确定把# layers/conscious.py
import logging
import time
from collections import deque
from typing import Dict, List, Optional, Any
from enum import Enum
from .preconscious import PreconsciousLayer
from ..states import ConsciousState
from ..memory import MemorySystem
from ..cognitive import CognitiveProcessor, WorkloadManager
from ..perception.stimulus import Stimulus
from ..conscious_stream import ConsciousnessStream # 新增意识流系统
logger = logging.getLogger('ConsciousLayer')
class ConsciousLayer:
"""增强型意识层 - 负责高级认知处理和决策，集成意识流记录"""
def __init__(self,
preconscious: PreconsciousLayer,
memory_system: MemorySystem,
cognitive_processor: Optional[CognitiveProcessor] = None,
stream_system: Optional[ConsciousnessStream] = None):
"""
初始化意识层
参数:
preconscious: 前意识层实例
memory_system: 记忆系统实例
cognitive_processor: 认知处理器实例 (可选)
stream_system: 意识流系统实例 (可选)
"""
self.preconscious = preconscious
self.memory_system = memory_system
self.cognitive_processor = cognitive_processor or CognitiveProcessor(memory_system)
# 意识流系统
self.stream_system = stream_system or ConsciousnessStream()
logger.info(f"意识流系统初始化完成 (容量: {self.stream_system.max_size})")
# 意识状态管理
self.current_state = ConsciousState.RELAXED
self.state_history = deque(maxlen=50) # 状态历史记录
# 注意力管理
self.attentional_focus = [] # 当前注意力焦点
self.focus_history = deque(maxlen=100) # 注意力历史
# 自我意识
self.self_awareness = True
self.self_model = {} # 自我模型
# 元认知监控
self.metacognition = {
"monitoring_interval": 5.0, # 元认知监控间隔 (秒)
"last_monitor_time": time.time()
}
# 初始化工作负载管理器
self.workload_manager = self.cognitive_processor.workload_manager
def link_stream_system(self, stream_system: ConsciousnessStream):
"""连接外部意识流系统"""
self.stream_system = stream_system
logger.info(f"已连接外部意识流系统 (容量: {stream_system.max_size})")
def process(self, elevated_stimulus: Dict, biological_state: Optional[Dict] = None) -> Dict:
"""
处理提升到意识层的刺激 (统一接口方法)
参数:
elevated_stimulus: 提升的刺激字典
biological_state: 生物状态信息 (可选)
返回:
处理结果字典
"""
return self.process_input(elevated_stimulus, biological_state)
def process_input(self, elevated_stimulus: Dict, biological_state: Optional[Dict] = None) -> Dict:
"""
处理提升到意识层的刺激 (详细实现)
参数:
elevated_stimulus: 提升的刺激字典
biological_state: 生物状态信息 (可选)
返回:
处理结果字典
"""
start_time = time.time()
# 更新意识状态
self._update_state(elevated_stimulus, biological_state)
# 如果有生物状态，更新刺激显著性
stimulus = elevated_stimulus["original"]
if biological_state:
elevated_stimulus["salience"] = stimulus.calculate_salience(biological_state)
# 使用认知处理器处理刺激
processor_result = self.cognitive_processor.process_elevated_stimulus(
elevated_stimulus,
self.current_state,
biological_state
)
# 更新注意力焦点
self._update_attentional_focus(elevated_stimulus, processor_result)
# 强化相关记忆
self._reinforce_related_memories(elevated_stimulus)
# 更新意识状态（如果处理器建议）
self._update_state_from_processor(processor_result)
# 将刺激添加到工作记忆
self._add_to_working_memory(elevated_stimulus, processor_result)
# 执行元认知监控
self._metacognitive_monitoring()
# ===== 记录到意识流 =====
self._record_to_stream(elevated_stimulus, processor_result, biological_state)
# 构建返回结果
result = {
"state": self.current_state.name,
"attention": self.attentional_focus,
"self_awareness": self._check_self_awareness(),
"workload": self.workload_manager.get_workload_report(),
"working_memory": self.get_working_memory_state(),
"processing_time": round(time.time() - start_time, 4),
"metacognition": processor_result.get("metacognition", {}),
"stream_id": processor_result.get("stream_id", None) # 返回意识流ID
}
result.update(processor_result)
return result
