS_FILE} 
+        WHERE level1_type = 'Images' 
+          AND style IS NOT NULL 
+          AND style != '' 
+          AND deprecated != 1
+        ORDER BY id
+    """
+    
+    if limit:
+        query += f" LIMIT {limit} OFFSET {offset}"
+    
+    cursor.execute(query)
+    records = cursor.fetchall()
+    cursor.close()
+    
+    return records
+
+
+def get_total_count(conn):
+    """获取总记录数"""
+    cursor = conn.cursor()
+    cursor.execute(f"""
+        SELECT COUNT(*) 
+        FROM {TABLE_SYS_FILE} 
+        WHERE level1_type = 'Images' 
+          AND style IS NOT NULL 
+          AND style != '' 
+          AND deprecated != 1
+    """)
+    count = cursor.fetchone()[0]
+    cursor.close()
+    return count
+
+
+def process_and_insert_batch(records, batch_size=1000, retry_times=3):
+    """
+    处理并批量插入向量
+    
+    Args:
+        records: 记录列表
+        batch_size: 批量大小
+        retry_times: 失败重试次数
+    
+    Returns:
+        (成功数量, 失败数量)
+    """
+    success_count = 0
+    failed_count = 0
+    failed_records = []
+    batch_data = []
+    
+    # 使用 tqdm 显示进度
+    with tqdm(total=len(records), desc="处理记录", unit="条") as pbar:
+        for idx, (sys_file_id, url, style, level3_type, level2_type, deprecated) in enumerate(records):
+            try:
+                # 计算 category
+                category = f"{level3_type.lower()}_{level2_type.lower()}"
+                
+                # 提取特征向量
+                feature_vector = extract_feature_vector(url)
+                # 归一化，便于 IP≈cosine 度量
+                feature_vector = normalize_vector(feature_vector)
+                
+                # 检查向量是否有效
+                if np.all(feature_vector == 0):
+                    logger.warning(f"向量提取失败，跳过: {url} (id={sys_file_id})")
+                    failed_count += 1
+                    failed_records.append((sys_file_id, url))
+                    pbar.update(1)
+                    continue
+                
+                # 准备数据
+                data_item = {
+                    "path": url,
+                    "sys_file_id": sys_file_id,
+                    "style": style,
+                    "category": category,
+                    "is_system_sketch": 1,
+                    "deprecated": deprecated if deprecated else 0,
+                    "feature_vector": feature_vector.tolist()
+                }
+                
+                batch_data.append(data_item)
+                
+                # 批量写入
+                if len(batch_data) >= batch_size:
+                    try:
+                        insert_vectors(batch_data)
+                        success_count += len(batch_data)
+                        batch_data = []
+                        logger.info(f"已成功插入 {success_count} 条记录")
+                    except Exception as e:
+                        logger.error(f"批量写入失败: {e}")
+                        failed_count += len(batch_data)
+                        failed_records.extend([(item["sys_file_id"], item["path"]) for item in batch_data])
+                        batch_data = []
+                
+                pbar.update(1)
+                
+            except Exception as e:
+                logger.error(f"处理记录失败 [id={sys_file_id}, url={url}]: {e}")
+                failed_count += 1
+                failed_records.append((sys_file_id, url))
+                pbar.update(1)
+        
+        # 写入剩余数据
+        if batch_data:
+            try:
+                insert_vectors(batch_data)
+                success_count += len(batch_data)
+                logger.info(f"写入剩余 {len(batch_data)} 条记录")
+            except Exception as e:
+                logger.error(f"写入剩余数据失败: {e}")
+                failed_count += len(batch_data)
+                failed_records.extend([(item["sys_file_id"], item["path"]) for item in batch_data])
+    
+    # 重试失败记录
+    if failed_records and retry_times > 0:
+        logger.info(f"开始重试 {len(failed_records)} 条失败记录，最多重试 {retry_times} 次...")
+        
+        for retry in range(retry_times):
+            if not failed_records:
+                break
+            
+            retry_failed = []
+            with tqdm(total=len(failed_records), desc=f"重试第 {retry + 1} 次", unit="条") as pbar:
+                for sys_file_id, url in failed_records:
+                    try:
+                        # 重新查询记录信息
+                        conn = pymysql.connect(**MYSQL_CONFIG)
+                        cursor = conn.cursor()
+                        cursor.execute(f"""
+                            SELECT id, url, style, level3_type, level2_type, deprecated 
+                            FROM {TABLE_SYS_FILE} 
+                            WHERE id = %s
+                        """, (sys_file_id,))
+                        record = cursor.fetchone()
+                        cursor.close()
+                        conn.close()
+                        
+                        if not record:
+                            retry_failed.append((sys_file_id, url))
+                            pbar.update(1)
+                            continue
+                        
+                        sys_file_id, url, style, level3_type, level2_type, deprecated = record
+                        category = f"{level3_type.lower()}_{level2_type.lower()}"
+                        
+                        feature_vector = extract_feature_vector(url)
+                        feature_vector = normalize_vector(feature_vector)
+                        if np.all(feature_vector == 0):
+                            retry_failed.append((sys_file_id, url))
+                            pbar.update(1)
+                            continue
+                        
+                        data_item = {
+                            "path": url,
+                            "sys_file_id": sys_file_id,
+                            "style": style,
+                            "category": category,
+                            "is_system_sketch": 1,
+                            "deprecated": deprecated if deprecated else 0,
+                            "feature_vector": feature_vector.tolist()
+                        }
+                        
+                        insert_vectors([data_item])
+                        success_count += 1
+                        failed_count -= 1
+                        pbar.update(1)
+                        
+                    except Exception as e:
+                        logger.error(f"重试失败 [id={sys_file_id}, url={url}]: {e}")
+                        retry_failed.append((sys_file_id, url))
+                        pbar.update(1)
+            
+            failed_records = retry_failed
+            if failed_records:
+                logger.warning(f"第 {retry + 1} 次重试后仍有 {len(failed_records)} 条记录失败")
+    
+    return success_count, failed_count, failed_records
+
+
+def main():
+    """主函数"""
+    parser = argparse.ArgumentParser(description='从 t_sys_file 导入系统图向量到 Milvus')
+    parser.add_argument('--batch-size', type=int, default=1000, help='批量处理大小（默认：1000）')
+    parser.add_argument('--retry-times', type=int, default=3, help='失败重试次数（默认：3）')
+    parser.add_argument('--limit', type=int, default=None, help='限制处理数量（用于测试，默认：不限制）')
+    parser.add_argument('--offset', type=int, default=0, help='起始偏移量（默认：0）')
+    parser.add_argument('--skip-create-collection', action='store_true', help='跳过创建集合（如果集合已存在）')
+    
+    args = parser.parse_args()
+    
+    logger.info("=" * 60)
+    logger.info("开始从 t_sys_file 导入系统图向量到 Milvus")
+    logger.info("=" * 60)
+    logger.info(f"配置参数：")
+    logger.info(f"  - 批量大小: {args.batch_size}")
+    logger.info(f"  - 重试次数: {args.retry_times}")
+    logger.info(f"  - 限制数量: {args.limit if args.limit else '不限制'}")
+    logger.info(f"  - 起始偏移: {args.offset}")
+    logger.info("=" * 60)
+    
+    # 1. 创建 Milvus 集合
+    if not args.skip_create_collection:
+        logger.info("创建 Milvus 集合...")
+        try:
+            create_collection()
+            logger.info("Milvus 集合创建成功（或已存在）")
+        except Exception as e:
+            logger.error(f"创建 Milvus 集合失败: {e}")
+            return
+    else:
+        logger.info("跳过创建集合")
+    
+    # 2. 连接数据库
+    logger.info("连接数据库...")
+    try:
+        conn = pymysql.connect(**MYSQL_CONFIG)
+        logger.info("数据库连接成功")
+    except Exception as e:
+        logger.error(f"数据库连接失败: {e}")
+        return
+    
+    try:
+        # 3. 获取总记录数
+        total_count = get_total_count(conn)
+        logger.info(f"找到 {total_count} 条系统图记录")
+        
+        if total_count == 0:
+            logger.warning("没有找到系统图数据")
+            return
+        
+        # 4. 获取记录
+        logger.info("获取记录...")
