386 lines
14 KiB
Bash
Executable File
386 lines
14 KiB
Bash
Executable File
#!/bin/bash
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# ============================================================================
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# 混合学习控制器 - 智能主题选择版 v4.0
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# ============================================================================
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# 功能：调用标准学习模块learn-core.sh完成学习任务
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# 特点：模块化、易维护、流程统一、智能主题选择
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# ============================================================================
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# 配置
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LEARNING_DIR="/root/.openclaw/workspace/sleep-learning"
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LEARNING_LOG="${LEARNING_DIR}/learning.log"
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PROBLEM_LOG="/var/log/openclaw/problems.log"
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KNOWLEDGE_GRAPH="/root/.openclaw/workspace/知识图谱.md"
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TOPIC_STATE="/root/.openclaw/.hybrid-learning-topic-state"
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LOG_RETENTION_DAYS=15
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DEEP_THINKING_ROUNDS=3
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mkdir -p "$(dirname "$PROBLEM_LOG")" "$LEARNING_DIR"
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# -------------------- 加载标准学习模块 --------------------
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source /root/.openclaw/scripts/learn-core.sh
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# ============================================================================
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# 日志功能
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# ============================================================================
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log_start() {
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local task="$1"
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local robot="$2"
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echo "========================================" >> "$LEARNING_LOG"
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echo "[开始] $(date '+%Y-%m-%d %H:%M:%S') | $robot | $task" >> "$LEARNING_LOG"
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echo "[开始] $task"
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}
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log_progress() {
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local message="$1"
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echo "[进度] $(date '+%H:%M:%S') | $message" >> "$LEARNING_LOG"
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echo " → $message"
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}
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log_deep() {
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local round="$1"
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local message="$2"
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echo "[深度-$round] $(date '+%H:%M:%S') | $message" >> "$LEARNING_LOG"
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echo " 🔹 第${round}轮深度思考: $message"
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}
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log_end() {
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local status="$1"
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local duration="$2"
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echo "[结束] $(date '+%Y-%m-%d %H:%M:%S') | 状态: $status | 耗时: ${duration}秒" >> "$LEARNING_LOG"
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echo "[结束] 完成 (${duration}秒)"
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}
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log_topic() {
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echo "[主题] $1" >&2 # 全部输出到stderr
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}
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# ============================================================================
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# 清理旧日志
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# ============================================================================
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clean_old_logs() {
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local count=$(find "$LEARNING_DIR" -name "*.log" -mtime +$LOG_RETENTION_DAYS 2>/dev/null | wc -l)
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if [ "$count" -gt 0 ]; then
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find "$LEARNING_DIR" -name "*.log" -mtime +$LOG_RETENTION_DAYS -delete
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log_progress "已删除 ${count} 个过期日志文件"
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fi
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}
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# ============================================================================
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# 7大品类定义 + Fallback主题
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# ============================================================================
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declare -A FALLBACK_TOPICS
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FALLBACK_TOPICS["写作助手"]="Prompt工程、多模态提示词、AI辅助写作技巧"
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FALLBACK_TOPICS["贵金属交易"]="黄金价格影响因素、白银投资策略、铂金市场分析"
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FALLBACK_TOPICS["期货交易"]="期货合约交割、套期保值策略、商品期货波动"
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FALLBACK_TOPICS["合约交易"]="永续合约机制、资金费率计算、杠杆风险管理"
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FALLBACK_TOPICS["虚拟货币"]="区块链技术原理、DeFi协议、NFT应用场景"
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FALLBACK_TOPICS["网络自动化"]="Selenium自动化、API接口对接、数据抓取技巧"
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FALLBACK_TOPICS["AI能力提升"]="大模型微调、RAG检索增强、AI Agent设计"
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# ============================================================================
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# 获取当前品类索引（轮询）
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# ============================================================================
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get_category_index() {
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local current=0
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if [ -f "$TOPIC_STATE" ]; then
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current=$(cat "$TOPIC_STATE")
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fi
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local next=$(( (current + 1) % 7 ))
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echo "$next" > "$TOPIC_STATE"
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echo "$next"
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}
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# ============================================================================
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# 获取品类名称
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# ============================================================================
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get_category_name() {
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local index=$1
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local categories=(
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"写作助手"
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"贵金属交易"
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"期货交易"
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"合约交易"
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"虚拟货币"
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"网络自动化"
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"AI能力提升"
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)
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echo "${categories[$index]}"
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}
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# ============================================================================
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# AI动态生成主题
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# ============================================================================
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generate_topic_with_ai() {
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local category="$1"
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log_topic "AI生成主题中: $category" >&2 # 输出到stderr，不干扰stdout
