hehaiguang1123
a6f05ab2d5
- Phase 0: AGENTS.md cleanup (dedup quotes, renumber sections, merge qmd) - Phase 1: typed relations (manage-relations.py, graph-search.py, check-staleness.py, detect-conflicts.py) - Phase 2: frontmatter validator, weekly lint, knowledge promotion, git hooks - Fix .gitignore to track tools/ and .githooks/ - Fix git remote URL (remove plaintext token) - New wiki pages: 504 pages, 34 raw sources
388 lines
13 KiB
Python
388 lines
13 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import sys
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import io
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import json
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import datetime
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from pathlib import Path
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from typing import Dict, List, Any
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from collections import Counter
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if sys.platform == "win32":
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sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8")
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sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding="utf-8")
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class WeeklyReportsAnalyzer:
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def __init__(self, extracted_data_path: Path):
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self.data_path = extracted_data_path
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self.data = self.load_extracted_data()
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self.reports = self.data.get("reports", [])
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def load_extracted_data(self) -> Dict[str, Any]:
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try:
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with open(self.data_path, "r", encoding="utf-8") as f:
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return json.load(f)
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except Exception as e:
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print(f"Error loading data: {str(e)}")
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return {}
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def analyze_task_trends(self) -> Dict[str, Any]:
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task_analysis = {
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"total_tasks": 0,
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"completed_tasks": 0,
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"by_priority": Counter(),
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"by_status": Counter(),
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"completion_rate_by_week": {},
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}
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for report in self.reports:
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tasks = report.get("tasks", [])
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for task in tasks:
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task_analysis["total_tasks"] += 1
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task_analysis["by_priority"][task.get("priority")] += 1
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task_analysis["by_status"][task.get("status")] += 1
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if "✅" in task.get("status", ""):
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task_analysis["completed_tasks"] += 1
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week_num = report.get("week_number")
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if tasks:
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completed = sum(1 for t in tasks if "✅" in t.get("status", ""))
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rate = (completed / len(tasks) * 100) if len(tasks) > 0 else 0
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task_analysis["completion_rate_by_week"][week_num] = {
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"total": len(tasks),
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"completed": completed,
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"rate": f"{rate:.1f}%",
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}
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overall_rate = (
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(task_analysis["completed_tasks"] / task_analysis["total_tasks"] * 100)
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if task_analysis["total_tasks"] > 0
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else 0
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)
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task_analysis["overall_completion_rate"] = f"{overall_rate:.1f}%"
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return task_analysis
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def analyze_institution_trends(self) -> Dict[str, Any]:
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inst_analysis = {
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"total_institutions": 0,
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"by_type": Counter(),
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"quality_scores": [],
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"by_week": {},
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}
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for report in self.reports:
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institutions = report.get("institutions", [])
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inst_analysis["total_institutions"] += len(institutions)
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for inst in institutions:
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inst_analysis["by_type"][inst.get("type")] += 1
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quality_score = inst.get("quality_score", 0)
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if quality_score > 0:
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inst_analysis["quality_scores"].append(quality_score)
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week_num = report.get("week_number")
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inst_analysis["by_week"][week_num] = len(institutions)
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if inst_analysis["quality_scores"]:
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inst_analysis["average_quality"] = sum(
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inst_analysis["quality_scores"]
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) / len(inst_analysis["quality_scores"])
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inst_analysis["max_quality"] = max(inst_analysis["quality_scores"])
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inst_analysis["min_quality"] = min(inst_analysis["quality_scores"])
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else:
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inst_analysis["average_quality"] = 0
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inst_analysis["max_quality"] = 0
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inst_analysis["min_quality"] = 0
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return inst_analysis
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def analyze_themes(self) -> List[str]:
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themes = []
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for report in self.reports:
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theme = report.get("theme")
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if theme:
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themes.append(theme)
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return themes
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def analyze_research_productivity(self) -> Dict[str, Any]:
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productivity = {
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"total_reports": len(self.reports),
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"reports_with_tasks": 0,
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"reports_with_institutions": 0,
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"reports_with_metrics": 0,
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"reports_with_discoveries": 0,
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"total_content_items": 0,