+        records = get_sys_file_records(conn, limit=args.limit, offset=args.offset)
+        logger.info(f"获取到 {len(records)} 条记录")
+        
+        if not records:
+            logger.warning("没有获取到记录")
+            return
+        
+        # 5. 处理并插入
+        logger.info("开始处理记录...")
+        success_count, failed_count, failed_records = process_and_insert_batch(
+            records,
+            batch_size=args.batch_size,
+            retry_times=args.retry_times
+        )
+        
+        # 6. 输出结果
+        logger.info("=" * 60)
+        logger.info("导入完成！")
+        logger.info(f"  - 成功: {success_count} 条")
+        logger.info(f"  - 失败: {failed_count} 条")
+        if failed_records:
+            logger.warning(f"  - 失败记录列表（前10条）:")
+            for sys_file_id, url in failed_records[:10]:
+                logger.warning(f"    ID={sys_file_id}, URL={url}")
+            if len(failed_records) > 10:
+                logger.warning(f"    ... 还有 {len(failed_records) - 10} 条失败记录")
+        logger.info("=" * 60)
+        
+    except Exception as e:
+        logger.error(f"处理过程中发生错误: {e}", exc_info=True)
+    finally:
+        conn.close()
+        logger.info("数据库连接已关闭")
+
+
+if __name__ == "__main__":
+    main()
+

app/service/recommendation_system/incremental_listener.py

@@ -0,0 +1,343 @@
+"""
+增量监听模块
+实时监听 user_preference_log_test 表的新增记录，更新用户偏好向量
+"""
+import logging
+import math
+import pymysql
+import numpy as np
+from typing import List, Dict, Set, Tuple, Optional
+from datetime import datetime
+from collections import defaultdict
+
+from apscheduler.schedulers.background import BackgroundScheduler
+from apscheduler.schedulers.blocking import BlockingScheduler
+
+from app.service.recommendation_system.config import (
+    MYSQL_CONFIG, TABLE_USER_PREFERENCE_LOG, TABLE_SYS_FILE,
+    RECOMMENDATION_CONFIG, REDIS_KEY_USER_PREF_PREFIX
+)
+from app.service.recommendation_system.vector_utils import extract_feature_vector, compute_weighted_average, normalize_vector
+from app.service.recommendation_system.milvus_client import query_vectors_by_paths, insert_vectors
+from app.service.utils.redis_utils import Redis
+import json
+
+logger = logging.getLogger(__name__)
+
+
+class IncrementalListener:
+    """增量监听器"""
+    
+    def __init__(self):
+        self.last_process_time = None
+        self.processed_combinations: Set[Tuple[int, str]] = set()  # 已处理的 (account_id, category) 组合
+        self.listen_interval = RECOMMENDATION_CONFIG["listen_interval_sec"]
+    
+    def get_new_like_records(self) -> List[Tuple]:
+        """
+        获取新增点赞记录
+        
+        Returns:
+            记录列表，每个元素为 (id, account_id, path, category, style, data_time, is_system_sketch, sys_file_id)
+        """
+        conn = None
+        try:
+            conn = pymysql.connect(**MYSQL_CONFIG)
+            cursor = conn.cursor()
+            
+            if self.last_process_time is None:
+                # 第一次运行，查询最近30分钟的数据
+                cursor.execute(f"""
+                    SELECT id, account_id, path, category, style, data_time, is_system_sketch, sys_file_id
+                    FROM {TABLE_USER_PREFERENCE_LOG}
+                    WHERE data_time > DATE_SUB(NOW(), INTERVAL 30 MINUTE)
+                    ORDER BY data_time
+                """)
+            else:
+                # 基于上次处理时间查询
+                cursor.execute(f"""
+                    SELECT id, account_id, path, category, style, data_time, is_system_sketch, sys_file_id
+                    FROM {TABLE_USER_PREFERENCE_LOG}
+                    WHERE data_time > %s
+                    ORDER BY data_time
+                """, (self.last_process_time,))
+            
+            records = cursor.fetchall()
+            return records
+            
+        except Exception as e:
+            logger.error(f"获取新增点赞记录失败: {e}", exc_info=True)
+            return []
+        finally:
+            if conn:
+                conn.close()
+    
+    def process_new_records(self, records: List[Tuple]):
+        """
+        处理新增记录
+        
+        Args:
+            records: 记录列表
+        """
+        if not records:
+            return
+        
+        # 按用户+类别分组
+        user_category_records = defaultdict(list)
+        for record in records:
+            account_id = record[1]
+            category = record[3]
+            if category:  # 只处理有类别的记录
+                user_category_records[(account_id, category)].append(record)
+        
+        # 去重：只处理一次每个 (account_id, category) 组合
+        to_process = []
+        for (account_id, category), recs in user_category_records.items():
+            if (account_id, category) not in self.processed_combinations:
+                to_process.append((account_id, category, recs))
+                self.processed_combinations.add((account_id, category))
+        
+        logger.info(f"需要处理 {len(to_process)} 个用户-类别组合")
+        
+        # 处理每个组合
+        for account_id, category, recs in to_process:
+            try:
+                self.update_user_preference_vector(account_id, category)
+            except Exception as e:
+                logger.error(f"更新用户偏好向量失败 [user={account_id}, category={category}]: {e}", exc_info=True)
+        
+        # 更新最后处理时间
+        if records:
+            self.last_process_time = records[-1][5]  # data_time
+        # 重置去重集合，确保下次周期不会跳过同一用户-类别
+        self.processed_combinations.clear()
+    
+    def update_user_preference_vector(self, account_id: int, category: str):
+        """
+        更新用户偏好向量
+        
+        Args:
+            account_id: 用户ID
+            category: 类别
+        """
+        conn = None
+        try:
+            conn = pymysql.connect(**MYSQL_CONFIG)
+            cursor = conn.cursor()
+            
+            # 1. 获取该用户该类别的所有点赞记录
+            cursor.execute(f"""
+                SELECT path, data_time 
+                FROM {TABLE_USER_PREFERENCE_LOG} 
+                WHERE account_id = %s AND category = %s 
+                ORDER BY data_time DESC
+            """, (account_id, category))
+            
+            like_records = cursor.fetchall()
+            
+            if not like_records:
+                return
+            
+            # 2. 批量查询点赞次数
+            paths = [r[0] for r in like_records]
+            placeholders = ','.join(['%s'] * len(paths))
+            cursor.execute(f"""
+                SELECT path, COUNT(*) as like_count 
+                FROM {TABLE_USER_PREFERENCE_LOG} 
+                WHERE account_id = %s AND category = %s AND path IN ({placeholders})
+                GROUP BY path
+            """, (account_id, category) + tuple(paths))
+            
+            like_counts = {row[0]: row[1] for row in cursor.fetchall()}
+            
+            # 3. 批量获取向量
+            vectors_dict = query_vectors_by_paths(paths)
+            
+            # 处理查询不到的 path（新用户图或异常情况）
+            missing_paths = [p for p in paths if p not in vectors_dict]
+            if missing_paths:
+                logger.info(f"用户 {account_id} 类别 {category} 有 {len(missing_paths)} 个 path 需要实时计算向量")
+                self._compute_and_insert_missing_vectors(missing_paths, conn)
+                # 重新查询
+                vectors_dict = query_vectors_by_paths(paths)
+            
+            # 4. 计算权重并加权平均
+            vectors = []
+            weights = []
+            K_half = RECOMMENDATION_CONFIG["K_half"]
+            
+            for k, (path, data_time) in enumerate(like_records, 1):
+                if path not in vectors_dict:
+                    continue
+                
+                vector_data = vectors_dict[path]
+                feature_vector = np.array(vector_data["feature_vector"])
+                
+                # 时间衰减权重
+                d_k = 0.5 ** (k / K_half)
+                
+                # 点赞次数权重
+                like_count = like_counts.get(path, 1)
