451 lines
17 KiB
Python
Executable File
451 lines
17 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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inkos 质量监控脚本
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监控写作质量，自动发现问题并提供修复建议
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"""
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import os
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import json
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import re
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import sys
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from datetime import datetime
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from pathlib import Path
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class QualityMonitor:
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def __init__(self, config_path=None):
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self.config = self.load_config(config_path)
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self.problems = []
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self.recommendations = []
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def load_config(self, config_path):
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"""加载配置文件"""
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default_config = {
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"paragraph": {
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"min_length": 35,
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"max_consecutive_short": 3,
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"short_warning_ratio": 0.3
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},
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"golden_points": {
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"required_per_chapter": 3,
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"keywords": ["打脸", "升级", "收获", "碾压", "反转", "爽点"]
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},
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"emotional_arc": {
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"required": True,
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"min_change": 0.3
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}
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}
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if config_path and os.path.exists(config_path):
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try:
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with open(config_path, 'r', encoding='utf-8') as f:
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return json.load(f)
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except:
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return default_config
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return default_config
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def analyze_chapter(self, chapter_path):
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"""分析章节质量"""
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if not os.path.exists(chapter_path):
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return {"error": "文件不存在"}
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with open(chapter_path, 'r', encoding='utf-8') as f:
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content = f.read()
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# 提取章节信息
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chapter_num = self.extract_chapter_number(chapter_path)
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# 分析各项指标
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results = {
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"chapter": chapter_num,
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"file": os.path.basename(chapter_path),
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"timestamp": datetime.now().isoformat(),
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"metrics": {}
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}
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# 1. 段落分析
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para_results = self.analyze_paragraphs(content)
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results["metrics"]["paragraphs"] = para_results
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# 2. 爽点分析
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golden_results = self.analyze_golden_points(content, chapter_num)
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results["metrics"]["golden_points"] = golden_results
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# 3. 情绪弧线分析
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emotion_results = self.analyze_emotional_arc(content)
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results["metrics"]["emotional_arc"] = emotion_results
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# 4. 对话分析
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dialogue_results = self.analyze_dialogue(content)
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results["metrics"]["dialogue"] = dialogue_results
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# 5. 问题汇总
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problems = self.identify_problems(results)
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results["problems"] = problems
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# 6. 修复建议
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recommendations = self.generate_recommendations(problems, chapter_num)
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results["recommendations"] = recommendations
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return results
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def extract_chapter_number(self, filepath):
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"""从文件名提取章节号"""
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filename = os.path.basename(filepath)
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match = re.search(r'(\d{4})_', filename)
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if match:
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return int(match.group(1))
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return 0
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def analyze_paragraphs(self, content):
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"""分析段落结构"""
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lines = content.split('\n')
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paragraphs = []
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current_para = []
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for line in lines:
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stripped = line.strip()
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if not stripped:
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if current_para:
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paragraphs.append(''.join(current_para))
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current_para = []
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else:
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current_para.append(line + ' ')
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if current_para:
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paragraphs.append(''.join(current_para))
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# 过滤空段落和标题
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filtered_paras = []
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for para in paragraphs:
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para_stripped = para.strip()
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if para_stripped and not para_stripped.startswith('#'):
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filtered_paras.append(para_stripped)
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# 统计段落长度
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lengths = []
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short_count = 0
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consecutive_short = 0
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max_consecutive = 0
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current_streak = 0
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for para in filtered_paras:
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# 计算中文字符数
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chinese_chars = len([c for c in para if '\u4e00' <= c <= '\u9fff'])
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# 数字和英文字母
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other_chars = len(re.findall(r'[a-zA-Z0-9]', para))
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total = chinese_chars + other_chars
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lengths.append(total)
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if total < self.config["paragraph"]["min_length"]:
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short_count += 1
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current_streak += 1
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if current_streak > max_consecutive:
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max_consecutive = current_streak
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else:
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current_streak = 0
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total_paras = len(filtered_paras)
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short_ratio = short_count / total_paras if total_paras > 0 else 0
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return {
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"total_paragraphs": total_paras,
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"short_paragraphs": short_count,
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"short_ratio": round(short_ratio, 3),
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"max_consecutive_short": max_consecutive,
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"avg_length": round(sum(lengths) / len(lengths)) if lengths else 0,
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"min_length": min(lengths) if lengths else 0,
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"max_length": max(lengths) if lengths else 0,