def _record_to_stream(self, elevated_stimulus: Dict, processor_result: Dict,
biological_state: Optional[Dict] = None):
"""记录处理过程到意识流"""
if not self.stream_system:
return
stimulus = elevated_stimulus["original"]
content = f"处理刺激: {stimulus.content[:50]}{'...' if len(stimulus.content) > 50 else ''}"
# 提取情感信息
emotional_valence = stimulus.emotional_valence
emotion = "NEUTRAL"
if emotional_valence < -0.7:
emotion = "FEAR"
elif emotional_valence < -0.3:
emotion = "SADNESS"
elif emotional_valence > 0.7:
emotion = "JOY"
elif emotional_valence > 0.3:
emotion = "INTEREST"
# 添加意识流条目
stream_id = self.stream_system.add_entry(
event_type="stimulus_processing",
content=content,
cognitive_state=self.current_state.name,
emotional_valence=emotional_valence,
additional_data={
"stimulus_id": stimulus.id,
"source": stimulus.source,
"category": stimulus.category,
"priority": elevated_stimulus.get("priority", 0.5),
"biological_state": biological_state.copy() if biological_state else {},
"processor_result": processor_result
}
)
# 在处理器结果中返回流ID
processor_result["stream_id"] = stream_id
def deliberate_recall(self, keyword: str, context: Optional[Dict] = None, max_results: int = 5) -> List[str]:
"""
主动回忆关键词相关的内容
参数:
keyword: 回忆关键词
context: 回忆上下文 (可选)
max_results: 最大返回结果数
返回:
回忆结果列表
"""
recalled = []
# 从前意识缓冲区回忆
for item in self.preconscious.get_buffer_content():
if keyword in item["stimulus"].content:
recalled.append(f"前意识: {item['stimulus'].content[:50]}...")
# 从长期记忆中回忆
memory_context = context or {"keywords": [keyword]}
affective_state = {"emotion": {"current_emotion": "NEUTRAL"}}
related_memories = self.memory_system.retrieve(
memory_context,
affective_state,
max_results=max_results
)
for mem in related_memories:
desc = mem.get("content", {}).get("description", mem["id"])
recalled.append(f"记忆: {desc[:50]}...")
# 记录回忆活动
recall_task = {
"id": f"recall_{int(time.time())}",
"type": "memory_recall",
"keyword": keyword,
"results": len(recalled)
}
self.workload_manager.add_task(recall_task)
# 记录到意识流
if self.stream_system:
self.stream_system.add_entry(
event_type="deliberate_recall",
content=f"主动回忆: {keyword}",
cognitive_state=self.current_state.name,
emotional_valence=0.2, # 中性偏积极
additional_data={
"keyword": keyword,
"results_count": len(recalled),
"results": recalled[:3] # 只记录前3个结果
}
)
return recalled[:max_results]
def _update_state(self, stimulus: Dict, biological_state: Optional[Dict] = None):
"""
根据刺激更新意识状态
参数:
stimulus: 刺激字典
biological_state: 生物状态 (可选)
"""
emotional_valence = stimulus["original"].emotional_valence
# 考虑生物状态的影响
fatigue = biological_state.get("fatigue", 0.0) if biological_state else 0.0
arousal = biological_state.get("arousal_level", 0.5) if biological_state else 0.5
# 状态转换逻辑
if emotional_valence < -0.7:
new_state = ConsciousState.TRAUMATIC
elif "dream" in stimulus["original"].content or fatigue > 0.8:
new_state = ConsciousState.DREAMLIKE
elif emotional_valence > 0.5 or stimulus.get("priority", 0) > 0.8:
new_state = ConsciousState.FOCUSED
elif stimulus.get("priority", 0) > 0.5:
new_state = ConsciousState.EXPLORATORY
elif emotional_valence < -0.2:
new_state = ConsciousState.REFLECTIVE
elif arousal < 0.3 or fatigue > 0.6:
new_state = ConsciousState.RELAXED
else:
new_state = ConsciousState.RELAXED
# 记录状态变化
if new_state != self.current_state:
logger.info(f"意识状态变化: {self.current_state.name} -> {new_state.name}")
self.state_history.append({
"timestamp": time.time(),
"from": self.current_state.name,
"to": new_state.name,
"stimulus": stimulus["original"].content[:30] + "..."