+                p_i = 1 + math.log(1 + like_count)
+                
+                # 综合权重
+                w_i = d_k * p_i
+                
+                vectors.append(feature_vector)
+                weights.append(w_i)
+            
+            if not vectors:
+                logger.warning(f"用户 {account_id} 类别 {category} 没有有效向量")
+                return
+            
+            # 5. 计算加权平均并做 L2 归一化，IP≈cosine
+            preference_vector = compute_weighted_average(vectors, weights)
+            preference_vector = normalize_vector(preference_vector)
+            
+            # 6. 写入 Redis
+            key = f"{REDIS_KEY_USER_PREF_PREFIX}:{account_id}:{category}"
+            vector_json = json.dumps(preference_vector.tolist())
+            Redis.write(
+                key=key,
+                value=vector_json,
+                expire=RECOMMENDATION_CONFIG["redis_expire_seconds"]
+            )
+            
+            logger.debug(f"用户偏好向量更新成功 [user={account_id}, category={category}]")
+            
+        except Exception as e:
+            logger.error(f"更新用户偏好向量失败 [user={account_id}, category={category}]: {e}", exc_info=True)
+            raise
+        finally:
+            if conn:
+                conn.close()
+    
+    def _compute_and_insert_missing_vectors(self, paths: List[str], conn: pymysql.connections.Connection):
+        """
+        计算并插入缺失的向量
+        
+        Args:
+            paths: 缺失的 path 列表
+            conn: 数据库连接
+        """
+        cursor = conn.cursor()
+        data_to_insert = []
+        
+        for path in paths:
+            try:
+                # 判断数据来源（查询 t_sys_file 表）
+                cursor.execute(f"""
+                    SELECT id, url, style, level3_type, level2_type, deprecated 
+                    FROM {TABLE_SYS_FILE} 
+                    WHERE url = %s 
+                    LIMIT 1
+                """, (path,))
+                
+                sys_file = cursor.fetchone()
+                
+                # 提取特征向量
+                feature_vector = extract_feature_vector(path)
+                
+                if np.all(feature_vector == 0):
+                    logger.warning(f"向量提取失败，跳过: {path}")
+                    continue
+                
+                if sys_file:
+                    # 系统图
+                    sys_file_id, url, style, level3_type, level2_type, deprecated = sys_file
+                    category = f"{level3_type.lower()}_{level2_type.lower()}"
+                    
+                    data_item = {
+                        "path": path,
+                        "sys_file_id": sys_file_id,
+                        "style": style,
+                        "category": category,
+                        "is_system_sketch": 1,
+                        "deprecated": deprecated if deprecated else 0,
+                        "feature_vector": feature_vector.tolist()
+                    }
+                else:
+                    # 用户图
+                    # 从 user_preference_log_test 获取 category（如果有）
+                    cursor.execute(f"""
+                        SELECT category 
+                        FROM {TABLE_USER_PREFERENCE_LOG} 
+                        WHERE path = %s AND category IS NOT NULL
+                        LIMIT 1
+                    """, (path,))
+                    
+                    category_result = cursor.fetchone()
+                    category = category_result[0] if category_result else None
+                    
+                    data_item = {
+                        "path": path,
+                        "sys_file_id": None,
+                        "style": None,
+                        "category": category,
+                        "is_system_sketch": 0,
+                        "deprecated": 0,
+                        "feature_vector": feature_vector.tolist()
+                    }
+                
+                data_to_insert.append(data_item)
+                
+            except Exception as e:
+                logger.error(f"处理缺失向量失败 [{path}]: {e}")
+        
+        # 批量插入
+        if data_to_insert:
+            try:
+                insert_vectors(data_to_insert)
+                logger.info(f"成功插入 {len(data_to_insert)} 个缺失向量")
+            except Exception as e:
+                logger.error(f"插入缺失向量失败: {e}")
+    
+    def process_once(self):
+        """单次轮询任务，供调度器调用"""
+        try:
+            records = self.get_new_like_records()
+
+            if records:
+                logger.info(f"发现 {len(records)} 条新增记录")
+                self.process_new_records(records)
+            else:
+                logger.debug("没有新增记录")
+        except Exception as e:
+            logger.error(f"监听轮询异常: {e}", exc_info=True)
+
+
+def start_background_listener(scheduler: BackgroundScheduler):
+    """将增量监听任务注册到后台调度器"""
+    listener = IncrementalListener()
+    scheduler.add_job(
+        listener.process_once,
+        "interval",
+        seconds=listener.listen_interval,
+        max_instances=1,
+        coalesce=True,
+        id="recommendation_incremental_listener",
+        replace_existing=True,
+    )
+    logger.info("增量监听任务已注册到调度器")
+
+
+def start_blocking_listener():
+    """以阻塞方式启动调度器（用于独立脚本运行）"""
+    listener = IncrementalListener()
+    scheduler = BlockingScheduler()
+    scheduler.add_job(
+        listener.process_once,
+        "interval",
+        seconds=listener.listen_interval,
+        max_instances=1,
+        coalesce=True,
+        id="recommendation_incremental_listener",
+        replace_existing=True,
+    )
+    logger.info("增量监听调度器已启动（BlockingScheduler）")
+    scheduler.start()
+
+
+if __name__ == "__main__":
+    start_blocking_listener()
+

app/service/recommendation_system/milvus_client.py

@@ -0,0 +1,295 @@
+"""
+Milvus 客户端封装
+"""
+import logging
+from typing import List, Dict, Optional, Any
+import numpy as np
+from pymilvus import MilvusClient, FieldSchema, CollectionSchema, DataType, connections, Collection
+
+from app.core.config import MILVUS_URL, MILVUS_TOKEN, MILVUS_ALIAS
+from app.service.recommendation_system.config import MILVUS_COLLECTION_SKETCH_VECTORS, RECOMMENDATION_CONFIG
+
+logger = logging.getLogger(__name__)
+
+# Milvus 客户端（单例）
+_milvus_client = None
+
+
+def get_milvus_client() -> MilvusClient:
+    """获取 Milvus 客户端（单例模式）"""
+    global _milvus_client
+    if _milvus_client is None:
+        try:
+            _milvus_client = MilvusClient(
+                uri=MILVUS_URL,
+                token=MILVUS_TOKEN,
+                db_name=MILVUS_ALIAS
+            )
+            logger.info("Milvus 客户端连接成功")
+        except Exception as e:
+            logger.error(f"Milvus 客户端连接失败: {e}")
+            raise
+    return _milvus_client
+
+
+def create_collection():
+    """
+    创建 Milvus 集合 sketch_vectors
+    
+    集合结构：
+    - path (PK, varchar(512)) - 主键，MinIO 逻辑 URL
+    - sys_file_id (int64, 可为NULL) - 系统文件ID
+    - style (varchar(50), 可为NULL) - 风格样式
+    - category (varchar(100), 可为NULL) - 类别
+    - is_system_sketch (int8, 默认 1) - 标记字段：1-系统图，0-用户图
+    - deprecated (int8, 默认 0) - 是否废弃
+    - feature_vector (FloatVector(2048)) - 2048维特征向量
+    """
+    client = get_milvus_client()
+    
+    # 检查集合是否已存在
+    collections = client.list_collections()
+    if MILVUS_COLLECTION_SKETCH_VECTORS in collections:
+        logger.info(f"集合 {MILVUS_COLLECTION_SKETCH_VECTORS} 已存在")
+        return
+    
+    try:
+        # 解析 Milvus URL
+        # 处理 http://host.docker.internal:19530 格式
+        url_clean = MILVUS_URL.replace("http://", "").replace("https://", "")
+        if ":" in url_clean:
+            host, port_str = url_clean.split(":", 1)
+            port = int(port_str)
+        else:
+            host = url_clean
+            port = 19530
+        
+        # 使用传统 API 创建集合（更可靠）
+        # 连接到 Milvus（如果未连接）
+        try:
+            connections.connect(
