docs: update README/INTERNALS for import feature, harden .gitignore
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P0luz
122
decay_engine.py
122
decay_engine.py
@@ -70,93 +70,102 @@ class DecayEngine:
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# Permanent buckets never decay / 固化桶永远不衰减
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# ---------------------------------------------------------
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# ---------------------------------------------------------
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# Time weight: 0-1d→1.0, day2→0.9, then ~10%/day, floor 0.3
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# 时间系数：0-1天=1.0，第2天=0.9，之后每天约降10%，7天后稳定在0.3
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# Freshness bonus: continuous exponential decay
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# 新鲜度加成：连续指数衰减
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# bonus = 1.0 + 1.0 × e^(-t/36), t in hours
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# t=0 → 2.0×, t≈25h(半衰) → 1.5×, t≈72h → ≈1.14×, t→∞ → 1.0×
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# ---------------------------------------------------------
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@staticmethod
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def _calc_time_weight(days_since: float) -> float:
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"""
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Piecewise time weight multiplier (multiplies base_score).
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分段式时间权重系数，作为 final_score 的乘数。
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Freshness bonus multiplier: 1.0 + e^(-t/36), t in hours.
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新鲜度加成乘数：刚存入×2.0，~36小时半衰，72小时后趋近×1.0。
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"""
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if days_since <= 1.0:
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return 1.0
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elif days_since <= 2.0:
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# Linear interpolation: 1.0→0.9 over [1,2]
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return 1.0 - 0.1 * (days_since - 1.0)
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else:
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# Exponential decay from 0.9, floor at 0.3
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# k = ln(3)/5 ≈ 0.2197 so that at day 7 (5 days past day 2) → 0.3
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raw = 0.9 * math.exp(-0.2197 * (days_since - 2.0))
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return max(0.3, raw)
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hours = days_since * 24.0
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return 1.0 + 1.0 * math.exp(-hours / 36.0)
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def calculate_score(self, metadata: dict) -> float:
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"""
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Calculate current activity score for a memory bucket.
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计算一个记忆桶的当前活跃度得分。
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Formula: final_score = time_weight × base_score
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base_score = Importance × (act_count^0.3) × e^(-λ×days) × (base + arousal×boost)
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time_weight is the outer multiplier, takes priority over emotion factors.
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New model: short-term vs long-term weight separation.
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新模型：短期/长期权重分离。
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- Short-term (≤3 days): time_weight dominates, emotion amplifies
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- Long-term (>3 days): emotion_weight dominates, time decays to floor
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短期（≤3天）：时间权重主导，情感放大
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长期（>3天）：情感权重主导，时间衰减到底线
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"""
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if not isinstance(metadata, dict):
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return 0.0
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# --- Pinned/protected buckets: never decay, importance locked to 10 ---
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# --- 固化桶（pinned/protected）：永不衰减，importance 锁定为 10 ---
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if metadata.get("pinned") or metadata.get("protected"):
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return 999.0
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# --- Permanent buckets never decay / 固化桶永不衰减 ---
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# --- Permanent buckets never decay ---
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if metadata.get("type") == "permanent":
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return 999.0
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# --- Feel buckets: never decay, fixed moderate score ---
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if metadata.get("type") == "feel":
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return 50.0
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importance = max(1, min(10, int(metadata.get("importance", 5))))
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activation_count = max(1, int(metadata.get("activation_count", 1)))
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# --- Days since last activation / 距离上次激活过了多少天 ---
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# --- Days since last activation ---
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last_active_str = metadata.get("last_active", metadata.get("created", ""))
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try:
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last_active = datetime.fromisoformat(str(last_active_str))
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days_since = max(0.0, (datetime.now() - last_active).total_seconds() / 86400)
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except (ValueError, TypeError):
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days_since = 30 # Parse failure → assume 30 days / 解析失败假设已过 30 天
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days_since = 30
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# --- Emotion weight: continuous arousal coordinate ---
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# --- 情感权重：基于连续 arousal 坐标计算 ---
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# Higher arousal → stronger emotion → higher weight → slower decay
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# arousal 越高 → 情感越强烈 → 权重越大 → 衰减越慢
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# --- Emotion weight ---
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try:
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arousal = max(0.0, min(1.0, float(metadata.get("arousal", 0.3))))
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except (ValueError, TypeError):
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arousal = 0.3
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emotion_weight = self.emotion_base + arousal * self.arousal_boost
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# --- Time weight (outer multiplier, highest priority) ---
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# --- 时间权重（外层乘数，优先级最高）---
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# --- Time weight ---
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time_weight = self._calc_time_weight(days_since)
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# --- Base score = Importance × act_count^0.3 × e^(-λ×days) × emotion ---
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# --- 基础得分 ---