})
self.current_state = new_state
# 记录到意识流
if self.stream_system:
self.stream_system.add_entry(
event_type="state_change",
content=f"状态变化: {self.current_state.name} → {new_state.name}",
cognitive_state=new_state.name,
emotional_valence=emotional_valence
)
def _update_attentional_focus(self, stimulus: Dict, processor_result: Dict):
"""
更新注意力焦点
参数:
stimulus: 刺激字典
processor_result: 处理器结果
"""
# 确定新的注意力焦点
new_focus = processor_result.get("attentional_focus", stimulus["associations"])
# 如果焦点变化，记录历史
if new_focus != self.attentional_focus:
self.focus_history.append({
"timestamp": time.time(),
"previous": self.attentional_focus,
"new": new_focus,
"reason": processor_result.get("attention_reason", "stimulus-driven")
})
# 更新当前焦点
self.attentional_focus = new_focus[:5] # 限制焦点数量
# 记录到意识流
if self.stream_system and new_focus != self.attentional_focus:
self.stream_system.add_entry(
event_type="attention_shift",
content=f"注意力转移: {', '.join(new_focus[:2])}...",
cognitive_state=self.current_state.name,
emotional_valence=0.0, # 中性
additional_data={
"previous_focus": self.attentional_focus,
"new_focus": new_focus
}
)
def _update_state_from_processor(self, processor_result: Dict):
"""
根据处理器结果更新状态
参数:
processor_result: 处理器结果字典
"""
if "new_state" in processor_result:
try:
new_state = ConsciousState[processor_result["new_state"]]
if new_state != self.current_state:
logger.info(f"处理器建议状态变化: {self.current_state.name} -> {new_state.name}")
self.current_state = new_state
# 记录到意识流
if self.stream_system:
self.stream_system.add_entry(
event_type="state_change",
content=f"处理器状态建议: {new_state.name}",
cognitive_state=new_state.name,
emotional_valence=0.5 # 积极
)
except KeyError:
logger.warning(f"未知意识状态: {processor_result['new_state']}")
def _reinforce_related_memories(self, elevated_stimulus: Dict):
"""
强化相关记忆
参数:
elevated_stimulus: 提升的刺激字典
"""
for mem in elevated_stimulus.get("related_memories", []):
self.memory_system.reinforce_memory(mem["id"], reinforcement=0.1)
def _add_to_working_memory(self, elevated_stimulus: Dict, processor_result: Dict):
"""
添加刺激到工作记忆
参数:
elevated_stimulus: 提升的刺激字典
processor_result: 处理器结果
"""
stimulus = elevated_stimulus["original"]
memory_item = {
"id": f"wm_{int(time.time())}",
"content": stimulus.content[:50] + "..." if len(stimulus.content) > 50 else stimulus.content,
"priority": elevated_stimulus.get("priority", 0.5),
"salience": elevated_stimulus.get("salience", 0.0),
"associations": elevated_stimulus["associations"],
"processing_result": processor_result.get("decision", {}),
"state": self.current_state.name,
"timestamp": time.time()
}
self.workload_manager.add_to_working_memory(memory_item, memory_item["priority"])
# 记录到意识流
if self.stream_system:
self.stream_system.add_entry(
event_type="working_memory",
content=f"工作记忆添加: {memory_item['content']}",
cognitive_state=self.current_state.name,
emotional_valence=0.3, # 轻微积极
additional_data={
"priority": memory_item["priority"],
"salience": memory_item["salience"]
}
)
def _metacognitive_monitoring(self):
"""执行元认知监控"""
current_time = time.time()
interval = self.metacognition["monitoring_interval"]
# 检查是否需要执行监控
if current_time - self.metacognition["last_monitor_time"] > interval:
logger.debug("执行元认知监控...")
# 记录监控开始
if self.stream_system:
self.stream_system.add_entry(
event_type="metacognition",
content="元认知监控开始",
cognitive_state=self.current_state.name,
emotional_valence=0.0
)
# 1. 检查认知负荷
workload = self.workload_manager.get_workload_report()
if workload["cognitive_load"] > 0.8:
logger.info("元认知: 高认知负荷检测，建议减少任务")
# 记录到意识流
if self.stream_system:
self.stream_system.add_entry(
event_type="metacognition",
content="高认知负荷，暂停低优先级任务",
cognitive_state=self.current_state.name,
emotional_valence=-0.3 # 轻微负面
)
# 自动减少任务
self.workload_manager.suspend_low_priority_tasks(threshold=0.4)
# 2. 检查注意力稳定性
if len(self.focus_history) > 3:
last_focus_changes = list(self.focus_history)[-3:]
