新增技能: studytime-analysis — 角色学习时间分析（一周分布+跨周趋势+寒暑假排除）
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@ -7,3 +7,4 @@ lark_wiki_operate_as_bot 2a37701f568849f03eb46dd938baeda171380fe252b698ac8bda69c
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vala_git_workspace_backup 4cf352bec88fe84af065ba1ffcbb06647b77df0e01860faaf0bca9fd64b968ec
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cron-schedule b1879fa59d60e3d99cea1138674f7abac84a4aecd32743b801d41bfd6ed7181d
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study-analysis 33217dc132073ecd47b921800834f6df89494da9e7708fa90f15b3de7742e37f
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studytime-analysis f83392be24766898c5cfc206cc0edaf4f44b81ed0cb353c3f31157154964a7c2
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52
skills/studytime-analysis/SKILL.md
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52
skills/studytime-analysis/SKILL.md
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---
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name: studytime-analysis
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description: 分析指定角色的完课时间分布与学习规律，包含一周内时间分布、跨周趋势、寒暑假排除。触发词：「学习时间分析」「看看学习时间」「学习时间有什么特点」「studytime」。
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slug: studytime-analysis
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version: 1.0.0
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---
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# 学习时间分析技能
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## 触发规则
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用户通过飞书发送以下格式的消息时触发：
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- 「学习时间分析 [角色ID]」
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- 「[角色ID] 学习时间分析」
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- 「看看 [角色ID] 的学习时间有什么特点」
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- 「分析 [角色ID] 的完课时间规律」
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角色ID 必须是纯数字。
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## 工作流程
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1. **参数提取** — 从用户消息中提取角色ID
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2. **数据采集** — 执行 Python 脚本查询该角色全部完课记录（自动排除寒假1-2月、暑假7-8月）
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3. **分析输出** — 脚本自动完成以下分析并输出格式化报告：
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- 一周分布：周一至周日各天完课数量 + 周一至周五上课时段（上午/中午/晚上）
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- 跨周趋势：覆盖总周数、周均完课数、连续性、断档、趋势变化
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- 完课记录明细表格
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## 调用方式
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```bash
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cd /root/.openclaw/workspace-xiaoban && \
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export PG_DB_HOST=bj-postgres-16pob4sg.sql.tencentcdb.com \
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PG_DB_PORT=28591 PG_DB_USER=ai_member \
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PG_DB_PASSWORD='...' PG_DB_DATABASE=vala && \
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python3 skills/studytime-analysis/scripts/studytime_analysis.py <role_id>
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```
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## 数据库说明
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- **数据源**: PostgreSQL Online (`vala` 库)
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- **核心表**: `user_chapter_play_record_0~7`（完课记录，8张分表）
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- **筛选**: `play_status = 1`（已完成），`EXTRACT(MONTH FROM updated_at) NOT IN (1,2,7,8)`（排除寒假1-2月、暑假7-8月）
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- **注意**: 表名无 `bi_` 前缀；课程通过 `level` + `chapter_id` 字段展示
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## 输出说明
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脚本输出为 Markdown 格式的分析报告，可直接发送给用户。包含：
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- 基本信息（角色ID、有效完课总数、排除的寒暑假记录数）
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- 一周时间分布表 + 规律总结
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- 跨周学习趋势表 + 趋势分析
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- 完课记录明细表（日期、时间、星期、时段、课程、课时）
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443
skills/studytime-analysis/scripts/studytime_analysis.py
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skills/studytime-analysis/scripts/studytime_analysis.py
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#!/usr/bin/env python3
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"""
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studytime-analysis — 角色学习时间分析工具
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用法: python3 studytime_analysis.py <role_id>
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输出: Markdown 格式的分析报告
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数据源: PostgreSQL Online (vala 库)
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核心表: user_chapter_play_record_0~7
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"""
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import os
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import sys
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import psycopg2
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import psycopg2.extras
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from datetime import datetime, timedelta
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from collections import defaultdict, OrderedDict
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# ── 配置 ──────────────────────────────────────────────
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PG_CONFIG = {
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"host": os.environ.get("PG_DB_HOST", "bj-postgres-16pob4sg.sql.tencentcdb.com"),
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"port": int(os.environ.get("PG_DB_PORT", "28591")),
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"user": os.environ.get("PG_DB_USER", "ai_member"),
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"password": os.environ.get("PG_DB_PASSWORD", ""),
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"dbname": os.environ.get("PG_DB_DATABASE", "vala"),