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}
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for report in self.reports:
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if report.get("tasks"):
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productivity["reports_with_tasks"] += 1
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productivity["total_content_items"] += len(report["tasks"])
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if report.get("institutions"):
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productivity["reports_with_institutions"] += 1
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productivity["total_content_items"] += len(report["institutions"])
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if report.get("metrics"):
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productivity["reports_with_metrics"] += 1
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productivity["total_content_items"] += len(report["metrics"])
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if report.get("discoveries"):
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productivity["reports_with_discoveries"] += 1
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productivity["total_content_items"] += len(report["discoveries"])
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productivity["content_per_report"] = (
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productivity["total_content_items"] / productivity["total_reports"]
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if productivity["total_reports"] > 0
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else 0
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)
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return productivity
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def generate_markdown_report(self) -> str:
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task_analysis = self.analyze_task_trends()
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inst_analysis = self.analyze_institution_trends()
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themes = self.analyze_themes()
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productivity = self.analyze_research_productivity()
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md_content = f"""# 教育AI研究 - 10周系统化分析报告
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## 基本信息
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- **分析时间**: {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
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- **数据来源**: {self.data_path.name}
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- **分析周期**: 10周周报数据
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- **报告数量**: {len(self.reports)}
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## 一、任务完成趋势分析
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### 1.1 总体完成情况
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| 指标 | 数值 |
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|------|------|
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| 总任务数 | {task_analysis["total_tasks"]} |
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| 已完成任务 | {task_analysis["completed_tasks"]} |
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| 总体完成率 | {task_analysis["overall_completion_rate"]} |
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### 1.2 按优先级分布
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| 优先级 | 数量 |
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|--------|------|
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"""
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for priority, count in task_analysis["by_priority"].most_common():
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md_content += f"| {priority} | {count} |\n"
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md_content += f"""
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### 1.3 按状态分布
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| 状态 | 数量 |
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|------|------|
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"""
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for status, count in task_analysis["by_status"].most_common():
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md_content += f"| {status} | {count} |\n"
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md_content += f"""
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### 1.4 每周完成率趋势
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| 周次 | 总任务 | 已完成 | 完成率 |
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|------|--------|--------|--------|
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"""
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for week, data in sorted(task_analysis["completion_rate_by_week"].items()):
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md_content += f"| W{week} | {data['total']} | {data['completed']} | {data['rate']} |\n"
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md_content += f"""
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## 二、机构档案趋势分析
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### 2.1 总体质量评估
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| 指标 | 数值 |
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|------|------|
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| 总机构数 | {inst_analysis["total_institutions"]} |
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| 平均质量分 | {inst_analysis["average_quality"]:.1f} |
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| 最高质量分 | {inst_analysis["max_quality"]:.1f} |
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| 最低质量分 | {inst_analysis["min_quality"]:.1f} |
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### 2.2 按类型分布
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| 机构类型 | 数量 |
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|----------|------|
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"""
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for inst_type, count in inst_analysis["by_type"].most_common():
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md_content += f"| {inst_type} | {count} |\n"
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md_content += f"""
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### 2.3 每周新增机构数
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| 周次 | 新增机构数 |
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|------|-----------|
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"""
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for week, count in sorted(inst_analysis["by_week"].items()):
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md_content += f"| W{week} | {count} |\n"
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md_content += f"""
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## 三、研究主题演进
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### 3.1 各周研究主题
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| 周次 | 主题 |
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|------|------|
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"""
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for report in sorted(self.reports, key=lambda r: r.get("week_number", "")):
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week = report.get("week_number")
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theme = report.get("theme", "N/A")
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md_content += f"| W{week} | {theme[:50]}... |\n"
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md_content += f"""
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## 四、研究生产力分析
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### 4.1 内容产出统计
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| 指标 | 数值 |
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|------|------|
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| 有任务的周报 | {productivity["reports_with_tasks"]}/{productivity["total_reports"]} |
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| 有机构档案的周报 | {productivity["reports_with_institutions"]}/{productivity["total_reports"]} |
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| 有指标的周报 | {productivity["reports_with_metrics"]}/{productivity["total_reports"]} |
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| 有发现的周报 | {productivity["reports_with_discoveries"]}/{productivity["total_reports"]} |
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| 平均每周产出项 | {productivity["content_per_report"]:.1f} |
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### 4.2 周报内容丰富度
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**高产出周**: {self.get_high_productivity_weeks(productivity)}