change_count = sum(1 for change in last_focus_changes
if change["reason"] == "distraction")
if change_count > 2:
logger.info("元认知: 注意力分散检测，建议专注训练")
# 记录到意识流
if self.stream_system:
self.stream_system.add_entry(
event_type="metacognition",
content="注意力分散，建议专注训练",
cognitive_state=self.current_state.name,
emotional_valence=-0.2
)
# 3. 更新自我模型
self._update_self_model()
# 记录监控结束
if self.stream_system:
self.stream_system.add_entry(
event_type="metacognition",
content="元认知监控完成",
cognitive_state=self.current_state.name,
emotional_valence=0.1
)
# 更新最后监控时间
self.metacognition["last_monitor_time"] = current_time
def _update_self_model(self):
"""更新自我模型"""
# 基于工作记忆和状态历史更新自我模型
self.self_model = {
"preferred_states": self._calculate_preferred_states(),
"attention_patterns": self._analyze_attention_patterns(),
"memory_effectiveness": self._assess_memory_effectiveness(),
"last_updated": time.time()
}
# 记录到意识流
if self.stream_system:
self.stream_system.add_entry(
event_type="self_model_update",
content="自我模型更新",
cognitive_state=self.current_state.name,
emotional_valence=0.2,
additional_data={
"preferred_state": max(self.self_model["preferred_states"].items(),
key=lambda x: x[1])[0] if self.self_model["preferred_states"] else "N/A"
}
)
def _calculate_preferred_states(self) -> Dict:
"""计算偏好的意识状态"""
state_counts = {}
for record in self.state_history:
state = record["to"]
state_counts[state] = state_counts.get(state, 0) + 1
# 计算偏好分数
total = len(self.state_history)
if total == 0:
return {}
preferences = {state: count / total for state, count in state_counts.items()}
return dict(sorted(preferences.items(), key=lambda x: x[1], reverse=True))
def _analyze_attention_patterns(self) -> List[Dict]:
"""分析注意力模式"""
# 简化实现 - 实际中应使用更复杂的分析
recent_focus = list(self.focus_history)[-10:]
if not recent_focus:
return []
# 计算焦点变化频率
focus_changes = len(recent_focus)
avg_duration = (recent_focus[-1]["timestamp"] - recent_focus[0]["timestamp"]) / focus_changes
return [{
"focus_change_count": focus_changes,
"avg_duration": avg_duration,
"common_reasons": self._count_reasons(recent_focus)
}]
def _count_reasons(self, focus_records: List) -> Dict:
"""计算注意力变化原因"""
reasons = {}
for record in focus_records:
reason = record["reason"]
reasons[reason] = reasons.get(reason, 0) + 1
return reasons
def _assess_memory_effectiveness(self) -> float:
"""评估记忆有效性"""
# 简化实现 - 实际中应使用记忆系统数据
recall_tasks = [t for t in self.workload_manager.completed_tasks
if t.get("type") == "memory_recall"]
if not recall_tasks:
return 0.7 # 默认值
success_rates = [task["results"] / task.get("expected", 5) for task in recall_tasks]
return round(sum(success_rates) / len(success_rates), 2)
def get_working_memory_state(self, max_items: int = 5) -> List[Dict]:
"""
获取工作记忆状态
参数:
max_items: 最大返回项目数
返回:
工作记忆项目列表
"""
return self.workload_manager.get_working_memory(max_items)
def _check_self_awareness(self) -> Dict:
"""
检查自我意识状态
返回:
包含自我意识状态的字典
"""
workload_status = self.workload_manager.get_workload_report()
return {
"aware": self.self_awareness,
"current_focus": workload_status.get("current_focus", None) is not None,
"active_tasks": workload_status.get("tasks", {}).get("active", 0) > 0,
"self_model_valid": bool(self.self_model),
"metacognition_active": time.time() - self.metacognition["last_monitor_time"] < 60
}
def get_consciousness_report(self) -> Dict:
"""
获取意识层状态报告
返回:
包含意识层状态的字典
"""
return {
"state": self.current_state.name,
"state_history": list(self.state_history)[-5:],
"attention": {
"current_focus": self.attentional_focus,
"history": list(self.focus_history)[-5:]
},
"self_awareness": self._check_self_awareness(),
"self_model": self.self_model,
"workload": self.workload_manager.get_workload_report(),
"working_memory": self.get_working_memory_state(3),
"metacognition": {
"last_monitor": self.metacognition["last_monitor_time"],
"interval": self.metacognition["monitoring_interval"]
},