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# 调用AI生成具体学习主题
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local ai_result
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ai_result=$(python3 /root/.openclaw/scripts/ocw_api.py chat "作为AI学习助手，请为${category}生成一个具体的、有价值的学习主题。长度不超过15字。直接输出主题。" 2>/dev/null)
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# 清理输出
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echo "$ai_result" | tr -d '[]【】'
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}
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# ============================================================================
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# 选择学习主题（AI生成 + Fallback）
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# ============================================================================
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select_topic() {
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local category="$1"
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local topic=""
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local source="AI"
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# 尝试AI生成
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local ai_topic
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ai_topic=$(generate_topic_with_ai "$category" 2>/dev/null)
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# 清理AI结果
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ai_topic=$(echo "$ai_topic" | grep -vE "\[主题\]|品类|生成" | head -1)
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# 验证AI结果
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if [ -n "$ai_topic" ] && [ ${#ai_topic} -lt 30 ] && [ ${#ai_topic} -gt 2 ]; then
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topic="$ai_topic"
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else
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# Fallback到预设主题
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local fallback="${FALLBACK_TOPICS[$category]}"
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local fallback_topic=$(echo "$fallback" | cut -d',' -f1 | tr -d ' ')
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topic="$fallback_topic"
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source="Fallback"
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fi
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# 输出主题和来源
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echo "${source}|${topic}"
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}
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# ============================================================================
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# 获取主题（带日志）
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# ============================================================================
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get_topic_with_log() {
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local category="$1"
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local result
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result=$(select_topic "$category")
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local source=$(echo "$result" | cut -d'|' -f1)
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local topic=$(echo "$result" | cut -d'|' -f2)
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echo "[主题] ${source}: $topic" >&2
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echo "$topic"
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}
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# ============================================================================
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# AI根据发展方向生成学习主题
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# ============================================================================
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# 发展方向主题池（API不可用时的备选）
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DIRECTION_TOPICS=(
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"AI Agent设计模式"
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"大模型RAG检索增强"
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"Prompt工程最佳实践"
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"多模态AI应用开发"
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"AI Agent工作流自动化"
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"大模型微调技术"
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"AI Agent记忆机制"
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"Function Calling实战"
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)
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# ============================================================================
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# AI根据发展方向生成学习主题
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# ============================================================================
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generate_topic_by_direction() {
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local robot="$1"
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log_progress "根据发展方向生成学习主题..."
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# 尝试调用API，失败则使用主题池
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local topic=""
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# 调用AI根据发展方向生成学习主题
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local ai_result
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ai_result=$(python3 /root/.openclaw/scripts/ocw_api.py chat "作为${robot}的AI学习助手，请根据当前技术发展趋势生成一个有价值的学习主题。长度不超过20字。直接输出主题。" 2>/dev/null)
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# 清理输出
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topic=$(echo "$ai_result" | tr -d '[]【】' | grep -vE "<think>|</think>|主题|生成|发展|趋势" | head -1)
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# 验证结果
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if [ -z "$topic" ] || [ ${#topic} -lt 2 ]; then
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# 使用主题池轮询
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local index=0
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if [ -f "$TOPIC_STATE" ]; then
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index=$(cat "$TOPIC_STATE")
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fi
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topic="${DIRECTION_TOPICS[$((index % ${#DIRECTION_TOPICS[@]}))]}"
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echo $((index + 1)) > "$TOPIC_STATE"
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fi
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echo "[主题] AI发展: $topic" >&2
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echo "$topic"
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}
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# ============================================================================
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# 模式A：自动学习（调用learn-core）
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# ============================================================================
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mode_a_deep_learning() {
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local robot="$1"
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local cycle=$2
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local use_direction="${3:-false}"
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log_progress "模式A：自动学习"
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local topic
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if [ "$use_direction" = "true" ]; then
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# 根据发展方向生成主题（不输出调试信息）
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topic=$(generate_topic_by_direction "$robot" 2>/dev/null)
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else
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# 选择品类（轮询）
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local category_index
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category_index=$(get_category_index)
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local category
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category=$(get_category_name "$category_index")
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log_topic "品类: $category"
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# 选择具体主题
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topic=$(get_topic_with_log "$category")
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fi
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# 调用学习模块
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learn_topic "$topic" "auto"
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log_progress "自动学习完成"
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}