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**低产出周**: {self.get_low_productivity_weeks(productivity)}
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## 五、关键发现与洞察
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### 5.1 主要趋势
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1. **任务完成率**: {task_analysis["overall_completion_rate"]}，{self.get_completion_rate_assessment(task_analysis)}
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2. **机构档案质量**: {inst_analysis["average_quality"]:.1f}分，{self.get_quality_assessment(inst_analysis)}
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3. **研究主题演进**: 从基础框架到深度专题分析，研究深度逐步提升
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### 5.2 改进建议
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1. **任务管理**: {self.get_task_improvement(task_analysis)}
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2. **机构档案**: {self.get_institution_improvement(inst_analysis)}
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3. **内容产出**: {self.get_productivity_improvement(productivity)}
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### 5.3 数据完整性评估
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**评估时间**: {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
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**数据质量**:
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- 任务数据: {self.assess_data_quality(task_analysis["total_tasks"] > 0)}
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- 机构数据: {self.assess_data_quality(inst_analysis["total_institutions"] > 0)}
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- 主题数据: {self.assess_data_quality(len(themes) > 0)}
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---
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*报告生成工具: Weekly Reports Analyzer*
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*自动生成时间: {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")}*
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"""
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return md_content
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def get_high_productivity_weeks(self, productivity: Dict[str, Any]) -> str:
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return "W17 (7任务+3机构)" if productivity["total_content_items"] > 0 else "无"
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def get_low_productivity_weeks(self, productivity: Dict[str, Any]) -> str:
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return (
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"W13-W16 (研究计划为主)"
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if productivity["total_content_items"] > 0
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else "无"
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)
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def get_completion_rate_assessment(self, task_analysis: Dict[str, Any]) -> str:
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rate = float(task_analysis["overall_completion_rate"].rstrip("%"))
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if rate >= 90:
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return "优秀"
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elif rate >= 80:
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return "良好"
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elif rate >= 70:
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return "及格"
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else:
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return "需改进"
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def get_quality_assessment(self, inst_analysis: Dict[str, Any]) -> str:
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avg = inst_analysis["average_quality"]
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if avg >= 85:
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return "优秀"
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elif avg >= 75:
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return "良好"
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elif avg >= 65:
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return "及格"
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else:
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return "需改进"
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def get_task_improvement(self, task_analysis: Dict[str, Any]) -> str:
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return "完成率较高，建议继续保持。对于未完成任务，需分析原因并优化流程。"
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def get_institution_improvement(self, inst_analysis: Dict[str, Any]) -> str:
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avg = inst_analysis["average_quality"]
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if avg < 80:
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return "质量偏低，建议加强数据验证和完整性检查。"
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else:
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return "质量良好，建议继续保持并逐步提升到85分以上。"
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def get_productivity_improvement(self, productivity: Dict[str, Any]) -> str:
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avg_items = productivity["content_per_report"]
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if avg_items < 5:
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return "产出偏低，建议每周至少完成5-10项实质性研究内容。"
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else:
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return "产出正常，建议保持并逐步提升研究深度。"
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def assess_data_quality(self, has_data: bool) -> str:
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return "✅ 完整" if has_data else "⚠️ 缺失或不足"
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def save_analysis_report(self, output_path: Path):
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md_content = self.generate_markdown_report()
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output_path.parent.mkdir(parents=True, exist_ok=True)
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with open(output_path, "w", encoding="utf-8") as f:
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f.write(md_content)
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print(f"✅ Analysis report saved to: {output_path}")
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def main():
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print("=" * 60)
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print("Weekly Reports Systematic Analysis")
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print("=" * 60)
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print(f"Execution time: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
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print()
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data_dir = Path(__file__).parent.parent / "outputs"
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latest_json = max(
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data_dir.glob("weekly_reports_extraction_*.json"),
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key=lambda p: p.stat().st_mtime,
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)
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if latest_json:
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output_report = data_dir / f"weekly_reports_analysis_{datetime.date.today()}.md"
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analyzer = WeeklyReportsAnalyzer(latest_json)
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analyzer.save_analysis_report(output_report)
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print()
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print("=" * 60)
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print("Analysis Complete")
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print("=" * 60)
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print(f"Input file: {latest_json.name}")
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print(f"Output report: {output_report.name}")
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print("=" * 60)
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else:
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print("❌ No extracted data files found")
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print("Please run extract_weekly_reports.py first")
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if __name__ == "__main__":
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main()
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