"stream_stats": self.stream_system.get_stats() if self.stream_system else None
}
def shutdown(self):
"""安全关闭意识层"""
# 保存当前状态
shutdown_data = {
"final_state": self.current_state.name,
"working_memory": self.get_working_memory_state(),
"self_model": self.self_model,
"timestamp": time.time(),
"stream_entries": self.stream_system.get_total_entries() if self.stream_system else 0
}
# 存储关闭状态到记忆系统
self.memory_system.store({
"content": shutdown_data,
"context": {"type": "system", "event": "shutdown"},
"emotional_valence": 0.0
})
# 记录关闭事件到意识流
if self.stream_system:
self.stream_system.add_entry(
event_type="shutdown",
content="意识层关闭",
cognitive_state=self.current_state.name,
emotional_valence=0.0
)
# 关闭组件
self.cognitive_processor = None
logger.info("意识层已安全关闭")
return shutdown_data
改成# layers/conscious.py
import logging
import time
from collections import deque, defaultdict
from typing import Dict, List, Optional, Any
from enum import Enum
from .preconscious import PreconsciousLayer
from .conscious_stream import ConsciousnessStream # 导入意识流系统
from ..states import ConsciousState
from ..memory import MemorySystem
from ..cognitive import CognitiveProcessor, WorkloadManager
from ..perception.stimulus import Stimulus
logger = logging.getLogger('ConsciousLayer')
class ConsciousLayer:
"""增强型意识层 - 负责高级认知处理和决策，集成意识流记录"""
def __init__(self,
preconscious: PreconsciousLayer,
memory_system: MemorySystem,
cognitive_processor: Optional[CognitiveProcessor] = None,
stream_system: Optional[ConsciousnessStream] = None):
"""
初始化意识层
参数:
preconscious: 前意识层实例
memory_system: 记忆系统实例
cognitive_processor: 认知处理器实例 (可选)
stream_system: 意识流系统实例 (可选)
"""
self.preconscious = preconscious
self.memory_system = memory_system
self.cognitive_processor = cognitive_processor or CognitiveProcessor(memory_system)
# 意识流系统
self.stream_system = stream_system or ConsciousnessStream()
logger.info(f"意识流系统初始化完成 (容量: {self.stream_system.max_size})")
# 意识状态管理
self.current_state = ConsciousState.RELAXED
self.state_history = deque(maxlen=50) # 状态历史记录
# 注意力管理
self.attentional_focus = [] # 当前注意力焦点
self.focus_history = deque(maxlen=100) # 注意力历史
# 自我意识
self.self_awareness = True
self.self_model = {} # 自我模型
# 元认知监控
self.metacognition = {
"monitoring_interval": 5.0, # 元认知监控间隔 (秒)
"last_monitor_time": time.time(),
"insights": []
}
# 初始化工作负载管理器
self.workload_manager = self.cognitive_processor.workload_manager
def link_stream_system(self, stream_system: ConsciousnessStream):
"""连接外部意识流系统"""
self.stream_system = stream_system
logger.info(f"已连接外部意识流系统 (容量: {stream_system.max_size})")
def process(self, elevated_stimulus: Dict, biological_state: Optional[Dict] = None) -> Dict:
"""
处理提升到意识层的刺激 (统一接口方法)
参数:
elevated_stimulus: 提升的刺激字典
biological_state: 生物状态信息 (可选)
返回:
处理结果字典
"""
return self.process_input(elevated_stimulus, biological_state)
def process_input(self, elevated_stimulus: Dict, biological_state: Optional[Dict] = None) -> Dict:
"""
处理提升到意识层的刺激 (详细实现)
参数:
elevated_stimulus: 提升的刺激字典
biological_state: 生物状态信息 (可选)
返回:
处理结果字典
"""
start_time = time.time()
# 记录处理开始
stream_id = self._record_to_stream("process_start",
"开始处理刺激",
elevated_stimulus,
biological_state)
# 更新意识状态
self._update_state(elevated_stimulus, biological_state)
# 如果有生物状态，更新刺激显著性
stimulus = elevated_stimulus["original"]
if biological_state:
elevated_stimulus["salience"] = stimulus.calculate_salience(biological_state)
# 使用认知处理器处理刺激
processor_result = self.cognitive_processor.process_elevated_stimulus(
elevated_stimulus,
self.current_state,
biological_state
)
# 更新注意力焦点
self._update_attentional_focus(elevated_stimulus, processor_result)
# 强化相关记忆
self._reinforce_related_memories(elevated_stimulus)
# 更新意识状态（如果处理器建议）
self._update_state_from_processor(processor_result)
# 将刺激添加到工作记忆
self._add_to_working_memory(elevated_stimulus, processor_result)
# 执行元认知监控
self._metacognitive_monitoring()
# 构建返回结果
result = {