+                alias=MILVUS_ALIAS,
+                host=host,
+                port=port,
+                token=MILVUS_TOKEN if MILVUS_TOKEN else None
+            )
+            logger.info(f"已连接到 Milvus: {host}:{port}")
+        except Exception as conn_e:
+            # 如果连接已存在，忽略错误
+            if "already exists" in str(conn_e).lower() or "Connection already exists" in str(conn_e):
+                logger.info("Milvus 连接已存在")
+            else:
+                logger.warning(f"连接 Milvus 时出现警告: {conn_e}")
+        
+        # 定义字段
+        fields = [
+            FieldSchema(name="path", dtype=DataType.VARCHAR, is_primary=True, max_length=512),
+            FieldSchema(name="sys_file_id", dtype=DataType.INT64),
+            FieldSchema(name="style", dtype=DataType.VARCHAR, max_length=50),
+            FieldSchema(name="category", dtype=DataType.VARCHAR, max_length=50),
+            FieldSchema(name="is_system_sketch", dtype=DataType.INT8),
+            FieldSchema(name="deprecated", dtype=DataType.INT8),
+            FieldSchema(
+                name="feature_vector",
+                dtype=DataType.FLOAT_VECTOR,
+                dim=RECOMMENDATION_CONFIG["vector_dim"]
+            )
+        ]
+        
+        # 创建 schema
+        schema = CollectionSchema(
+            fields=fields,
+            description="Sketch vectors collection for recommendation system"
+        )
+        
+        # 创建集合
+        collection = Collection(
+            name=MILVUS_COLLECTION_SKETCH_VECTORS,
+            schema=schema,
+            using=MILVUS_ALIAS
+        )
+        
+        # 创建索引
+        # 注意：使用 IP（内积）作为度量类型，与搜索时保持一致
+        # 如果向量已归一化，IP 等价于 COSINE
+        index_params = {
+            "metric_type": "IP",  # 内积（Inner Product）
+            "index_type": "IVF_FLAT",
+            "params": {"nlist": 1024}
+        }
+        
+        collection.create_index(
+            field_name="feature_vector",
+            index_params=index_params
+        )
+        
+        logger.info(f"集合 {MILVUS_COLLECTION_SKETCH_VECTORS} 创建成功")
+        
+    except Exception as e:
+        logger.error(f"创建集合失败: {e}", exc_info=True)
+        raise
+
+
+def insert_vectors(data: List[Dict[str, Any]]):
+    """
+    批量插入向量到 Milvus
+    
+    Args:
+        data: 数据列表，每个元素包含：
+            - path: str
+            - sys_file_id: int (可选)
+            - style: str (可选)
+            - category: str (可选)
+            - is_system_sketch: int (默认 1)
+            - deprecated: int (默认 0)
+            - feature_vector: List[float] (2048维)
+    """
+    if not data:
+        return
+    
+    client = get_milvus_client()
+    
+    try:
+        client.insert(
+            collection_name=MILVUS_COLLECTION_SKETCH_VECTORS,
+            data=data
+        )
+        logger.info(f"成功插入 {len(data)} 条向量数据")
+    except Exception as e:
+        logger.error(f"插入向量失败: {e}", exc_info=True)
+        raise
+
+
+def query_vectors_by_paths(paths: List[str]) -> Dict[str, Dict]:
+    """
+    根据 path 列表批量查询向量
+    
+    Args:
+        paths: path 列表
+    
+    Returns:
+        {path: {feature_vector: [...], ...}} 字典
+    """
+    if not paths:
+        return {}
+    
+    client = get_milvus_client()
+    
+    try:
+        # 构建查询表达式
+        # 使用 filter 参数而不是 expr（根据 pymilvus MilvusClient API）
+        # 对于字符串列表，使用单引号包裹每个值
+        path_list = ", ".join([f"'{p}'" for p in paths])
+        filter_expr = f"path in [{path_list}]"
+        
+        results = client.query(
+            collection_name=MILVUS_COLLECTION_SKETCH_VECTORS,
+            filter=filter_expr,
+            output_fields=["path", "feature_vector", "style", "category", "sys_file_id", "is_system_sketch", "deprecated"]
+        )
+        
+        # 转换为字典
+        result_dict = {}
+        for r in results:
+            result_dict[r["path"]] = r
+        
+        return result_dict
+    except Exception as e:
+        logger.error(f"查询向量失败: {e}", exc_info=True)
+        return {}
+
+
+def search_similar_vectors(
+    query_vector: np.ndarray,
+    category: str,
+    topk: int = 500,
+    style: Optional[str] = None
+) -> List[Dict]:
+    """
+    向量相似度检索
+    
+    Args:
+        query_vector: 查询向量（2048维）
+        category: 类别过滤
+        topk: 返回数量
+        style: 风格过滤（可选）
+    
+    Returns:
+        检索结果列表，每个元素包含 path, score, style, category 等字段
+    """
+    client = get_milvus_client()
+    
+    try:
+        # 构建过滤表达式
+        # 使用 filter 参数而不是 expr（根据 pymilvus MilvusClient API）
+        filter_expr = f"category == '{category}' && deprecated == 0"
+        if style:
+            filter_expr += f" && style == '{style}'"
+        
+        # 搜索
+        results = client.search(
+            collection_name=MILVUS_COLLECTION_SKETCH_VECTORS,
+            data=[query_vector.tolist()],
+            anns_field="feature_vector",
+            search_params={"metric_type": "IP", "params": {"nprobe": 10}},
+            limit=topk,
+            filter=filter_expr,
+            output_fields=["path", "style", "category", "sys_file_id"]
+        )
+        
+        # 格式化结果
+        formatted_results = []
+        if results and len(results) > 0:
+            for hit in results[0]:
+                formatted_results.append({
+                    "path": hit.get("entity", {}).get("path", ""),
+                    "score": hit.get("distance", 0.0),
+                    "style": hit.get("entity", {}).get("style", ""),
+                    "category": hit.get("entity", {}).get("category", ""),
+                    "sys_file_id": hit.get("entity", {}).get("sys_file_id")
+                })
+        
+        return formatted_results
+    except Exception as e:
+        logger.error(f"向量检索失败: {e}", exc_info=True)
+        return []
+
+
+def query_random_candidates(category: str, style: Optional[str] = None, limit: int = 10) -> List[Dict]:
+    """
+    随机查询候选（用于探索分支）
+    
+    Args:
+        category: 类别
+        style: 风格（可选）
+        limit: 返回数量
+    
+    Returns:
+        候选列表
+    """
+    client = get_milvus_client()
+    
+    try:
+        # 构建过滤表达式
+        filter_expr = f"category == '{category}' && deprecated == 0"
+        if style:
+            filter_expr += f" && style == '{style}'"
+        
+        # 查询所有符合条件的记录
+        results = client.query(
+            collection_name=MILVUS_COLLECTION_SKETCH_VECTORS,
+            filter=filter_expr,
+            output_fields=["path", "style", "category"],
+            limit=10000  # 先查询大量数据，然后随机选择
+        )
+        
+        # 随机选择
+        if len(results) > limit:
+            import random
+            results = random.sample(results, limit)
+        
+        return results
+    except Exception as e:
+        logger.error(f"随机查询候选失败: {e}", exc_info=True)
+        return []
+

app/service/recommendation_system/precompute.py

@@ -0,0 +1,556 @@
+"""
+预计算模块
+包含：数据库表结构优化、Milvus集合创建、系统图向量预计算、初始用户偏好向量生成
+"""
+import logging
+import math
+import pymysql
+import numpy as np
+from typing import List, Dict, Tuple, Optional
+from collections import defaultdict
+
+from app.service.recommendation_system.config import (
+    MYSQL_CONFIG, TABLE_USER_PREFERENCE_LOG, TABLE_SYS_FILE,
+    RECOMMENDATION_CONFIG, REDIS_KEY_USER_PREF_PREFIX
+)
+from app.service.recommendation_system.vector_utils import extract_feature_vector, normalize_vector, compute_weighted_average
+from app.service.recommendation_system.milvus_client import (
+    create_collection, insert_vectors, query_vectors_by_paths
+)
+from app.service.utils.redis_utils import Redis
+import json
+
+logger = logging.getLogger(__name__)
+
+
+def optimize_database_table():
+    """
+    优化 user_preference_log_test 表结构
+    添加冗余字段和索引
+    """
+    conn = None
+    try:
+        conn = pymysql.connect(**MYSQL_CONFIG)
+        cursor = conn.cursor()
+        
+        # 1. 添加冗余字段
+        logger.info("添加冗余字段...")