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"lengths": lengths[:20] # 只保留前20个用于展示
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}
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def analyze_golden_points(self, content, chapter_num):
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"""分析爽点密度"""
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keywords = self.config["golden_points"]["keywords"]
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required = self.config["golden_points"]["required_per_chapter"]
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found_keywords = []
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keyword_positions = {}
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for keyword in keywords:
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if keyword in content:
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found_keywords.append(keyword)
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# 统计出现次数
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count = content.count(keyword)
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keyword_positions[keyword] = count
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# 根据章节号调整期望值
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if chapter_num == 1:
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expected_min = 3 # 黄金三章需要更多爽点
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elif chapter_num <= 3:
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expected_min = 2
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else:
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expected_min = required
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return {
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"found_keywords": found_keywords,
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"total_found": len(found_keywords),
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"expected_min": expected_min,
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"meets_requirement": len(found_keywords) >= expected_min,
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"keyword_counts": keyword_positions,
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"content_samples": self.extract_golden_point_samples(content, found_keywords)
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}
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def extract_golden_point_samples(self, content, keywords, sample_count=3):
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"""提取爽点示例"""
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samples = []
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lines = content.split('\n')
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for keyword in keywords[:3]: # 最多检查3个关键词
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for i, line in enumerate(lines):
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if keyword in line and len(line.strip()) > 20:
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# 取上下文
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start = max(0, i - 1)
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end = min(len(lines), i + 2)
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context = '\n'.join(lines[start:end])
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samples.append({
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"keyword": keyword,
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"context": context
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})
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if len(samples) >= sample_count:
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break
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if len(samples) >= sample_count:
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break
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return samples
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def analyze_emotional_arc(self, content):
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"""分析情绪弧线（简化版）"""
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# 情绪关键词
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positive_words = ["兴奋", "开心", "满足", "自信", "希望", "轻松"]
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negative_words = ["紧张", "焦虑", "恐惧", "痛苦", "绝望", "压力"]
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neutral_words = ["平静", "思考", "观察", "计算", "等待"]
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positive_count = sum(content.count(word) for word in positive_words)
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negative_count = sum(content.count(word) for word in negative_words)
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neutral_count = sum(content.count(word) for word in neutral_words)
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total = positive_count + negative_count + neutral_count
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if total == 0:
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intensity = 0
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else:
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# 情绪强度 = (积极+消极)/总数
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intensity = (positive_count + negative_count) / total
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# 情绪变化（简化：检查是否有情绪转换）
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lines = content.split('\n')
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emotion_changes = 0
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last_emotion = None
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for line in lines[:50]: # 只检查前50行
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line_emotion = None
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if any(word in line for word in positive_words):
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line_emotion = "positive"
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elif any(word in line for word in negative_words):
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line_emotion = "negative"
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elif any(word in line for word in neutral_words):
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line_emotion = "neutral"
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if last_emotion and line_emotion and line_emotion != last_emotion:
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emotion_changes += 1
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if line_emotion:
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last_emotion = line_emotion
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return {
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"positive_count": positive_count,
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"negative_count": negative_count,
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"neutral_count": neutral_count,
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"total_emotion_words": total,
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"emotional_intensity": round(intensity, 3),
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"emotion_changes": emotion_changes,
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"has_emotional_arc": emotion_changes >= 2
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}
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def analyze_dialogue(self, content):
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"""分析对话质量"""
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# 简单的对话检测
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dialogue_pattern = r'["「](.+?)["」]'
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dialogues = re.findall(dialogue_pattern, content)
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total_chars = len(content)
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dialogue_chars = sum(len(d) for d in dialogues)
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dialogue_ratio = dialogue_chars / total_chars if total_chars > 0 else 0
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# 对话长度分析
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dialogue_lengths = [len(d) for d in dialogues]
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avg_dialogue_length = sum(dialogue_lengths) / len(dialogue_lengths) if dialogues else 0
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return {
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"dialogue_count": len(dialogues),
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"dialogue_ratio": round(dialogue_ratio, 3),
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"avg_dialogue_length": round(avg_dialogue_length, 1),
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"sample_dialogues": dialogues[:5] # 前5个对话示例
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}
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def identify_problems(self, results):
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"""识别问题"""
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problems = []
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# 段落问题
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para_metrics = results["metrics"]["paragraphs"]
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if para_metrics["short_ratio"] > self.config["paragraph"]["short_warning_ratio"]:
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problems.append({
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"type": "paragraph_structure",
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"severity": "high",
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"description": f"短段落比例过高：{para_metrics['short_ratio']*100:.1f}%",
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"details": f"共{para_metrics['total_paragraphs']}个段落，其中{para_metrics['short_paragraphs']}个短段落"
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})
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if para_metrics["max_consecutive_short"] > self.config["paragraph"]["max_consecutive_short"]:
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problems.append({
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"type": "consecutive_short_paragraphs",
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"severity": "medium",
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"description": f"连续短段落过多：{para_metrics['max_consecutive_short']}个",