"state": self.current_state.name,
"attention": self.attentional_focus,
"self_awareness": self._check_self_awareness(),
"workload": self.workload_manager.get_workload_report(),
"working_memory": self.get_working_memory_state(),
"processing_time": round(time.time() - start_time, 4),
"metacognition": processor_result.get("metacognition", {}),
"stream_id": stream_id,
"processor_result": processor_result
}
# 记录处理完成
self._record_to_stream("process_complete",
"刺激处理完成",
elevated_stimulus,
biological_state,
additional_data={"result": result})
return result
def _record_to_stream(self,
event_type: str,
content: str,
elevated_stimulus: Dict,
biological_state: Optional[Dict] = None,
additional_data: dict = None) -> str:
"""记录处理过程到意识流"""
if not self.stream_system:
return None
stimulus = elevated_stimulus["original"]
emotional_valence = stimulus.emotional_valence
# 创建附加数据
record_data = {
"stimulus_id": stimulus.id,
"source": stimulus.source,
"category": stimulus.category,
"priority": elevated_stimulus.get("priority", 0.5),
"salience": elevated_stimulus.get("salience", 0.0),
}
if biological_state:
record_data["biological_state"] = biological_state.copy()
if additional_data:
record_data.update(additional_data)
# 添加意识流条目
return self.stream_system.add_entry(
event_type=event_type,
content=content,
cognitive_state=self.current_state.name,
emotional_valence=emotional_valence,
additional_data=record_data
)
def deliberate_recall(self, keyword: str, context: Optional[Dict] = None, max_results: int = 5) -> List[str]:
"""
主动回忆关键词相关的内容
参数:
keyword: 回忆关键词
context: 回忆上下文 (可选)
max_results: 最大返回结果数
返回:
回忆结果列表
"""
# 记录回忆开始
recall_id = self._record_to_stream("recall_start",
f"开始回忆: {keyword}",
{"original": Stimulus(content=keyword, source="internal")})
recalled = []
# 从前意识缓冲区回忆
for item in self.preconscious.get_buffer_content():
if keyword in item["stimulus"].content:
recalled.append(f"前意识: {item['stimulus'].content[:50]}...")
# 从长期记忆中回忆
memory_context = context or {"keywords": [keyword]}
affective_state = {"emotion": {"current_emotion": "NEUTRAL"}}
related_memories = self.memory_system.retrieve(
memory_context,
affective_state,
max_results=max_results
)
for mem in related_memories:
desc = mem.get("content", {}).get("description", mem["id"])
recalled.append(f"记忆: {desc[:50]}...")
# 记录回忆活动
recall_task = {
"id": f"recall_{int(time.time())}",
"type": "memory_recall",
"keyword": keyword,
"results": len(recalled)
}
self.workload_manager.add_task(recall_task)
# 记录回忆结果
self._record_to_stream("recall_results",
f"回忆完成: {keyword} ({len(recalled)}结果)",
{"original": Stimulus(content=keyword, source="internal")},
additional_data={"results": recalled[:3]})
return recalled[:max_results]
def _update_state(self, stimulus: Dict, biological_state: Optional[Dict] = None):
"""
根据刺激更新意识状态
参数:
stimulus: 刺激字典
biological_state: 生物状态 (可选)
"""
emotional_valence = stimulus["original"].emotional_valence
# 考虑生物状态的影响
fatigue = biological_state.get("fatigue", 0.0) if biological_state else 0.0
arousal = biological_state.get("arousal_level", 0.5) if biological_state else 0.5
# 状态转换逻辑
if emotional_valence < -0.7:
new_state = ConsciousState.TRAUMATIC
elif "dream" in stimulus["original"].content or fatigue > 0.8:
new_state = ConsciousState.DREAMLIKE
elif emotional_valence > 0.5 or stimulus.get("priority", 0) > 0.8:
new_state = ConsciousState.FOCUSED
elif stimulus.get("priority", 0) > 0.5:
new_state = ConsciousState.EXPLORATORY
elif emotional_valence < -0.2:
new_state = ConsciousState.REFLECTIVE
elif arousal < 0.3 or fatigue > 0.6:
new_state = ConsciousState.RELAXED
else:
new_state = ConsciousState.RELAXED
# 记录状态变化
if new_state != self.current_state:
logger.info(f"意识状态变化: {self.current_state.name} -> {new_state.name}")
self.state_history.append({
"timestamp": time.time(),
"from": self.current_state.name,
"to": new_state.name,
"stimulus": stimulus["original"].content[:30] + "..."
