feat(statistic): 优化内存使用，添加分批查询和统计处理上限

feat(typo_generator): 实现单例模式以复用拼音字典和字频数据
feat(query): 添加分批迭代获取结果的功能，优化内存使用

src/chat/utils/statistic.py

@@ -4,6 +4,7 @@ from datetime import datetime, timedelta
 from typing import Any
 
 from src.common.database.compatibility import db_get, db_query
+from src.common.database.api.query import QueryBuilder
 from src.common.database.core.models import LLMUsage, Messages, OnlineTime
 from src.common.logger import get_logger
 from src.manager.async_task_manager import AsyncTask
@@ -11,6 +12,11 @@ from src.manager.local_store_manager import local_storage
 
 logger = get_logger("maibot_statistic")
 
+# 统计查询的批次大小
+STAT_BATCH_SIZE = 2000
+# 内存优化：单次统计最大处理记录数（防止极端情况）
+STAT_MAX_RECORDS = 100000
+
 # 彻底异步化：删除原同步包装器 _sync_db_get，所有数据库访问统一使用 await db_get。
 
 
@@ -314,17 +320,23 @@ class StatisticOutputTask(AsyncTask):
         }
 
         # 以最早的时间戳为起始时间获取记录
+        # 🔧 内存优化：使用分批查询代替全量加载
         query_start_time = collect_period[-1][1]
-        records = (
+        
-            await db_get(
+        query_builder = (
-                model_class=LLMUsage,
+            QueryBuilder(LLMUsage)
-                filters={"timestamp": {"$gte": query_start_time}},
+            .no_cache()
-                order_by="-timestamp",
+            .filter(timestamp__gte=query_start_time)
-            )
+            .order_by("-timestamp")
-            or []
         )
         
-        for record_idx, record in enumerate(records, 1):
+        total_processed = 0
+        async for batch in query_builder.iter_batches(batch_size=STAT_BATCH_SIZE, as_dict=True):
+            for record in batch:
+                if total_processed >= STAT_MAX_RECORDS:
+                    logger.warning(f"统计处理记录数达到上限 {STAT_MAX_RECORDS}，跳过剩余记录")
+                    break
+                    
                 if not isinstance(record, dict):
                     continue
 
@@ -392,7 +404,16 @@ class StatisticOutputTask(AsyncTask):
                                 stats[period_key][TIME_COST_BY_PROVIDER][provider_name].append(time_cost)
                         break
 
-            await StatisticOutputTask._yield_control(record_idx)
+                total_processed += 1
+                if total_processed % 500 == 0:
+                    await StatisticOutputTask._yield_control(total_processed, interval=1)
+            
+            # 检查是否达到上限
+            if total_processed >= STAT_MAX_RECORDS:
+                break
+            
+            # 每批处理完后让出控制权
+            await asyncio.sleep(0)
         # -- 计算派生指标 --
         for period_key, period_stats in stats.items():
             # 计算模型相关指标
@@ -591,16 +612,16 @@ class StatisticOutputTask(AsyncTask):
         }
 
         query_start_time = collect_period[-1][1]
-        records = (
+        # 🔧 内存优化：使用分批查询
-            await db_get(
+        query_builder = (
-                model_class=OnlineTime,
+            QueryBuilder(OnlineTime)
-                filters={"end_timestamp": {"$gte": query_start_time}},
+            .no_cache()
-                order_by="-end_timestamp",
+            .filter(end_timestamp__gte=query_start_time)
-            )
+            .order_by("-end_timestamp")
-            or []
         )
 
-        for record_idx, record in enumerate(records, 1):
+        async for batch in query_builder.iter_batches(batch_size=STAT_BATCH_SIZE, as_dict=True):
+            for record in batch:
                 if not isinstance(record, dict):
                     continue
 
@@ -629,7 +650,9 @@ class StatisticOutputTask(AsyncTask):
                                 stats[period_key][ONLINE_TIME] += (overlap_end - overlap_start).total_seconds()
                         break
             
-            await StatisticOutputTask._yield_control(record_idx)
+            # 每批处理完后让出控制权
+            await asyncio.sleep(0)
+
         return stats
 
     async def _collect_message_count_for_period(self, collect_period: list[tuple[str, datetime]]) -> dict[str, Any]:
@@ -652,16 +675,21 @@ class StatisticOutputTask(AsyncTask):
         }
 
         query_start_timestamp = collect_period[-1][1].timestamp()  # Messages.time is a DoubleField (timestamp)
-        records = (
+        # 🔧 内存优化：使用分批查询
-            await db_get(
+        query_builder = (
-                model_class=Messages,
+            QueryBuilder(Messages)
-                filters={"time": {"$gte": query_start_timestamp}},
+            .no_cache()
-                order_by="-time",
+            .filter(time__gte=query_start_timestamp)
-            )
+            .order_by("-time")
-            or []
         )
 