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}
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EXCLUDED_MONTHS = (1, 2, 7, 8) # 寒假1-2月, 暑假7-8月
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WEEKDAY_NAMES = ["周一", "周二", "周三", "周四", "周五", "周六", "周日"]
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PERIODS = OrderedDict([
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("凌晨", (0, 6)),
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("上午", (6, 12)),
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("中午", (12, 14)),
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("下午", (14, 18)),
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("晚上", (18, 24)),
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])
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# ── 数据库查询 ────────────────────────────────────────
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def get_connection():
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"""连接 PostgreSQL"""
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conn = psycopg2.connect(
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host=PG_CONFIG["host"],
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port=PG_CONFIG["port"],
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user=PG_CONFIG["user"],
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password=PG_CONFIG["password"],
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dbname=PG_CONFIG["dbname"],
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)
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return conn
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def fetch_completion_records(role_id):
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"""查询指定角色全部完课记录（排除寒暑假）"""
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params = {}
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union_parts = []
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for i in range(8):
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param_name = f"rid_{i}"
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params[param_name] = role_id
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union_parts.append(f"""
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SELECT user_id, chapter_id, chapter_unique_id, level, updated_at
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FROM user_chapter_play_record_{i}
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WHERE user_id = %({param_name})s
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AND play_status = 1
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AND EXTRACT(MONTH FROM updated_at) NOT IN (1, 2, 7, 8)
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""")
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union_sql = " UNION ALL ".join(union_parts)
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sql = f"""
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SELECT * FROM (
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{union_sql}
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) t
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ORDER BY updated_at ASC
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"""
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conn = get_connection()
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try:
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with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur:
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cur.execute(sql, params)
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rows = cur.fetchall()
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finally:
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conn.close()
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return rows
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def count_excluded_records(role_id):
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"""统计被寒暑假排除的记录数"""
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params = {}
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union_parts = []
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for i in range(8):
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param_name = f"rid_{i}"
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params[param_name] = role_id
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union_parts.append(f"""
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SELECT COUNT(*) as cnt
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FROM user_chapter_play_record_{i}
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WHERE user_id = %({param_name})s
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AND play_status = 1
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AND EXTRACT(MONTH FROM updated_at) IN (1, 2, 7, 8)
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""")
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union_sql = " UNION ALL ".join(union_parts)
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sql = f"SELECT SUM(cnt) as total FROM ({union_sql}) t"
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conn = get_connection()
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try:
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with conn.cursor() as cur:
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cur.execute(sql, params)
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result = cur.fetchone()
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finally:
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conn.close()
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return result[0] if result and result[0] else 0
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# ── 分析函数 ──────────────────────────────────────────
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def classify_period(hour):
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"""根据小时数返回时段名称"""
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for name, (lo, hi) in PERIODS.items():
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if lo <= hour < hi:
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return name
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return "未知"