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# ============================================================================
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# 模式B：问题驱动学习（调用learn-core）
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# ============================================================================
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mode_b_deep_learning() {
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local robot="$1"
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local problem="$2"
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log_progress "模式B：问题驱动学习"
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# 使用learn-core进行问题分析
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log_progress "问题: $problem"
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# 调用AI分析问题
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python3 /root/.openclaw/scripts/ocw_api.py chat "作为${robot}，请深度分析以下问题：${problem}。请从以下维度分析（每点不超过30字）：1. 问题本质 2. 根本原因 3. 解决思路。直接输出分析结果。" 2>/dev/null
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log_progress "问题分析完成"
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}
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# ============================================================================
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# 问题检测
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# ============================================================================
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detect_problems() {
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log_progress "检测最近问题"
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if [ -f "/root/.openclaw/scripts/monitor-error.log" ]; then
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local recent_errors=$(tail -10 "/root/.openclaw/scripts/monitor-error.log" 2>/dev/null)
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if [ -n "$recent_errors" ]; then
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log_progress "发现系统错误"
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fi
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fi
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echo "[$(date '+%Y-%m-%d %H:%M:%S')] 问题检测完成" >> "$PROBLEM_LOG"
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}
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# ============================================================================
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# 整合到知识体系
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# ============================================================================
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integrate_knowledge() {
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local robot="$1"
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local type="$2"
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log_progress "整合到知识体系"
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if [ -f "$KNOWLEDGE_GRAPH" ]; then
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echo "- [$(date '+%Y-%m-%d %H:%M:%S')] ${robot} 完成${type}学习" >> "${LEARNING_DIR}/knowledge-updates.log"
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fi
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log_progress "知识整合完成"
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}
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# ============================================================================
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# 主程序
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# ============================================================================
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main() {
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local mode="${1:-hybrid}"
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local robot="${2:-青龙}"
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local problem="${3:-}"
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local use_direction="${4:-false}" # 是否按发展方向生成
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local start_time=$(date +%s)
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echo "╔════════════════════════════════════════════════════╗"
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echo "║ 混合学习控制器 v4.1 (发展方向版) ║"
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echo "╚════════════════════════════════════════════════════╝"
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echo ""
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echo "机器人：$robot"
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echo "模式：$mode"
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echo "学习模块：learn-core.sh"
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if [ "$use_direction" = "true" ]; then
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echo "主题选择：AI根据发展方向生成"
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else
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echo "主题选择：7大品类轮询 + AI生成"
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fi
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echo ""
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clean_old_logs
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case "$mode" in
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hybrid)
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log_start "混合学习" "$robot"
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detect_problems
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if [ -n "$problem" ]; then
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mode_b_deep_learning "$robot" "$problem"
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fi
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mode_a_deep_learning "$robot" 1 "$use_direction"
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integrate_knowledge "$robot" "hybrid"
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;;
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auto)
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log_start "自动学习" "$robot"
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mode_a_deep_learning "$robot" 1 "$use_direction"
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integrate_knowledge "$robot" "auto"
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;;
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problem)
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if [ -z "$problem" ]; then
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echo "❌ 模式B需要提供问题参数"
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exit 1
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fi
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log_start "问题驱动学习" "$robot"
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mode_b_deep_learning "$robot" "$problem"
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integrate_knowledge "$robot" "problem"
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;;
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direction)
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# 纯发展方向模式
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log_start "发展方向学习" "$robot"
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topic=$(generate_topic_by_direction "$robot")
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learn_topic "$topic" "auto"
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integrate_knowledge "$robot" "direction"
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;;
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*)
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echo "用法：$0 {hybrid|auto|problem|direction} [机器人] [问题] [direction]"
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echo ""
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echo "示例："
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echo " $0 hybrid 2号机 # 混合学习（品类轮询）"
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echo " $0 auto 2号机 # 自动学习（品类轮询）"
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echo " $0 direction 2号机 # 按发展方向生成学习主题"
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echo " $0 hybrid 2号机 '' true # 混合学习+发展方向模式"
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echo " $0 problem 2号机 '问题X' # 问题驱动学习"
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;;
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esac
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local end_time=$(date +%s)
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local duration=$((end_time - start_time))
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log_end "ok" "$duration"
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echo ""
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echo "📊 日志: $LEARNING_LOG"
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}
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# 执行主程序
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main "$@"
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