-        for message_idx, message in enumerate(records, 1):
+        total_processed = 0
+        async for batch in query_builder.iter_batches(batch_size=STAT_BATCH_SIZE, as_dict=True):
+            for message in batch:
+                if total_processed >= STAT_MAX_RECORDS:
+                    logger.warning(f"消息统计处理记录数达到上限 {STAT_MAX_RECORDS}，跳过剩余记录")
+                    break
+                    
                 if not isinstance(message, dict):
                     continue
                 message_time_ts = message.get("time")  # This is a float timestamp
@@ -682,7 +710,6 @@ class StatisticOutputTask(AsyncTask):
                     chat_name = message.get("user_nickname")  # SENDER's nickname
                 else:
                     # If neither group_id nor sender_id is available for chat identification
-                logger.warning(f"Message (PK: {message.get('id', 'N/A')}) lacks group_id and user_id for chat stats.")
                     continue
 
                 if not chat_id:  # Should not happen if above logic is correct
@@ -702,7 +729,16 @@ class StatisticOutputTask(AsyncTask):
                             stats[period_key][MSG_CNT_BY_CHAT][chat_id] += 1
                         break
 
-            await StatisticOutputTask._yield_control(message_idx)
+                total_processed += 1
+                if total_processed % 500 == 0:
+                    await StatisticOutputTask._yield_control(total_processed, interval=1)
+            
+            # 检查是否达到上限
+            if total_processed >= STAT_MAX_RECORDS:
+                break
+            
+            # 每批处理完后让出控制权
+            await asyncio.sleep(0)
 
         return stats
 
@@ -755,7 +791,38 @@ class StatisticOutputTask(AsyncTask):
                     current_dict = stat["all_time"][key]
                     for sub_key, sub_val in val.items():
                         if sub_key in current_dict:
-                            # For lists (like TIME_COST), this extends. For numbers, this adds.
+                            current_val = current_dict[sub_key]
+                            # 🔧 内存优化：处理压缩格式的 TIME_COST 数据
+                            if isinstance(sub_val, dict) and "sum" in sub_val and "count" in sub_val:
+                                # 压缩格式合并
+                                if isinstance(current_val, dict) and "sum" in current_val:
+                                    # 两边都是压缩格式
+                                    current_dict[sub_key] = {
+                                        "sum": current_val["sum"] + sub_val["sum"],
+                                        "count": current_val["count"] + sub_val["count"],
+                                        "sum_sq": current_val.get("sum_sq", 0) + sub_val.get("sum_sq", 0),
+                                    }
+                                elif isinstance(current_val, list):
+                                    # 当前是列表，历史是压缩格式：先压缩当前再合并
+                                    curr_sum = sum(current_val) if current_val else 0
+                                    curr_count = len(current_val)
+                                    curr_sum_sq = sum(v * v for v in current_val) if current_val else 0
+                                    current_dict[sub_key] = {
+                                        "sum": curr_sum + sub_val["sum"],
+                                        "count": curr_count + sub_val["count"],
+                                        "sum_sq": curr_sum_sq + sub_val.get("sum_sq", 0),
+                                    }
+                                else:
+                                    # 未知情况，保留历史值
+                                    current_dict[sub_key] = sub_val
+                            elif isinstance(sub_val, list):
+                                # 列表格式：extend（兼容旧数据，但新版不会产生这种情况）
+                                if isinstance(current_val, list):
+                                    current_dict[sub_key] = current_val + sub_val
+                                else:
+                                    current_dict[sub_key] = sub_val
+                            else:
+                                # 数值类型：直接相加
                                 current_dict[sub_key] += sub_val
                         else:
                             current_dict[sub_key] = sub_val
@@ -764,8 +831,10 @@ class StatisticOutputTask(AsyncTask):
                     stat["all_time"][key] += val
 