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# --- Short-term vs Long-term weight separation ---
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# 短期（≤3天）：time_weight 占 70%，emotion 占 30%
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# 长期（>3天）：emotion 占 70%，time_weight 占 30%
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if days_since <= 3.0:
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# Short-term: time dominates, emotion amplifies
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combined_weight = time_weight * 0.7 + emotion_weight * 0.3
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else:
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# Long-term: emotion dominates, time provides baseline
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combined_weight = emotion_weight * 0.7 + time_weight * 0.3
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# --- Base score ---
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base_score = (
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importance
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* (activation_count ** 0.3)
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* math.exp(-self.decay_lambda * days_since)
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* emotion_weight
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* combined_weight
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)
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# --- final_score = time_weight × base_score ---
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score = time_weight * base_score
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# --- Weight pool modifiers ---
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# resolved + digested (has feel) → accelerated fade: ×0.02
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# resolved only → ×0.05
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# 已处理+已消化（写过feel）→ 加速淡化：×0.02
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# 仅已处理 → ×0.05
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resolved = metadata.get("resolved", False)
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digested = metadata.get("digested", False) # set when feel is written for this memory
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if resolved and digested:
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resolved_factor = 0.02
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elif resolved:
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resolved_factor = 0.05
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else:
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resolved_factor = 1.0
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urgency_boost = 1.5 if (arousal > 0.7 and not resolved) else 1.0
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# --- Weight pool modifiers / 权重池修正因子 ---
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# Resolved events drop to 5%, sink to bottom awaiting keyword reactivation
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# 已解决的事件权重骤降到 5%，沉底等待关键词激活
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resolved_factor = 0.05 if metadata.get("resolved", False) else 1.0
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# High-arousal unresolved buckets get urgency boost for priority surfacing
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# 高唤醒未解决桶额外加成，优先浮现
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urgency_boost = 1.5 if (arousal > 0.7 and not metadata.get("resolved", False)) else 1.0
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return round(score * resolved_factor * urgency_boost, 4)
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return round(base_score * resolved_factor * urgency_boost, 4)
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# ---------------------------------------------------------
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# Execute one decay cycle
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@@ -180,17 +189,41 @@ class DecayEngine:
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checked = 0
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archived = 0
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auto_resolved = 0
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lowest_score = float("inf")
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for bucket in buckets:
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meta = bucket.get("metadata", {})
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# Skip permanent / pinned / protected buckets
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# 跳过固化桶和钉选/保护桶
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if meta.get("type") == "permanent" or meta.get("pinned") or meta.get("protected"):
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# Skip permanent / pinned / protected / feel buckets
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# 跳过固化桶、钉选/保护桶和 feel 桶
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if meta.get("type") in ("permanent", "feel") or meta.get("pinned") or meta.get("protected"):
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continue
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checked += 1
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# --- Auto-resolve: imp≤4 + >30 days old + not resolved → auto resolve ---
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# --- 自动结案：重要度≤4 + 超过30天 + 未解决 → 自动 resolve ---
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if not meta.get("resolved", False):
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imp = int(meta.get("importance", 5))
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last_active_str = meta.get("last_active", meta.get("created", ""))
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try:
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last_active = datetime.fromisoformat(str(last_active_str))
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days_since = (datetime.now() - last_active).total_seconds() / 86400
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except (ValueError, TypeError):
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days_since = 999
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if imp <= 4 and days_since > 30:
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try:
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await self.bucket_mgr.update(bucket["id"], resolved=True)
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auto_resolved += 1
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logger.info(
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f"Auto-resolved / 自动结案: "
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f"{meta.get('name', bucket['id'])} "
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f"(imp={imp}, days={days_since:.0f})"
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)
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except Exception as e:
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logger.warning(f"Auto-resolve failed / 自动结案失败: {e}")
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try:
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score = self.calculate_score(meta)
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except Exception as e:
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@@ -223,6 +256,7 @@ class DecayEngine:
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result = {
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"checked": checked,
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"archived": archived,
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"auto_resolved": auto_resolved,
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"lowest_score": lowest_score if checked > 0 else 0,
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}
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logger.info(f"Decay cycle complete / 衰减周期完成: {result}")
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