+        alter_sqls = [
+            f"ALTER TABLE {TABLE_USER_PREFERENCE_LOG} ADD COLUMN category VARCHAR(100) COMMENT '类别：lower(level3_type + \"_\" + level2_type)'",
+            f"ALTER TABLE {TABLE_USER_PREFERENCE_LOG} ADD COLUMN style VARCHAR(50) COMMENT '风格样式'",
+            f"ALTER TABLE {TABLE_USER_PREFERENCE_LOG} ADD COLUMN is_system_sketch TINYINT(1) DEFAULT 1 COMMENT '是否为系统图（1-是，0-用户图）'",
+            f"ALTER TABLE {TABLE_USER_PREFERENCE_LOG} ADD COLUMN sys_file_id BIGINT NULL COMMENT '系统文件ID'",
+        ]
+        
+        for sql in alter_sqls:
+            try:
+                cursor.execute(sql)
+                logger.info(f"执行成功: {sql[:50]}...")
+            except Exception as e:
+                if "Duplicate column name" in str(e):
+                    logger.info(f"字段已存在，跳过: {sql[:50]}...")
+                else:
+                    logger.warning(f"执行失败: {sql[:50]}... 错误: {e}")
+        
+        # 2. 创建索引（MySQL 不支持 IF NOT EXISTS，需要先检查）
+        logger.info("创建索引...")
+        index_definitions = [
+            ("idx_account_category_time", ["account_id", "category", "data_time"]),
+            ("idx_account_path", ["account_id", "path"]),
+        ]
+        
+        for index_name, columns in index_definitions:
+            try:
+                # 检查索引是否已存在
+                cursor.execute(f"""
+                    SELECT COUNT(*) 
+                    FROM information_schema.statistics 
+                    WHERE table_schema = DATABASE() 
+                      AND table_name = '{TABLE_USER_PREFERENCE_LOG}' 
+                      AND index_name = '{index_name}'
+                """)
+                exists = cursor.fetchone()[0] > 0
+                
+                if exists:
+                    logger.info(f"索引已存在，跳过: {index_name}")
+                else:
+                    # 创建索引
+                    columns_str = ', '.join(columns)
+                    create_sql = f"CREATE INDEX {index_name} ON {TABLE_USER_PREFERENCE_LOG}({columns_str})"
+                    cursor.execute(create_sql)
+                    logger.info(f"索引创建成功: {index_name}")
+            except Exception as e:
+                logger.warning(f"索引创建失败: {index_name} 错误: {e}")
+        
+        conn.commit()
+        logger.info("数据库表结构优化完成")
+        
+    except Exception as e:
+        logger.error(f"数据库表结构优化失败: {e}", exc_info=True)
+        if conn:
+            conn.rollback()
+    finally:
+        if conn:
+            conn.close()
+
+
+def migrate_historical_data(batch_size: int = 1000):
+    """
+    历史数据迁移：批量更新冗余字段
+    
+    Args:
+        batch_size: 每批处理数量
+    """
+    conn = None
+    try:
+        conn = pymysql.connect(**MYSQL_CONFIG)
+        cursor = conn.cursor()
+        
+        # 查询需要更新的记录数
+        cursor.execute(f"""
+            SELECT COUNT(*) 
+            FROM {TABLE_USER_PREFERENCE_LOG} u
+            WHERE u.category IS NULL
+        """)
+        total_count = cursor.fetchone()[0]
+        logger.info(f"需要迁移的记录数: {total_count}")
+        
+        if total_count == 0:
+            logger.info("无需迁移数据")
+            return
+        
+        # 分批处理
+        offset = 0
+        processed = 0
+        
+        while offset < total_count:
+            # 查询一批记录
+            cursor.execute(f"""
+                SELECT u.id, u.path
+                FROM {TABLE_USER_PREFERENCE_LOG} u
+                WHERE u.category IS NULL
+                LIMIT {batch_size} OFFSET {offset}
+            """)
+            records = cursor.fetchall()
+            
+            if not records:
+                break
+            
+            # 批量更新
+            for record_id, path in records:
+                # 查询 t_sys_file 表
+                cursor.execute(f"""
+                    SELECT id, url, style, level3_type, level2_type, deprecated 
+                    FROM {TABLE_SYS_FILE} 
+                    WHERE url = %s 
+                    LIMIT 1
+                """, (path,))
+                
+                sys_file = cursor.fetchone()
+                
+                if sys_file:
+                    # 系统图
+                    sys_file_id, url, style, level3_type, level2_type, deprecated = sys_file
+                    category = f"{level3_type.lower()}_{level2_type.lower()}"
+                    
+                    cursor.execute(f"""
+                        UPDATE {TABLE_USER_PREFERENCE_LOG}
+                        SET category = %s,
+                            style = %s,
+                            is_system_sketch = 1,
+                            sys_file_id = %s
+                        WHERE id = %s
+                    """, (category, style, sys_file_id, record_id))
+                else:
+                    # 用户图
+                    cursor.execute(f"""
+                        UPDATE {TABLE_USER_PREFERENCE_LOG}
+                        SET is_system_sketch = 0,
+                            category = NULL,
+                            style = NULL,
+                            sys_file_id = NULL
+                        WHERE id = %s
+                    """, (record_id,))
+            
+            conn.commit()
+            processed += len(records)
+            offset += batch_size
+            logger.info(f"已迁移 {processed}/{total_count} 条记录")
+        
+        logger.info("历史数据迁移完成")
+        
+    except Exception as e:
+        logger.error(f"历史数据迁移失败: {e}", exc_info=True)
+        if conn:
+            conn.rollback()
+    finally:
+        if conn:
+            conn.close()
+
+
+def precompute_system_sketch_vectors(batch_size: int = 1000, retry_times: int = 3):
+    """
+    系统图向量预计算与导入
+    
+    Args:
+        batch_size: 每批处理数量
+        retry_times: 失败重试次数
+    """
+    conn = None
+    try:
+        conn = pymysql.connect(**MYSQL_CONFIG)
+        cursor = conn.cursor()
+        
+        # 1. 数据筛选
+        logger.info("查询系统图数据...")
+        cursor.execute(f"""
+            SELECT id, url, style, level3_type, level2_type, deprecated 
+            FROM {TABLE_SYS_FILE} 
+            WHERE level1_type = 'Images' 
+              AND style IS NOT NULL 
+              AND style != '' 
+              AND deprecated != 1
+        """)
+        records = cursor.fetchall()
+        logger.info(f"找到 {len(records)} 条系统图记录")
+        
+        if not records:
+            logger.warning("没有找到系统图数据")
+            return
+        
+        # 2. 批量处理
+        failed_records = []
+        batch_data = []
+        
+        for idx, (sys_file_id, url, style, level3_type, level2_type, deprecated) in enumerate(records, 1):
+            try:
+                # 计算 category
+                category = f"{level3_type.lower()}_{level2_type.lower()}"
+                
+                # 提取特征向量
+                feature_vector = extract_feature_vector(url)
+                
+                # 检查向量是否有效
+                if np.all(feature_vector == 0):
+                    logger.warning(f"向量提取失败，跳过: {url}")
+                    failed_records.append((sys_file_id, url))
+                    continue
+                
+                # 准备数据
+                data_item = {
+                    "path": url,
+                    "sys_file_id": sys_file_id,
+                    "style": style,
+                    "category": category,
+                    "is_system_sketch": 1,
+                    "deprecated": deprecated if deprecated else 0,
+                    "feature_vector": feature_vector.tolist()
+                }
+                
+                batch_data.append(data_item)
+                
+                # 批量写入
+                if len(batch_data) >= batch_size:
+                    try:
+                        insert_vectors(batch_data)
+                        batch_data = []
+                        logger.info(f"已处理 {idx}/{len(records)} 条记录")
+                    except Exception as e:
+                        logger.error(f"批量写入失败: {e}")
+                        failed_records.extend([(item["sys_file_id"], item["path"]) for item in batch_data])
+                        batch_data = []
+                
+            except Exception as e:
+                logger.error(f"处理记录失败 [{url}]: {e}")
+                failed_records.append((sys_file_id, url))
+        
+        # 写入剩余数据
+        if batch_data:
+            try:
+                insert_vectors(batch_data)
+            except Exception as e:
+                logger.error(f"写入剩余数据失败: {e}")
+                failed_records.extend([(item["sys_file_id"], item["path"]) for item in batch_data])
+        
+        # 3. 重试失败记录
+        if failed_records and retry_times > 0:
+            logger.info(f"重试 {len(failed_records)} 条失败记录...")