         # 更新上次完整统计数据的时间戳
+        # 🔧 内存优化：在保存前压缩 TIME_COST 列表为聚合数据，避免无限增长
+        compressed_stat_data = self._compress_time_cost_lists(stat["all_time"])
         # 将所有defaultdict转换为普通dict以避免类型冲突
-        clean_stat_data = self._convert_defaultdict_to_dict(stat["all_time"])
+        clean_stat_data = self._convert_defaultdict_to_dict(compressed_stat_data)
         local_storage["last_full_statistics"] = {
             "name_mapping": self.name_mapping,
             "stat_data": clean_stat_data,
@@ -774,6 +843,54 @@ class StatisticOutputTask(AsyncTask):
 
         return stat
 
+    def _compress_time_cost_lists(self, data: dict[str, Any]) -> dict[str, Any]:
+        """🔧 内存优化：将 TIME_COST_BY_* 的 list 压缩为聚合数据
+        
+        原始格式: {"model_a": [1.2, 2.3, 3.4, ...]}  (可能无限增长)
+        压缩格式: {"model_a": {"sum": 6.9, "count": 3, "sum_sq": 18.29}}
+        
+        这样合并时只需要累加 sum/count/sum_sq，不会无限增长。
+        avg = sum / count
+        std = sqrt(sum_sq / count - (sum / count)^2)
+        """
+        # TIME_COST 相关的 key 前缀
+        time_cost_keys = [
+            TIME_COST_BY_TYPE, TIME_COST_BY_USER, TIME_COST_BY_MODEL,
+            TIME_COST_BY_MODULE, TIME_COST_BY_PROVIDER
+        ]
+        
+        result = dict(data)  # 浅拷贝
+        
+        for key in time_cost_keys:
+            if key not in result:
+                continue
+            
+            original = result[key]
+            if not isinstance(original, dict):
+                continue
+            
+            compressed = {}
+            for sub_key, values in original.items():
+                if isinstance(values, list):
+                    # 原始列表格式，需要压缩
+                    if values:
+                        total = sum(values)
+                        count = len(values)
+                        sum_sq = sum(v * v for v in values)
+                        compressed[sub_key] = {"sum": total, "count": count, "sum_sq": sum_sq}
+                    else:
+                        compressed[sub_key] = {"sum": 0.0, "count": 0, "sum_sq": 0.0}
+                elif isinstance(values, dict) and "sum" in values and "count" in values:
+                    # 已经是压缩格式，直接保留
+                    compressed[sub_key] = values
+                else:
+                    # 未知格式，保留原值
+                    compressed[sub_key] = values
+            
+            result[key] = compressed
+        
+        return result
+
     def _convert_defaultdict_to_dict(self, data):
         # sourcery skip: dict-comprehension, extract-duplicate-method, inline-immediately-returned-variable, merge-duplicate-blocks
         """递归转换defaultdict为普通dict"""
@@ -884,16 +1001,16 @@ class StatisticOutputTask(AsyncTask):
         time_labels = [t.strftime("%H:%M") for t in time_points]
         interval_seconds = interval_minutes * 60
 
-        # 单次查询 LLMUsage
+        # 🔧 内存优化：使用分批查询 LLMUsage
-        llm_records = (
+        llm_query_builder = (
-            await db_get(
+            QueryBuilder(LLMUsage)
-                model_class=LLMUsage,
+            .no_cache()
-                filters={"timestamp": {"$gte": start_time}},
+            .filter(timestamp__gte=start_time)
-                order_by="-timestamp",
+            .order_by("-timestamp")
         )
-            or []
+        
-        )
+        async for batch in llm_query_builder.iter_batches(batch_size=STAT_BATCH_SIZE, as_dict=True):
-        for record_idx, record in enumerate(llm_records, 1):
+            for record in batch:
                 if not isinstance(record, dict) or not record.get("timestamp"):
                     continue
                 record_time = record["timestamp"]
@@ -917,18 +1034,18 @@ class StatisticOutputTask(AsyncTask):
                         cost_by_module[module_name] = [0.0] * len(time_points)
                     cost_by_module[module_name][idx] += cost
             
-            await StatisticOutputTask._yield_control(record_idx)
+            await asyncio.sleep(0)
 
-        # 单次查询 Messages
+        # 🔧 内存优化：使用分批查询 Messages
-        msg_records = (
+        msg_query_builder = (
-            await db_get(
+            QueryBuilder(Messages)
-                model_class=Messages,
+            .no_cache()
-                filters={"time": {"$gte": start_time.timestamp()}},
+            .filter(time__gte=start_time.timestamp())
-                order_by="-time",
+            .order_by("-time")
         )
-            or []
+        
-        )
+        async for batch in msg_query_builder.iter_batches(batch_size=STAT_BATCH_SIZE, as_dict=True):
-        for msg_idx, msg in enumerate(msg_records, 1):
+            for msg in batch:
                 if not isinstance(msg, dict) or not msg.get("time"):
                     continue
                 msg_ts = msg["time"]
@@ -947,7 +1064,7 @@ class StatisticOutputTask(AsyncTask):
                             message_by_chat[chat_name] = [0] * len(time_points)
                         message_by_chat[chat_name][idx] += 1
             