})
self.current_state = new_state
# 记录状态变化到意识流
self._record_to_stream("state_change",
f"状态变化: {self.current_state.name} → {new_state.name}",
stimulus,
biological_state)
def _update_attentional_focus(self, stimulus: Dict, processor_result: Dict):
"""
更新注意力焦点
参数:
stimulus: 刺激字典
processor_result: 处理器结果
"""
# 确定新的注意力焦点
new_focus = processor_result.get("attentional_focus", stimulus["associations"])
# 如果焦点变化，记录历史
if new_focus != self.attentional_focus:
self.focus_history.append({
"timestamp": time.time(),
"previous": self.attentional_focus,
"new": new_focus,
"reason": processor_result.get("attention_reason", "stimulus-driven")
})
# 更新当前焦点
self.attentional_focus = new_focus[:5] # 限制焦点数量
# 记录注意力转移
if new_focus != self.attentional_focus:
self._record_to_stream("attention_shift",
f"注意力转移: {', '.join(new_focus[:2])}...",
stimulus)
def _update_state_from_processor(self, processor_result: Dict):
"""
根据处理器结果更新状态
参数:
processor_result: 处理器结果字典
"""
if "new_state" in processor_result:
try:
new_state = ConsciousState[processor_result["new_state"]]
if new_state != self.current_state:
logger.info(f"处理器建议状态变化: {self.current_state.name} -> {new_state.name}")
self.current_state = new_state
# 记录状态建议
self._record_to_stream("state_suggestion",
f"处理器状态建议: {new_state.name}",
{},
additional_data={"processor_result": processor_result})
except KeyError:
logger.warning(f"未知意识状态: {processor_result['new_state']}")
def _reinforce_related_memories(self, elevated_stimulus: Dict):
"""
强化相关记忆
参数:
elevated_stimulus: 提升的刺激字典
"""
for mem in elevated_stimulus.get("related_memories", []):
self.memory_system.reinforce_memory(mem["id"], reinforcement=0.1)
# 记录记忆强化
if elevated_stimulus.get("related_memories"):
self._record_to_stream("memory_reinforcement",
"强化相关记忆",
elevated_stimulus)
def _add_to_working_memory(self, elevated_stimulus: Dict, processor_result: Dict):
"""
添加刺激到工作记忆
参数:
elevated_stimulus: 提升的刺激字典
processor_result: 处理器结果
"""
stimulus = elevated_stimulus["original"]
memory_item = {
"id": f"wm_{int(time.time())}",
"content": stimulus.content[:50] + "..." if len(stimulus.content) > 50 else stimulus.content,
"priority": elevated_stimulus.get("priority", 0.5),
"salience": elevated_stimulus.get("salience", 0.0),
"associations": elevated_stimulus["associations"],
"processing_result": processor_result.get("decision", {}),
"state": self.current_state.name,
"timestamp": time.time()
}
self.workload_manager.add_to_working_memory(memory_item, memory_item["priority"])
# 记录工作记忆添加
self._record_to_stream("working_memory",
f"工作记忆添加: {memory_item['content']}",
elevated_stimulus,
additional_data=memory_item)
def _metacognitive_monitoring(self):
"""执行元认知监控"""
current_time = time.time()
interval = self.metacognition["monitoring_interval"]
# 检查是否需要执行监控
if current_time - self.metacognition["last_monitor_time"] > interval:
logger.debug("执行元认知监控...")
# 记录监控开始
monitor_id = self._record_to_stream("metacognition_start",
"元认知监控开始",
{})
# 1. 检查认知负荷
workload = self.workload_manager.get_workload_report()
if workload["cognitive_load"] > 0.8:
logger.info("元认知: 高认知负荷检测，建议减少任务")
# 自动减少任务
suspended = self.workload_manager.suspend_low_priority_tasks(threshold=0.4)
# 记录到意识流
self._record_to_stream("workload_adjustment",
f"高负荷暂停{suspended}个任务",
{},
additional_data={"workload": workload})
# 2. 检查注意力稳定性
if len(self.focus_history) > 3:
last_focus_changes = list(self.focus_history)[-3:]
change_count = sum(1 for change in last_focus_changes
if change["reason"] == "distraction")
if change_count > 2:
logger.info("元认知: 注意力分散检测，建议专注训练")
# 记录到意识流
self._record_to_stream("attention_warning",
"注意力分散警告",
{},
additional_data={"focus_history": last_focus_changes})
# 3. 更新自我模型
self._update_self_model()
# 生成元认知洞察
insight = self._generate_metacognitive_insight(workload)
self.metacognition["insights"].append(insight)
# 记录监控完成
self._record_to_stream("metacognition_complete",
"元认知监控完成",
{},
additional_data={"insight": insight})
# 更新最后监控时间
self.metacognition["last_monitor_time"] = current_time
def _generate_metacognitive_insight(self, workload: dict) -> str:
"""生成元认知洞察"""
wm_count = len(self.get_working_memory_state())
avg_load = workload.get("cognitive_load", 0.5)
