ruff
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LuiKlee
@@ -38,7 +38,7 @@ class InterestManager:
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self._calculation_queue = asyncio.Queue()
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self._worker_task = None
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self._shutdown_event = asyncio.Event()
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# 性能优化相关字段
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self._result_cache: OrderedDict[str, InterestCalculationResult] = OrderedDict() # LRU缓存
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self._cache_max_size = 1000 # 最大缓存数量
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@@ -48,13 +48,13 @@ class InterestManager:
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self._batch_timeout = 0.1 # 批处理超时（秒）
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self._batch_task = None
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self._is_warmed_up = False # 预热状态标记
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# 性能统计
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self._cache_hits = 0
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self._cache_misses = 0
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self._batch_calculations = 0
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self._total_calculation_time = 0.0
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self._initialized = True
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async def initialize(self):
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@@ -67,7 +67,7 @@ class InterestManager:
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async def shutdown(self):
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"""关闭管理器"""
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self._shutdown_event.set()
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# 取消批处理任务
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if self._batch_task and not self._batch_task.done():
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self._batch_task.cancel()
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@@ -79,7 +79,7 @@ class InterestManager:
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if self._current_calculator:
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await self._current_calculator.cleanup()
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self._current_calculator = None
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# 清理缓存
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self._result_cache.clear()
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@@ -142,9 +142,9 @@ class InterestManager:
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interest_value=0.3,
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error_message="没有可用的兴趣值计算组件",
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)
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message_id = getattr(message, "message_id", "")
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# 缓存查询
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if use_cache and message_id:
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cached_result = self._get_from_cache(message_id)
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@@ -183,11 +183,11 @@ class InterestManager:
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interest_value=0.3,
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error_message=f"计算异常: {e!s}",
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)
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# 缓存结果
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if use_cache and result.success and message_id:
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self._put_to_cache(message_id, result)
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return result
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async def _async_calculate(self, message: "DatabaseMessages") -> InterestCalculationResult:
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@@ -249,36 +249,36 @@ class InterestManager:
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break
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except Exception as e:
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logger.error(f"计算工作线程异常: {e}")
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def _get_from_cache(self, message_id: str) -> InterestCalculationResult | None:
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"""从缓存中获取结果（LRU策略）"""
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if message_id not in self._result_cache:
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return None
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# 检查TTL
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result = self._result_cache[message_id]
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if time.time() - result.timestamp > self._cache_ttl:
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# 过期，删除
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del self._result_cache[message_id]
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return None
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# 更新访问顺序（LRU）
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self._result_cache.move_to_end(message_id)
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return result
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def _put_to_cache(self, message_id: str, result: InterestCalculationResult):
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"""将结果放入缓存（LRU策略）"""
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# 如果已存在，更新
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if message_id in self._result_cache:
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self._result_cache.move_to_end(message_id)
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self._result_cache[message_id] = result
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# 限制缓存大小
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while len(self._result_cache) > self._cache_max_size:
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# 删除最旧的项
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self._result_cache.popitem(last=False)
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async def calculate_interest_batch(self, messages: list["DatabaseMessages"], timeout: float | None = None) -> list[InterestCalculationResult]:
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"""批量计算消息兴趣值（并发优化）
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@@ -291,11 +291,11 @@ class InterestManager:
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"""
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if not messages:
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return []
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# 并发计算所有消息
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tasks = [self.calculate_interest(msg, timeout=timeout) for msg in messages]
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results = await asyncio.gather(*tasks, return_exceptions=True)
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# 处理异常
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final_results = []
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for i, result in enumerate(results):
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@@ -309,44 +309,44 @@ class InterestManager:
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))
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else:
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final_results.append(result)
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self._batch_calculations += 1
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return final_results
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async def _batch_processing_worker(self):
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"""批处理工作线程"""
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while not self._shutdown_event.is_set():
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batch = []
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deadline = time.time() + self._batch_timeout
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try:
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# 收集批次
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while len(batch) < self._batch_size and time.time() < deadline:
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remaining_time = deadline - time.time()
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if remaining_time <= 0:
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break
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try:
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item = await asyncio.wait_for(self._batch_queue.get(), timeout=remaining_time)
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batch.append(item)
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except asyncio.TimeoutError:
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break
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# 处理批次
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if batch:
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await self._process_batch(batch)
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except asyncio.CancelledError:
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break
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except Exception as e:
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logger.error(f"批处理工作线程异常: {e}")
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async def _process_batch(self, batch: list):
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"""处理批次消息"""
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# 这里可以实现具体的批处理逻辑
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# 当前版本只是占位，实际的批处理逻辑可以根据具体需求实现
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pass
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async def warmup(self, sample_messages: list["DatabaseMessages"] | None = None):
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"""预热兴趣计算器
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@@ -356,10 +356,10 @@ class InterestManager:
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if not self._current_calculator:
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logger.warning("无法预热：没有可用的兴趣值计算组件")
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return
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logger.info("开始预热兴趣值计算器...")