-            await StatisticOutputTask._yield_control(msg_idx)
+            await asyncio.sleep(0)
 
         return {
             "time_labels": time_labels,

src/chat/utils/typo_generator.py

@@ -1,5 +1,7 @@
 """
 错别字生成器 - 基于拼音和字频的中文错别字生成工具
+
+内存优化：使用单例模式，避免重复创建拼音字典（约20992个汉字映射）
 """
 
 import math
@@ -8,6 +10,7 @@ import random
 import time
 from collections import defaultdict
 from pathlib import Path
+from threading import Lock
 
 import orjson
 import rjieba
@@ -17,6 +20,59 @@ from src.common.logger import get_logger
 
 logger = get_logger("typo_gen")
 
+# 🔧 全局单例和缓存
+_typo_generator_singleton: "ChineseTypoGenerator | None" = None
+_singleton_lock = Lock()
+_shared_pinyin_dict: dict | None = None
+_shared_char_frequency: dict | None = None
+
+
+def get_typo_generator(
+    error_rate: float = 0.3,
+    min_freq: int = 5,
+    tone_error_rate: float = 0.2,
+    word_replace_rate: float = 0.3,
+    max_freq_diff: int = 200,
+) -> "ChineseTypoGenerator":
+    """
+    获取错别字生成器单例（内存优化）
+    
+    如果参数与缓存的单例不同，会更新参数但复用拼音字典和字频数据。
+    
+    参数:
+        error_rate: 单字替换概率
+        min_freq: 最小字频阈值
+        tone_error_rate: 声调错误概率
+        word_replace_rate: 整词替换概率
+        max_freq_diff: 最大允许的频率差异
+        
+    返回:
+        ChineseTypoGenerator 实例
+    """
+    global _typo_generator_singleton
+    
+    with _singleton_lock:
+        if _typo_generator_singleton is None:
+            _typo_generator_singleton = ChineseTypoGenerator(
+                error_rate=error_rate,
+                min_freq=min_freq,
+                tone_error_rate=tone_error_rate,
+                word_replace_rate=word_replace_rate,
+                max_freq_diff=max_freq_diff,
+            )
+            logger.info("ChineseTypoGenerator 单例已创建")
+        else:
+            # 更新参数但复用字典
+            _typo_generator_singleton.set_params(
+                error_rate=error_rate,
+                min_freq=min_freq,
+                tone_error_rate=tone_error_rate,
+                word_replace_rate=word_replace_rate,
+                max_freq_diff=max_freq_diff,
+            )
+    
+    return _typo_generator_singleton
+
 
 class ChineseTypoGenerator:
     def __init__(self, error_rate=0.3, min_freq=5, tone_error_rate=0.2, word_replace_rate=0.3, max_freq_diff=200):
@@ -30,18 +86,24 @@ class ChineseTypoGenerator:
             word_replace_rate: 整词替换概率
             max_freq_diff: 最大允许的频率差异
         """
+        global _shared_pinyin_dict, _shared_char_frequency
+        
         self.error_rate = error_rate
         self.min_freq = min_freq
         self.tone_error_rate = tone_error_rate
         self.word_replace_rate = word_replace_rate
         self.max_freq_diff = max_freq_diff
 
-        # 加载数据
+        # 🔧 内存优化：复用全局缓存的拼音字典和字频数据
-        # print("正在加载汉字数据库，请稍候...")
+        if _shared_pinyin_dict is None:
-        # logger.info("正在加载汉字数据库，请稍候...")
+            _shared_pinyin_dict = self._create_pinyin_dict()
+            logger.debug("拼音字典已创建并缓存")
+        self.pinyin_dict = _shared_pinyin_dict
         
-        self.pinyin_dict = self._create_pinyin_dict()
+        if _shared_char_frequency is None:
-        self.char_frequency = self._load_or_create_char_frequency()
+            _shared_char_frequency = self._load_or_create_char_frequency()
+            logger.debug("字频数据已加载并缓存")
+        self.char_frequency = _shared_char_frequency
 
     def _load_or_create_char_frequency(self):
         """
@@ -433,7 +495,7 @@ class ChineseTypoGenerator:
 
     def set_params(self, **kwargs):
         """
-        设置参数
+        设置参数（静默模式，供单例复用时调用）
 