return (
f"系统状态: {self.current_state.name}, "
f"工作记忆: {wm_count}项, "
f"认知负载: {avg_load:.2f}, "
f"注意力焦点: {', '.join(self.attentional_focus[:2]) or '无'}"
)
def _update_self_model(self):
"""更新自我模型"""
# 基于工作记忆和状态历史更新自我模型
self.self_model = {
"preferred_states": self._calculate_preferred_states(),
"attention_patterns": self._analyze_attention_patterns(),
"memory_effectiveness": self._assess_memory_effectiveness(),
"last_updated": time.time()
}
# 记录自我模型更新
self._record_to_stream("self_model_update",
"自我模型更新",
{},
additional_data=self.self_model)
def _calculate_preferred_states(self) -> Dict:
"""计算偏好的意识状态"""
state_counts = defaultdict(int)
for record in self.state_history:
state = record["to"]
state_counts[state] += 1
# 计算偏好分数
total = len(self.state_history)
if total == 0:
return {}
preferences = {state: count / total for state, count in state_counts.items()}
return dict(sorted(preferences.items(), key=lambda x: x[1], reverse=True))
def _analyze_attention_patterns(self) -> List[Dict]:
"""分析注意力模式"""
recent_focus = list(self.focus_history)[-10:]
if not recent_focus:
return []
# 计算焦点变化频率
focus_changes = len(recent_focus)
avg_duration = (recent_focus[-1]["timestamp"] - recent_focus[0]["timestamp"]) / focus_changes
return [{
"focus_change_count": focus_changes,
"avg_duration": avg_duration,
"common_reasons": self._count_reasons(recent_focus)
}]
def _count_reasons(self, focus_records: List) -> Dict:
"""计算注意力变化原因"""
reasons = defaultdict(int)
for record in focus_records:
reason = record["reason"]
reasons[reason] += 1
return dict(reasons)
def _assess_memory_effectiveness(self) -> float:
"""评估记忆有效性"""
recall_tasks = [t for t in self.workload_manager.completed_tasks
if t.get("type") == "memory_recall"]
if not recall_tasks:
return 0.7 # 默认值
success_rates = [task["results"] / task.get("expected", 5) for task in recall_tasks]
return round(sum(success_rates) / len(success_rates), 2)
def get_working_memory_state(self, max_items: int = 5) -> List[Dict]:
"""
获取工作记忆状态
参数:
max_items: 最大返回项目数
返回:
工作记忆项目列表
"""
return self.workload_manager.get_working_memory(max_items)
def _check_self_awareness(self) -> Dict:
"""
检查自我意识状态
返回:
包含自我意识状态的字典
"""
workload_status = self.workload_manager.get_workload_report()
return {
"aware": self.self_awareness,
"current_focus": workload_status.get("current_focus", None) is not None,
"active_tasks": workload_status.get("tasks", {}).get("active", 0) > 0,
"self_model_valid": bool(self.self_model),
"metacognition_active": time.time() - self.metacognition["last_monitor_time"] < 60
}
def get_consciousness_report(self) -> Dict:
"""
获取意识层状态报告
返回:
包含意识层状态的字典
"""
return {
"state": self.current_state.name,
"state_history": list(self.state_history)[-5:],
"attention": {
"current_focus": self.attentional_focus,
"history": list(self.focus_history)[-5:]
},
"self_awareness": self._check_self_awareness(),
"self_model": self.self_model,
"workload": self.workload_manager.get_workload_report(),
"working_memory": self.get_working_memory_state(3),
"metacognition": {
"last_monitor": self.metacognition["last_monitor_time"],
"interval": self.metacognition["monitoring_interval"],
"insights": self.metacognition["insights"][-3:]
},
"stream_stats": self.stream_system.get_stats() if self.stream_system else None
}
def shutdown(self):
"""安全关闭意识层"""
# 保存当前状态
shutdown_data = {
"final_state": self.current_state.name,
"working_memory": self.get_working_memory_state(),
"self_model": self.self_model,
"timestamp": time.time(),
"stream_entries": self.stream_system.get_total_entries() if self.stream_system else 0
}
# 存储关闭状态到记忆系统
self.memory_system.store({
"content": shutdown_data,
"context": {"type": "system", "event": "shutdown"},
"emotional_valence": 0.0
})
# 记录关闭事件到意识流
if self.stream_system:
self.stream_system.add_entry(
event_type="shutdown",
content="意识层关闭",
cognitive_state=self.current_state.name,
emotional_valence=0.0,
additional_data=shutdown_data
)
# 关闭组件
self.cognitive_processor = None
logger.info("意识层已安全关闭")
return shutdown_data
对吗？
本文提出MetaRAG，一种结合元认知的检索增强大型语言模型，提高多跳推理的准确性。通过元认知过程，模型能自我评估和改进其响应策略，实验证明MetaRAG在多跳QA数据集上优于现有方法。
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