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def analyze_weekly_distribution(records):
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"""
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分析一周内分布: 周一至周日各天完课数 + 周一至周五时段分布
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返回: (day_counts, weekday_periods)
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"""
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day_counts = defaultdict(int)
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weekday_periods = defaultdict(lambda: defaultdict(int))
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today = datetime.now().date()
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for r in records:
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dt = r["updated_at"]
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if dt is None:
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continue
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# dt is timezone-aware, convert to local naive for analysis
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if hasattr(dt, 'tzinfo') and dt.tzinfo is not None:
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# PostgreSQL returns tz-aware, but we just need local time
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pass
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weekday = dt.weekday() # 0=Mon
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hour = dt.hour
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period = classify_period(hour)
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day_counts[weekday] += 1
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if weekday < 5:
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weekday_periods[period][weekday] += 1
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return day_counts, weekday_periods
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def analyze_weekly_trend(records):
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"""
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按周统计完课趋势
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返回: (weeks_data, analysis_dict)
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"""
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if not records:
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return [], {}
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week_counts = defaultdict(int)
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for r in records:
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dt = r["updated_at"]
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if dt is None:
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continue
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iso = dt.isocalendar()
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year, week_num = iso[0], iso[1]
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week_counts[(year, week_num)] += 1
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sorted_weeks = sorted(week_counts.keys())
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weeks_data = [(y, w, week_counts[(y, w)]) for y, w in sorted_weeks]
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total_weeks = len(weeks_data)
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total_lessons = sum(c for _, _, c in weeks_data)
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avg_per_week = round(total_lessons / total_weeks, 1) if total_weeks > 0 else 0
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# 时间跨度（含空周）
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if sorted_weeks:
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first = datetime.fromisocalendar(sorted_weeks[0][0], sorted_weeks[0][1], 1)
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last = datetime.fromisocalendar(sorted_weeks[-1][0], sorted_weeks[-1][1], 1)
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total_span_weeks = ((last - first).days // 7) + 1
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all_weeks_in_span = set()
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cur = first
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while cur <= last:
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iso = cur.isocalendar()
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all_weeks_in_span.add((iso[0], iso[1]))
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cur += timedelta(days=7)
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active_weeks = set(sorted_weeks)
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empty_weeks = sorted(all_weeks_in_span - active_weeks)
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else:
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total_span_weeks = 0
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empty_weeks = []
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consecutive = (len(empty_weeks) == 0)
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# 趋势: 前半段 vs 后半段
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mid = len(weeks_data) // 2
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first_half_data = weeks_data[:mid]
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first_half_avg = sum(c for _, _, c in first_half_data) / mid if mid > 0 else 0
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second_half_start = mid if len(weeks_data) % 2 == 0 else mid + 1
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second_half_data = weeks_data[second_half_start:]
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second_half_avg = sum(c for _, _, c in second_half_data) / len(second_half_data) if second_half_data else 0
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trend = "持平"
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if first_half_avg > 0:
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ratio = second_half_avg / first_half_avg
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if ratio > 1.15:
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trend = "上涨 ↑"
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elif ratio < 0.85:
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trend = "下降 ↓"
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return weeks_data, {
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"total_weeks": total_weeks,