+            for retry in range(retry_times):
+                retry_failed = []
+                for sys_file_id, url in failed_records:
+                    try:
+                        category = f"{level3_type.lower()}_{level2_type.lower()}"
+                        feature_vector = extract_feature_vector(url)
+                        if not np.all(feature_vector == 0):
+                            data_item = {
+                                "path": url,
+                                "sys_file_id": sys_file_id,
+                                "style": style,
+                                "category": category,
+                                "is_system_sketch": 1,
+                                "deprecated": 0,
+                                "feature_vector": feature_vector.tolist()
+                            }
+                            insert_vectors([data_item])
+                        else:
+                            retry_failed.append((sys_file_id, url))
+                    except Exception as e:
+                        logger.error(f"重试失败 [{url}]: {e}")
+                        retry_failed.append((sys_file_id, url))
+                
+                failed_records = retry_failed
+                if not failed_records:
+                    break
+        
+        if failed_records:
+            logger.warning(f"仍有 {len(failed_records)} 条记录处理失败")
+        
+        logger.info("系统图向量预计算完成")
+        
+    except Exception as e:
+        logger.error(f"系统图向量预计算失败: {e}", exc_info=True)
+    finally:
+        if conn:
+            conn.close()
+
+
+def compute_user_preference_vector(
+    account_id: int,
+    category: str,
+    conn: Optional[pymysql.connections.Connection] = None
+    # max_date: Optional[datetime] = None
+) -> Optional[np.ndarray]:
+    """
+    计算用户偏好向量
+    
+    Args:
+        account_id: 用户ID
+        category: 类别
+        conn: 数据库连接（可选）
+        max_date: 最大日期（可选，用于评估时只使用训练集数据）
+    
+    Returns:
+        用户偏好向量（2048维），失败返回 None
+    """
+    from datetime import datetime
+    
+    should_close = False
+    if conn is None:
+        conn = pymysql.connect(**MYSQL_CONFIG)
+        should_close = True
+    
+    try:
+        cursor = conn.cursor()
+        
+        # 1. 获取点赞记录（如果指定了max_date，只查询该日期之前的数据）
+        if max_date:
+            cursor.execute(f"""
+                SELECT path, data_time 
+                FROM {TABLE_USER_PREFERENCE_LOG} 
+                WHERE account_id = %s AND category = %s AND style is not null
+                  AND data_time < %s
+                ORDER BY data_time DESC
+            """, (account_id, category, max_date))
+        else:
+            cursor.execute(f"""
+                SELECT path, data_time 
+                FROM {TABLE_USER_PREFERENCE_LOG} 
+                WHERE account_id = %s AND category = %s  AND style is not null
+                ORDER BY data_time DESC
+            """, (account_id, category))
+        
+        like_records = cursor.fetchall()
+        
+        if not like_records:
+            return None
+        
+        # 2. 批量查询点赞次数（如果指定了max_date，只统计该日期之前的点赞）
+        paths = [r[0] for r in like_records]
+        if not paths:
+            return None
+            
+        placeholders = ','.join(['%s'] * len(paths))
+        if max_date:
+            cursor.execute(f"""
+                SELECT path, COUNT(*) as like_count 
+                FROM {TABLE_USER_PREFERENCE_LOG} 
+                WHERE account_id = %s AND category = %s AND path IN ({placeholders})
+                  AND data_time < %s
+                GROUP BY path
+            """, (account_id, category) + tuple(paths) + (max_date,))
+        else:
+            cursor.execute(f"""
+                SELECT path, COUNT(*) as like_count 
+                FROM {TABLE_USER_PREFERENCE_LOG} 
+                WHERE account_id = %s AND category = %s AND path IN ({placeholders})
+                GROUP BY path
+            """, (account_id, category) + tuple(paths))
+        
+        like_counts = {row[0]: row[1] for row in cursor.fetchall()}
+        
+        # 3. 批量获取向量
+        vectors_dict = query_vectors_by_paths(paths)
+        
+        # 处理查询不到的 path（用户图或异常情况）
+        missing_paths = [p for p in paths if p not in vectors_dict]
+        if missing_paths:
+            logger.info(f"用户 {account_id} 类别 {category} 有 {len(missing_paths)} 个 path 需要实时计算向量")
+            # 目前未有非系统图向量，跳过
+            # 这里可以实时计算并写入 Milvus，但为了简化，先跳过
+            # 实际实现中应该调用 vector_utils.extract_feature_vector 并写入 Milvus
+        
+        # 4. 计算权重并加权平均
+        vectors = []
+        weights = []
+        K_half = RECOMMENDATION_CONFIG["K_half"]
+        
+        for k, (path, data_time) in enumerate(like_records, 1):
+            if path not in vectors_dict:
+                continue
+            
+            vector_data = vectors_dict[path]
+            feature_vector = np.array(vector_data["feature_vector"])
+            
+            # 时间衰减权重
+            d_k = 0.5 ** (k / K_half)
+            
+            # 点赞次数权重
+            like_count = like_counts.get(path, 1)
+            p_i = 1 + math.log(1 + like_count)
+            
+            # 综合权重
+            # w_i = d_k * p_i
+            w_i = p_i
+
+            vectors.append(feature_vector)
+            weights.append(w_i)
+        
+        if not vectors:
+            return None
+        
+        # 5. 计算加权平均并做 L2 归一化，IP≈cosine
+        preference_vector = compute_weighted_average(vectors, weights)
+        preference_vector = normalize_vector(preference_vector)
+        
+        return preference_vector
+        
+    except Exception as e:
+        logger.error(f"计算用户偏好向量失败 [user={account_id}, category={category}]: {e}", exc_info=True)
+        return None
+    finally:
+        if should_close and conn:
+            conn.close()
+
+
+def generate_initial_user_preference_vectors(batch_size: int = 100):
+    """
+    初始用户偏好向量生成
+    
+    Args:
+        batch_size: 每批处理用户数
+    """
+    conn = None
+    try:
+        conn = pymysql.connect(**MYSQL_CONFIG)
+        cursor = conn.cursor()
+        
+        # 1. 扫描历史数据
+        logger.info("扫描用户和类别组合...")
+        cursor.execute(f"""
+            SELECT DISTINCT account_id, category 
+            FROM {TABLE_USER_PREFERENCE_LOG} 
+            WHERE category IS NOT NULL
+            AND style IS NOT NULL
+        """)
+        
+        user_categories = cursor.fetchall()
+        logger.info(f"找到 {len(user_categories)} 个用户-类别组合")
+        
+        if not user_categories:
+            logger.warning("没有找到用户-类别组合")
+            return
+        
+        # 2. 批量处理
+        processed = 0
+        failed = 0
+        
+        for account_id, category in user_categories:
+            try:
+                # 计算偏好向量
+                preference_vector = compute_user_preference_vector(account_id, category, conn)
+                
+                if preference_vector is not None:
+                    # 写入 Redis
+                    key = f"{REDIS_KEY_USER_PREF_PREFIX}:{account_id}:{category}"
+                    # 序列化向量（使用 JSON）
+                    vector_json = json.dumps(preference_vector.tolist())
+                    Redis.write(
+                        key=key,
+                        value=vector_json,
+                        expire=RECOMMENDATION_CONFIG["redis_expire_seconds"]
+                    )
+                    processed += 1
+                else:
+                    failed += 1
+                
+                if (processed + failed) % batch_size == 0:
+                    logger.info(f"已处理 {processed + failed}/{len(user_categories)} 个组合，成功: {processed}, 失败: {failed}")
+                    
+            except Exception as e:
+                logger.error(f"处理失败 [user={account_id}, category={category}]: {e}")
+                failed += 1
+        
+        logger.info(f"初始用户偏好向量生成完成，成功: {processed}, 失败: {failed}")
+        
+    except Exception as e:
+        logger.error(f"初始用户偏好向量生成失败: {e}", exc_info=True)
+    finally:
+        if conn:
+            conn.close()
+
+
+def run_precompute():
+    """
+    运行所有预计算任务
+    """
+    logger.info("=" * 50)
+    logger.info("开始预计算任务")
+    logger.info("=" * 50)
+    
+    # 1. 优化数据库表结构
+    logger.info("\n[1/5] 优化数据库表结构...")