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start_time = time.time()
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# 如果提供了样本消息，进行预热计算
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if sample_messages:
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try:
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@@ -370,15 +370,15 @@ class InterestManager:
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logger.error(f"预热过程中出现异常: {e}")
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else:
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logger.info(f"预热完成：计算器已就绪，耗时 {time.time() - start_time:.2f}s")
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self._is_warmed_up = True
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def clear_cache(self):
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"""清空缓存"""
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cleared_count = len(self._result_cache)
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self._result_cache.clear()
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logger.info(f"已清空 {cleared_count} 条缓存记录")
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def set_cache_config(self, max_size: int | None = None, ttl: int | None = None):
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"""设置缓存配置
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@@ -389,11 +389,11 @@ class InterestManager:
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if max_size is not None:
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self._cache_max_size = max_size
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logger.info(f"缓存最大容量设置为: {max_size}")
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if ttl is not None:
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self._cache_ttl = ttl
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logger.info(f"缓存TTL设置为: {ttl}秒")
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# 如果当前缓存超过新的最大值，清理旧数据
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if max_size is not None:
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while len(self._result_cache) > self._cache_max_size:
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@@ -446,14 +446,14 @@ class InterestManager:
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def has_calculator(self) -> bool:
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"""检查是否有可用的计算组件"""
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return self._current_calculator is not None and self._current_calculator.is_enabled
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async def adaptive_optimize(self):
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"""自适应优化：根据性能统计自动调整参数"""
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if not self._current_calculator:
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return
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stats = self.get_statistics()["manager_statistics"]
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# 根据缓存命中率调整缓存大小
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cache_hit_rate = stats["cache_hit_rate"]
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if cache_hit_rate < 0.5 and self._cache_max_size < 5000:
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@@ -469,7 +469,7 @@ class InterestManager:
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# 清理多余缓存
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while len(self._result_cache) > self._cache_max_size:
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self._result_cache.popitem(last=False)
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# 根据平均计算时间调整批处理参数
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avg_calc_time = stats["average_calculation_time"]
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if avg_calc_time > 0.5 and self._batch_size < 50:
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@@ -482,11 +482,11 @@ class InterestManager:
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new_batch_size = max(self._batch_size // 2, 5)
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logger.info(f"自适应优化：平均计算时间较短 ({avg_calc_time:.3f}s)，减小批次大小 {self._batch_size} -> {new_batch_size}")
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self._batch_size = new_batch_size
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def get_performance_report(self) -> str:
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"""生成性能报告"""
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stats = self.get_statistics()["manager_statistics"]
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report = [
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"=" * 60,
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"兴趣值管理器性能报告",
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@@ -504,7 +504,7 @@ class InterestManager:
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f"当前计算器: {stats['current_calculator'] or '无'}",
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"=" * 60,
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]
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# 添加计算器统计
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if self._current_calculator:
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calc_stats = self.get_statistics()["calculator_statistics"]
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@@ -520,7 +520,7 @@ class InterestManager:
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f" 平均耗时: {calc_stats['average_calculation_time']:.4f}s",
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"=" * 60,
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])
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return "\n".join(report)
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