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"details": "影响阅读流畅性"
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})
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# 爽点问题
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golden_metrics = results["metrics"]["golden_points"]
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if not golden_metrics["meets_requirement"]:
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problems.append({
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"type": "insufficient_golden_points",
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"severity": "high",
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"description": f"爽点不足：找到{golden_metrics['total_found']}个，需要{golden_metrics['expected_min']}个",
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"details": f"找到的关键词：{', '.join(golden_metrics['found_keywords'])}"
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})
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# 情绪弧线问题
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emotion_metrics = results["metrics"]["emotional_arc"]
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if self.config["emotional_arc"]["required"] and not emotion_metrics["has_emotional_arc"]:
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problems.append({
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"type": "flat_emotional_arc",
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"severity": "medium",
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"description": "情绪弧线平坦",
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"details": f"情绪变化次数：{emotion_metrics['emotion_changes']}"
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})
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# 对话问题
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dialogue_metrics = results["metrics"]["dialogue"]
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if dialogue_metrics["dialogue_ratio"] < 0.2:
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problems.append({
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"type": "low_dialogue_ratio",
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"severity": "low",
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"description": f"对话比例偏低：{dialogue_metrics['dialogue_ratio']*100:.1f}%",
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"details": "番茄小说建议对话比例在30-40%"
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})
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return problems
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def generate_recommendations(self, problems, chapter_num):
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"""生成修复建议"""
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recommendations = []
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for problem in problems:
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if problem["type"] == "paragraph_structure":
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recommendations.append({
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"action": "merge_short_paragraphs",
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"priority": "high" if problem["severity"] == "high" else "medium",
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"description": "合并短段落，提高段落平均长度",
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"command": f"python3 merge_paragraphs.py --chapter {chapter_num} --min-length 35"
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})
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elif problem["type"] == "insufficient_golden_points":
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recommendations.append({
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"action": "add_golden_points",
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"priority": "high",
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"description": "增加爽点密度",
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"suggestions": [
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"增加一个打脸情节",
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"展现主角的优势",
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"设置一个小型反转",
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"增加资源收获"
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]
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})
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elif problem["type"] == "flat_emotional_arc":
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recommendations.append({
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"action": "enhance_emotional_arc",
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"priority": "medium",
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"description": "增强情绪起伏",
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"suggestions": [
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"在章节开头设置紧张情绪",
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"在中间加入情绪释放点",
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"在结尾设置情绪钩子"
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]
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})
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return recommendations
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def generate_report(self, results, output_path=None):
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"""生成质量报告"""
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report = {
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"summary": {
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"chapter": results["chapter"],
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"file": results["file"],
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"timestamp": results["timestamp"],
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"problem_count": len(results["problems"]),
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"recommendation_count": len(results["recommendations"])
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},
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"metrics": results["metrics"],
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"problems": results["problems"],
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"recommendations": results["recommendations"]
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}
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if output_path:
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with open(output_path, 'w', encoding='utf-8') as f:
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json.dump(report, f, ensure_ascii=False, indent=2)
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return report
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def print_summary(self, report):
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"""打印摘要"""
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print(f"\n{'='*60}")
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print(f"章节质量报告 - 第{report['summary']['chapter']}章")
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print(f"{'='*60}")
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print(f"\n📊 指标概览：")
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print(f" 段落总数：{report['metrics']['paragraphs']['total_paragraphs']}")
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print(f" 短段落比例：{report['metrics']['paragraphs']['short_ratio']*100:.1f}%")
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print(f" 爽点数量：{report['metrics']['golden_points']['total_found']}")
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print(f" 情绪变化：{report['metrics']['emotional_arc']['emotion_changes']}次")
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print(f" 对话比例：{report['metrics']['dialogue']['dialogue_ratio']*100:.1f}%")
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if report['problems']:
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print(f"\n⚠️ 发现问题（{len(report['problems'])}个）：")
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for i, problem in enumerate(report['problems'], 1):
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print(f" {i}. [{problem['severity'].upper()}] {problem['description']}")
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if report['recommendations']:
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print(f"\n💡 修复建议（{len(report['recommendations'])}条）：")
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for i, rec in enumerate(report['recommendations'], 1):
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print(f" {i}. [{rec['priority']}] {rec['description']}")
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def main():
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"""主函数"""
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if len(sys.argv) < 2:
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print("用法：python quality_monitor.py <章节文件路径> [配置文件路径]")
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sys.exit(1)
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chapter_path = sys.argv[1]
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config_path = sys.argv[2] if len(sys.argv) > 2 else None
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monitor = QualityMonitor(config_path)
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results = monitor.analyze_chapter(chapter_path)
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# 生成报告文件
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report_file = f"quality_report_chapter{results['chapter']}.json"
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report = monitor.generate_report(results, report_file)
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# 打印摘要
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monitor.print_summary(report)
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print(f"\n📄 完整报告已保存到：{report_file}")
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# 如果有严重问题，返回非零退出码
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if any(p["severity"] == "high" for p in results["problems"]):
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sys.exit(1)
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else:
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sys.exit(0)
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if __name__ == "__main__":
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main() |