+    optimize_database_table()
+    
+    # # 2. 创建 Milvus 集合
+    # logger.info("\n[2/5] 创建 Milvus 集合...")
+    # create_collection()
+    
+    # 3. 历史数据迁移
+    logger.info("\n[3/5] 历史数据迁移...")
+    migrate_historical_data()
+    
+    # # 4. 系统图向量预计算
+    # logger.info("\n[4/5] 系统图向量预计算...")
+    # precompute_system_sketch_vectors()
+    
+    # 5. 初始用户偏好向量生成
+    logger.info("\n[5/5] 初始用户偏好向量生成...")
+    generate_initial_user_preference_vectors()
+    
+    logger.info("=" * 50)
+    logger.info("预计算任务完成")
+    logger.info("=" * 50)
+
+
+if __name__ == "__main__":
+    # 1. 优化数据库表结构
+    logger.info("\n[1/5] 优化数据库表结构...")
+    optimize_database_table()
+
+    # 3. 历史数据迁移
+    logger.info("\n[3/5] 历史数据迁移...")
+    migrate_historical_data()
+
+    # 5. 初始用户偏好向量生成
+    logger.info("\n[5/5] 初始用户偏好向量生成...")
+    generate_initial_user_preference_vectors()

app/service/recommendation_system/recommendation_api.py

@@ -0,0 +1,214 @@
+"""
+推荐接口实现
+实现探索/利用分支、向量检索、Softmax抽样等功能
+"""
+import logging
+import math
+import random
+import numpy as np
+from typing import List, Dict, Optional
+
+from app.service.recommendation_system.config import RECOMMENDATION_CONFIG, REDIS_KEY_USER_PREF_PREFIX
+from app.service.recommendation_system.milvus_client import search_similar_vectors, query_random_candidates
+from app.service.recommendation_system.precompute import compute_user_preference_vector
+from app.service.recommendation_system.vector_utils import normalize_vector
+from app.service.utils.redis_utils import Redis
+import json
+
+logger = logging.getLogger(__name__)
+
+
+def get_user_preference_vector(user_id: int, category: str) -> Optional[np.ndarray]:
+    """
+    获取用户偏好向量
+    
+    Args:
+        user_id: 用户ID
+        category: 类别
+    
+    Returns:
+        用户偏好向量（2048维），失败返回 None
+    """
+    # 1. 从 Redis 获取
+    key = f"{REDIS_KEY_USER_PREF_PREFIX}:{user_id}:{category}"
+    vector_json = Redis.read(key)
+    
+    if vector_json:
+        try:
+            vector_list = json.loads(vector_json)
+            return np.array(vector_list, dtype=np.float32)
+        except Exception as e:
+            logger.warning(f"解析 Redis 向量失败 [user={user_id}, category={category}]: {e}")
+    
+    # 2. 如果不存在，实时计算
+    logger.info(f"Redis 中不存在用户偏好向量，实时计算 [user={user_id}, category={category}]")
+    preference_vector = compute_user_preference_vector(user_id, category)
+    
+    if preference_vector is not None:
+        # 写入 Redis
+        vector_json = json.dumps(preference_vector.tolist())
+        Redis.write(
+            key=key,
+            value=vector_json,
+            expire=RECOMMENDATION_CONFIG["redis_expire_seconds"]
+        )
+    
+    return preference_vector
+
+
+def explore_branch(category: str, style: Optional[str] = None) -> List[str]:
+    """
+    探索分支（随机推荐）
+    
+    Args:
+        category: 类别
+        style: 风格（可选）
+    
+    Returns:
+        推荐结果列表，每个元素包含 path, style, category 等字段
+    """
+    # 查询候选（随机池）
+    pool_size = 10  # 固定查询10个，然后随机选择
+    
+    candidates = query_random_candidates(category, style, limit=pool_size)
+    
+    if not candidates:
+        logger.warning(f"探索分支：类别 {category} 没有候选数据")
+        return []
+    
+    # 随机选择
+    if len(candidates) > 1:
+        import random
+        candidates = random.sample(candidates, 1)
+    
+    # 格式化返回结果
+    return [candidate.get("path", "") for candidate in candidates[:1]]
+
+
+def exploit_branch(
+    user_id: int,
+    category: str,
+    style: Optional[str] = None
+) -> List[str]:
+    """
+    利用分支（基于向量相似度推荐）
+    
+    Args:
+        user_id: 用户ID
+        category: 类别
+        num_recommendations: 返回数量
+        style: 风格（可选，用于加分）
+    
+    Returns:
+        推荐结果列表，每个元素包含 path, style, category, similarity, sample_score 等字段
+    """
+    # 1. 获取用户偏好向量
+    embedding = get_user_preference_vector(user_id, category)
+    
+    if embedding is None:
+        logger.warning(f"利用分支：无法获取用户偏好向量，回退到探索分支 [user={user_id}, category={category}]")
+        return explore_branch(category, style)
+    
+    # 2. Milvus 相似度检索（内积 IP）
+    topk = RECOMMENDATION_CONFIG["topk"]
+    results = search_similar_vectors(embedding, category, topk)
+    
+    if not results:
+        logger.warning(f"利用分支：向量检索无结果，回退到探索分支 [user={user_id}, category={category}]")
+        return explore_branch(category, style)
+    
+    # 3. Style 加分（可选，需传入 style 参数）
+    style_bonus = RECOMMENDATION_CONFIG["style_bonus"]
+    if style:
+        for result in results:
+            similarity = result["score"]
+            if result.get("style") == style:
+                # 加分：相似度 * (1 + style_bonus)
+                similarity = similarity * (1 + style_bonus)
+            result["final_score"] = similarity
+    else:
+        for result in results:
+            result["final_score"] = result["score"]
+    
+    # 4. Softmax 抽样
+    scores = [r["final_score"] for r in results]
+    probabilities = softmax_with_temperature(scores, RECOMMENDATION_CONFIG["softmax_temperature"])
+    
+    # 根据概率抽样
+    if not results:
+        return []
+
+    selected_index = np.random.choice(len(results), size=1, p=probabilities, replace=False)
+    selected_results = [results[int(selected_index[0])]]
+
+    # 5. 返回结果
+    return [result.get("path", "") for result in selected_results]
+
+
+def softmax_with_temperature(scores: List[float], temperature: float = 1.0) -> List[float]:
+    """
+    Softmax 函数（带温度参数）
+    
+    Args:
+        scores: 分数列表
+        temperature: 温度参数
+    
+    Returns:
+        概率列表
+    """
+    if not scores:
+        return []
+    
+    # 除以温度
+    scaled_scores = [s / temperature for s in scores]
+    
+    # 减去最大值（数值稳定性）
+    max_score = max(scaled_scores)
+    exp_scores = [math.exp(s - max_score) for s in scaled_scores]
+    
+    # 归一化
+    sum_exp = sum(exp_scores)
+    if sum_exp == 0:
+        # 如果所有分数都是负无穷或非常小，返回均匀分布
+        return [1.0 / len(scores)] * len(scores)
+    
+    probabilities = [exp_s / sum_exp for exp_s in exp_scores]
+    return probabilities
+
+
+def get_recommendations(
+    user_id: int,
+    category: str,
+    style: Optional[str] = None
+) -> List[str]:
+    """
+    获取推荐结果（主函数）
+    
+    Args:
+        user_id: 用户ID
+        category: 类别（如 female_skirt）
+        num_recommendations: 返回推荐数量（默认 1）
+        style: 风格（可选）：若传入，则在利用分支对同 style 的候选进行加分
+    
+    Returns:
+        推荐结果列表，每个元素包含 path 等字段
+    """
+    try:
+        # 1. 读取配置参数
+        explore_ratio = RECOMMENDATION_CONFIG["explore_ratio"]
+        
+        # 2. 探索/利用决策
+        r = random.random()  # 生成随机数 (0-1)
+        
+        if r < explore_ratio:
+            logger.debug(f"探索分支 [user={user_id}, category={category}]")
+            return explore_branch(category, style)
+
+        logger.debug(f"利用分支 [user={user_id}, category={category}]")
+        return exploit_branch(user_id, category, style)
+        
+    except Exception as e:
+        logger.error(f"获取推荐结果失败 [user={user_id}, category={category}]: {e}", exc_info=True)
+        # 容错：回退到探索分支
+        return explore_branch(category, style)
+

app/service/recommendation_system/vector_utils.py

@@ -0,0 +1,189 @@
+"""
+向量计算工具类
+包含 ResNet50 特征提取、向量归一化等功能
+"""
+import io
+import logging
+import numpy as np
+import torch
+from torchvision import models, transforms
+from PIL import Image
+from minio import Minio
+
+from app.core.config import MINIO_URL, MINIO_ACCESS, MINIO_SECRET, MINIO_SECURE
+from app.service.recommendation_system.config import RECOMMENDATION_CONFIG
+
+logger = logging.getLogger(__name__)
+
+# 图像预处理（与ResNet训练时的预处理一致）
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),  # ResNet 要求 224x224 的输入
+    transforms.ToTensor(),  # 转换为 Tensor
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),  # 标准化
+])
+
+# 加载预训练的 ResNet50 模型（去掉最后全连接层）
+_resnet_model = None
+
+
+def get_resnet_model():
+    """获取 ResNet50 模型（单例模式）"""
+    global _resnet_model
+    if _resnet_model is None:
+        logger.info("加载 ResNet50 模型...")