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"total_span_weeks": total_span_weeks,
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"total_lessons": total_lessons,
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"avg_per_week": avg_per_week,
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"consecutive": consecutive,
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"empty_weeks": empty_weeks,
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"first_half_avg": round(first_half_avg, 1),
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"second_half_avg": round(second_half_avg, 1),
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"trend": trend,
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}
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# ── 输出格式化 ────────────────────────────────────────
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def format_report(role_id, records, excluded_count, day_counts, weekday_periods, weeks_data, analysis):
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"""生成 Markdown 格式分析报告"""
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lines = []
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now_str = datetime.now().strftime('%Y-%m-%d %H:%M')
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lines.append(f"# 📊 学习时间分析报告 — 角色 {role_id}")
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lines.append(f"")
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lines.append(f"**分析时间**: {now_str}")
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lines.append(f"**有效完课记录**: {len(records)} 条")
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if excluded_count > 0:
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lines.append(f"**已排除寒暑假记录**: {excluded_count} 条（寒假1-2月、暑假7-8月，不算入分析）")
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lines.append(f"")
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if not records:
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lines.append("> ⚠️ 该角色没有非寒暑假期间的完课记录，无法进行分析。")
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return "\n".join(lines)
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# ═══ 一、一周时间分布 ═══
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lines.append(f"---")
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lines.append(f"## 一、一周时间分布")
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lines.append(f"")
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# 日分布表
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lines.append(f"### 各天完课数量")
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lines.append(f"")
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total = sum(day_counts.values())
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max_day = max(day_counts.values()) if day_counts else 1
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lines.append(f"| 星期 | 完课数 | 占比 |")
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lines.append(f"|------|--------|------|")
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for i, name in enumerate(WEEKDAY_NAMES):
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cnt = day_counts.get(i, 0)
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pct = f"{cnt / total * 100:.1f}%" if total > 0 else "0%"
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bar = "█" * max(1, int(cnt / max_day * 20)) if cnt > 0 else ""
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lines.append(f"| {name} | {cnt} {bar} | {pct} |")
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lines.append(f"")
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# 规律小结
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weekday_total = sum(day_counts.get(i, 0) for i in range(5))
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weekend_total = sum(day_counts.get(i, 0) for i in range(5, 7))
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lines.append(f"### 规律小结")
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lines.append(f"")
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if weekend_total > 0:
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sat = day_counts.get(5, 0)
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sun = day_counts.get(6, 0)
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lines.append(f"- **周末上课**: ✅ 是 — 周六 {sat} 节，周日 {sun} 节")
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else:
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lines.append(f"- **周末上课**: ❌ 否 — 周末无完课记录")
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# 时段分布（周一至周五）
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lines.append(f"")
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lines.append(f"### 周一至周五上课时段分布")
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lines.append(f"")
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lines.append(f"| 时段 | 周一 | 周二 | 周三 | 周四 | 周五 | 合计 |")
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lines.append(f"|------|------|------|------|------|------|------|")
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for period in ["上午", "中午", "下午", "晚上", "凌晨"]:
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period_data = weekday_periods.get(period, {})
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period_total = sum(period_data.values())
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if period_total == 0:
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continue
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row = [period]
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for d in range(5):
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cnt = period_data.get(d, 0)
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row.append(str(cnt) if cnt > 0 else "-")
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row.append(str(period_total))
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lines.append(f"| {' | '.join(row)} |")
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lines.append(f"")
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# 时段规律
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lines.append(f"**时段规律分析**:")
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for period in ["上午", "中午", "下午", "晚上"]:
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period_data = weekday_periods.get(period, {})
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period_sum = sum(period_data.values())
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if period_sum == 0:
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continue
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pct = period_sum / weekday_total * 100 if weekday_total > 0 else 0
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active_days = [WEEKDAY_NAMES[d] for d in range(5) if period_data.get(d, 0) > 0]
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if active_days:
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lines.append(f"- **{period}**（{period_sum}节, {pct:.0f}%）→ 集中在 {'、'.join(active_days)}")
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else:
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lines.append(f"- **{period}**（{period_sum}节, {pct:.0f}%）")
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lines.append(f"")
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# ═══ 二、跨周学习趋势 ═══
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lines.append(f"---")
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lines.append(f"## 二、跨周学习趋势")
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lines.append(f"")
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lines.append(f"### 基本数据")
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lines.append(f"- 完课跨越 **{analysis['total_span_weeks']}** 个自然周（含空周），有课周数 **{analysis['total_weeks']}** 周")
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lines.append(f"- 有效完课总数 **{analysis['total_lessons']}** 节")
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lines.append(f"- 平均每周完课 **{analysis['avg_per_week']}** 节")
|
||||
lines.append(f"- 连续性: {'✅ 每周连续上课，无中断' if analysis['consecutive'] else '⚠️ 存在中断周（见下方）'}")
|
||||
lines.append(f"")
|
||||
|
||||
if analysis["empty_weeks"]:
|
||||
lines.append(f"### 中断周明细")
|
||||
empty_list = []
|
||||
for y, w in sorted(analysis["empty_weeks"]):
|
||||
monday = datetime.fromisocalendar(y, w, 1)
|
||||
empty_list.append(f"{y}年W{w:02d}（{monday.strftime('%m/%d')}起）")
|
||||
lines.append(f"- {', '.join(empty_list)}")
|
||||
lines.append(f"")
|
||||
|
||||
lines.append(f"### 各周完课详情")
|
||||
lines.append(f"")
|
||||
lines.append(f"| 周次 | 起止日期 | 完课数 | 趋势 |")
|
||||
lines.append(f"|------|----------|--------|------|")
|
||||
|
||||
max_count = max(c for _, _, c in weeks_data) if weeks_data else 1
|
||||
for i, (y, w, cnt) in enumerate(weeks_data):
|
||||
monday = datetime.fromisocalendar(y, w, 1)
|
||||
sunday = monday + timedelta(days=6)
|
||||
date_range = f"{monday.strftime('%m/%d')}-{sunday.strftime('%m/%d')}"
|
||||
|
||||
marker = ""
|
||||
if i > 0:
|
||||
prev_cnt = weeks_data[i - 1][2]
|
||||
if prev_cnt > 0 and cnt >= prev_cnt * 2:
|
||||
marker = "📈 突增"
|
||||
elif cnt > prev_cnt * 1.3:
|
||||
marker = "📈"
|
||||
elif prev_cnt > 0 and cnt < prev_cnt * 0.7:
|
||||
marker = "📉"
|
||||
|
||||
bar_len = max(1, int(cnt / max_count * 15)) if cnt > 0 else 0
|
||||
bar = "█" * bar_len if bar_len > 0 else ""
|
||||
lines.append(f"| {y}W{w:02d} | {date_range} | {cnt} {bar} | {marker} |")
|
||||
|
||||
lines.append(f"")
|
||||
|
||||
# 趋势总结
|
||||
lines.append(f"### 趋势分析")
|
||||
lines.append(f"- **整体趋势**: {analysis['trend']}")
|
||||
first_half_weeks = len(weeks_data) // 2
|
||||
second_half_weeks = len(weeks_data) - first_half_weeks
|
||||
lines.append(f" - 前半段（前 {first_half_weeks} 周）平均: {analysis['first_half_avg']} 节/周")
|
||||
lines.append(f" - 后半段（后 {second_half_weeks} 周）平均: {analysis['second_half_avg']} 节/周")
|
||||
lines.append(f"")
|
||||
|
||||
# 特殊事件
|
||||
if len(weeks_data) >= 2:
|
||||
counts = [c for _, _, c in weeks_data]
|
||||
events_found = []
|
||||
|
||||
for i in range(1, len(counts)):
|
||||
if counts[i - 1] > 0 and counts[i] >= counts[i - 1] * 2:
|
||||
y, w, _ = weeks_data[i]
|
||||
monday = datetime.fromisocalendar(y, w, 1)
|
||||
events_found.append(f"⚡ **{y}年W{w:02d}周（{monday.strftime('%m/%d')}起）完课量突增**：{counts[i-1]}→{counts[i]} 节")
|
||||
break
|
||||
|
||||
for i in range(1, len(counts)):
|
||||
if counts[i - 1] >= 3 and counts[i - 1] > 0 and counts[i] <= 1:
|
||||
y, w, _ = weeks_data[i]
|
||||
monday = datetime.fromisocalendar(y, w, 1)
|
||||
events_found.append(f"🔻 **{y}年W{w:02d}周（{monday.strftime('%m/%d')}起）完课量骤降**：{counts[i-1]}→{counts[i]} 节")
|
||||
break
|
||||
|
||||
if events_found:
|
||||
lines.append(f"**值得关注的变化**:")
|
||||
for ev in events_found:
|
||||
lines.append(f"- {ev}")
|
||||
lines.append(f"")
|
||||
|
||||
# ═══ 三、完课记录明细 ═══
|
||||
lines.append(f"---")
|
||||
lines.append(f"## 三、完课记录明细")
|
||||
lines.append(f"")
|
||||
lines.append(f"| 序号 | 日期 | 时间 | 星期 | 时段 | 级别 | 课程ID |")
|
||||
lines.append(f"|------|------|------|------|------|------|--------|")
|
||||
|
||||
for i, r in enumerate(records, 1):
|
||||
dt = r["updated_at"]
|
||||
if dt is None:
|
||||
continue
|
||||
date_str = dt.strftime("%Y-%m-%d")
|
||||
time_str = dt.strftime("%H:%M")
|
||||
weekday = WEEKDAY_NAMES[dt.weekday()]
|
||||
period = classify_period(dt.hour)
|
||||
level = r.get("level") or "-"
|
||||
chapter_id = r.get("chapter_id") or "-"
|
||||
lines.append(f"| {i} | {date_str} | {time_str} | {weekday} | {period} | {level} | {chapter_id} |")
|
||||
|
||||
lines.append(f"")
|
||||
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
# ── 主函数 ────────────────────────────────────────────
|
||||
|
||||
def main():
|
||||
if len(sys.argv) < 2:
|
||||
print("用法: python3 studytime_analysis.py <role_id>", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
try:
|
||||
role_id = int(sys.argv[1])
|
||||
except ValueError:
|
||||
print(f"错误: 角色ID必须是数字，收到: {sys.argv[1]}", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
records = fetch_completion_records(role_id)
|
||||
excluded_count = count_excluded_records(role_id)
|
||||
day_counts, weekday_periods = analyze_weekly_distribution(records)
|
||||
weeks_data, analysis = analyze_weekly_trend(records)
|
||||
report = format_report(role_id, records, excluded_count, day_counts, weekday_periods, weeks_data, analysis)
|
||||
|
||||
print(report)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Reference in New Issue
Block a user