         可设置参数:
             error_rate: 单字替换概率
@@ -445,9 +507,6 @@ class ChineseTypoGenerator:
         for key, value in kwargs.items():
             if hasattr(self, key):
                 setattr(self, key, value)
-                print(f"参数 {key} 已设置为 {value}")
-            else:
-                print(f"警告: 参数 {key} 不存在")
 
 
 def main():

src/chat/utils/utils.py

@@ -16,7 +16,7 @@ from src.config.config import global_config, model_config
 from src.llm_models.utils_model import LLMRequest
 from src.person_info.person_info import PersonInfoManager, get_person_info_manager
 from src.common.data_models.database_data_model import DatabaseUserInfo
-from .typo_generator import ChineseTypoGenerator
+from .typo_generator import get_typo_generator
 
 logger = get_logger("chat_utils")
 
@@ -443,7 +443,8 @@ def process_llm_response(text: str, enable_splitter: bool = True, enable_chinese
     #     logger.warning(f"回复过长 ({len(cleaned_text)} 字符)，返回默认回复")
     #     return ["懒得说"]
 
-    typo_generator = ChineseTypoGenerator(
+    # 🔧 内存优化：使用单例工厂函数，避免重复创建拼音字典
+    typo_generator = get_typo_generator(
         error_rate=global_config.chinese_typo.error_rate,
         min_freq=global_config.chinese_typo.min_freq,
         tone_error_rate=global_config.chinese_typo.tone_error_rate,

src/common/database/api/query.py

@@ -5,8 +5,10 @@
 - 聚合查询
 - 排序和分页
 - 关联查询
+- 流式迭代（内存优化）
 """
 
+from collections.abc import AsyncIterator
 from typing import Any, Generic, TypeVar
 
 from sqlalchemy import and_, asc, desc, func, or_, select
@@ -183,6 +185,84 @@ class QueryBuilder(Generic[T]):
         self._use_cache = False
         return self
 
+    async def iter_batches(
+        self,
+        batch_size: int = 1000,
+        *,
+        as_dict: bool = True,
+    ) -> AsyncIterator[list[T] | list[dict[str, Any]]]:
+        """分批迭代获取结果（内存优化）
+
+        使用 LIMIT/OFFSET 分页策略，避免一次性加载全部数据到内存。
+        适用于大数据量的统计、导出等场景。
+
+        Args:
+            batch_size: 每批获取的记录数，默认1000
+            as_dict: 为True时返回字典格式
+
+        Yields:
+            每批的模型实例列表或字典列表
+
+        Example:
+            async for batch in query_builder.iter_batches(batch_size=500):
+                for record in batch:
+                    process(record)
+        """
+        offset = 0
+
+        while True:
+            # 构建带分页的查询
+            paginated_stmt = self._stmt.offset(offset).limit(batch_size)
+
+            async with get_db_session() as session:
+                result = await session.execute(paginated_stmt)
+                # .all() 已经返回 list，无需再包装
+                instances = result.scalars().all()
+
+                if not instances:
+                    # 没有更多数据
+                    break
+
+                # 在 session 内部转换为字典列表
+                instances_dicts = [_model_to_dict(inst) for inst in instances]
+
+                if as_dict:
+                    yield instances_dicts
+                else:
+                    yield [_dict_to_model(self.model, row) for row in instances_dicts]
+
+                # 如果返回的记录数小于 batch_size，说明已经是最后一批
+                if len(instances) < batch_size:
+                    break
+
+                offset += batch_size
+
+    async def iter_all(
+        self,
+        batch_size: int = 1000,
+        *,
+        as_dict: bool = True,
+    ) -> AsyncIterator[T | dict[str, Any]]:
+        """逐条迭代所有结果（内存优化）
+
+        内部使用分批获取，但对外提供逐条迭代的接口。
+        适用于需要逐条处理但数据量很大的场景。
+
+        Args:
+            batch_size: 内部分批大小，默认1000
+            as_dict: 为True时返回字典格式
+
+        Yields:
+            单个模型实例或字典
+
+        Example:
+            async for record in query_builder.iter_all():
+                process(record)
+        """
+        async for batch in self.iter_batches(batch_size=batch_size, as_dict=as_dict):
+            for item in batch:
+                yield item
+
     async def all(self, *, as_dict: bool = False) -> list[T] | list[dict[str, Any]]:
         """获取所有结果