+        _resnet_model = models.resnet50(pretrained=True)
+        modules = list(_resnet_model.children())[:-1]  # 移除最后的全连接层
+        _resnet_model = torch.nn.Sequential(*modules)
+        _resnet_model.eval()  # 设置为评估模式
+        logger.info("ResNet50 模型加载完成")
+    return _resnet_model
+
+
+# MinIO 客户端（单例）
+_minio_client = None
+
+
+def get_minio_client():
+    """获取 MinIO 客户端（单例模式）"""
+    global _minio_client
+    if _minio_client is None:
+        _minio_client = Minio(
+            MINIO_URL,
+            access_key=MINIO_ACCESS,
+            secret_key=MINIO_SECRET,
+            secure=MINIO_SECURE
+        )
+    return _minio_client
+
+
+def get_image_from_minio(path: str) -> Image.Image:
+    """
+    从 MinIO 获取图片
+    
+    Args:
+        path: MinIO 逻辑 URL，格式如 "bucket_name/object_name"
+    
+    Returns:
+        PIL Image 对象，失败返回 None
+    """
+    try:
+        # 分割路径，获取桶名和文件路径
+        path_parts = path.split('/', 1)
+        if len(path_parts) != 2:
+            logger.error(f"路径格式错误: {path}")
+            return None
+        
+        bucket_name, file_name = path_parts
+        minio_client = get_minio_client()
+        
+        # 获取文件
+        obj = minio_client.get_object(bucket_name, file_name)
+        img_data = obj.read()  # 读取图像数据
+        img = Image.open(io.BytesIO(img_data))  # 将数据转为图像对象
+        
+        return img
+    except Exception as e:
+        logger.error(f"从 MinIO 获取图片失败 [{path}]: {e}")
+        return None
+
+
+def extract_feature_vector(path: str) -> np.ndarray:
+    """
+    使用 ResNet50 提取图片特征向量（2048维）
+    
+    Args:
+        path: MinIO 逻辑 URL
+    
+    Returns:
+        2048维特征向量（numpy array），失败返回零向量
+    """
+    try:
+        # 从 MinIO 获取图像
+        img = get_image_from_minio(path)
+        if img is None:
+            logger.warning(f"无法获取图片，返回零向量: {path}")
+            return np.zeros(RECOMMENDATION_CONFIG["vector_dim"], dtype=np.float32)
+        
+        # 预处理
+        # 部分 MinIO 图片可能是 RGBA/CMYK，转换成 RGB 以匹配 3 通道标准化参数
+        if img.mode != "RGB":
+            try:
+                img = img.convert("RGB")
+            except Exception:
+                logger.warning(f"无法转换图片为RGB，返回零向量: {path}")
+                return np.zeros(RECOMMENDATION_CONFIG["vector_dim"], dtype=np.float32)
+
+        img_tensor = transform(img).unsqueeze(0)  # 扩展维度以适应批量处理
+        
+        # 提取特征
+        resnet_model = get_resnet_model()
+        with torch.no_grad():  # 在不需要计算梯度的情况下进行推断
+            feature_vector = resnet_model(img_tensor)  # 获取 ResNet 的输出
+            feature_vector = feature_vector.squeeze().cpu().numpy()  # 转换为 NumPy 数组并去掉 batch 维度
+        
+        # 确保是 2048 维
+        if feature_vector.ndim > 1:
+            feature_vector = feature_vector.flatten()
+        
+        # 确保维度正确
+        if len(feature_vector) != RECOMMENDATION_CONFIG["vector_dim"]:
+            logger.warning(f"向量维度不正确: {len(feature_vector)}, 期望: {RECOMMENDATION_CONFIG['vector_dim']}")
+            # 如果维度不对，尝试调整
+            if len(feature_vector) > RECOMMENDATION_CONFIG["vector_dim"]:
+                feature_vector = feature_vector[:RECOMMENDATION_CONFIG["vector_dim"]]
+            else:
+                padded = np.zeros(RECOMMENDATION_CONFIG["vector_dim"], dtype=np.float32)
+                padded[:len(feature_vector)] = feature_vector
+                feature_vector = padded
+        
+        return feature_vector.astype(np.float32)
+    except Exception as e:
+        logger.error(f"提取特征向量失败 [{path}]: {e}", exc_info=True)
+        return np.zeros(RECOMMENDATION_CONFIG["vector_dim"], dtype=np.float32)
+
+
+def normalize_vector(vector: np.ndarray) -> np.ndarray:
+    """
+    L2 归一化向量
+    
+    Args:
+        vector: 输入向量
+    
+    Returns:
+        归一化后的向量
+    """
+    norm = np.linalg.norm(vector)
+    if norm == 0:
+        return vector
+    return vector / norm
+
+
+def compute_weighted_average(vectors: list, weights: list) -> np.ndarray:
+    """
+    计算加权平均向量
+    
+    Args:
+        vectors: 向量列表
+        weights: 权重列表
+    
+    Returns:
+        加权平均向量（不做归一化，模长为加权平均后的尺度）
+    """
+    if not vectors or not weights:
+        return np.zeros(RECOMMENDATION_CONFIG["vector_dim"], dtype=np.float32)
+    
+    # 确保所有向量都是 numpy array
+    vectors = [np.array(v) for v in vectors]
+    weights = np.array(weights)
+    
+    # 计算加权和
+    weighted_sum = np.zeros_like(vectors[0])
+    for v, w in zip(vectors, weights):
+        weighted_sum += v * w
+    
+    # 返回加权平均（除以权重和，不做 L2 归一化，模长不会随条数线性暴涨）
+    weight_total = weights.sum()
+    if weight_total == 0:
+        return weighted_sum
+    return weighted_sum / weight_total
+