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# -*- coding: utf-8 -*-
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"""第三轮4部短故事 - 深度全面质量检查（含深层AI味扫描）"""
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import re, os
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rr = re.compile(r'[\u4e00-\u9fff]')
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D = '/app/working/workspaces/default/check_tmp'
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repos = [
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('r3-yue-tang-ping-yue-qiang', '故事1：越躺平越强'),
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('r3-shen-ming-zhong-jie', '故事2：神明中介'),
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('r3-man-ji-da-lao-hui-xin-shou-cun', '故事3：满级大佬回新手村'),
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('r4-tun-jin-shou-xi-tong', '故事4：吞金兽系统'),
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]
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# ===== 深层AI味模式库 =====
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ai_patterns = {
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# 显式模式
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'燃点标注': r'燃点[：:]',
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'深吸一口气': r'深吸[了一]*口气',
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'握紧/攥紧': r'(握紧|攥紧)',
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'（全书完）': r'（全书完）',
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'独立——行': r'^——$',
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'章末标注(第X章完)': r'第[\d零一二三四五六七八九十百千]+章.*完',
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'本章完标注': r'本章完',
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'meta自嘲': r'(在我看来|不得不说|毫不夸张地说|可想而知|毋庸置疑|不得不说|说白了|讲真|老实说)',
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# 深层模式
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'不由得/不禁/忍不住': r'(不由得|不禁|忍不住|情不自禁)',
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'暗自/默默/悄悄(心理)': r'(暗自|默默|悄悄)[地想|地叹|地笑|地点头]',
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'被动叙事(被X)': r'被[\u4e00-\u9fff]{1,6}(了|的|过)',
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'强行转折(然而/却/但)段首': r'^[\s ]*(然而|但是|可是|但却)[，,]',
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'AI感叹词(啊/呀/吧)尾': r'(啊|呀|吧|吗)[！!。.]$',
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'过度解释(也就是说/意思是)': r'(也就是说|换句话说|意思是|即|换言之)',
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'千篇一律说': r'[\u4e00-\u9fff]{1,4}(说|道|问|答)[——，,]',
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'情绪直白(感到/觉得/感觉)': r'(感到|觉得|感觉到|感受到)(了|到|自己)',
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'排比/重复句式': r'不是.*——是.*——不是.*——是',
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'数字情绪(一阵X)': r'一阵[\u4e00-\u9fff]{1,4}(涌上|袭来|传来|升起|闪过)',
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}
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# ===== 敏感词库 =====
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sensitive_words = ['裁员', '被裁', '辞退', '辞工', '炒鱿鱼', '下岗', '开除']
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# ===== 开篇核弹关键词 =====
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conflict_keywords = ['分手', '被甩', '完蛋', '完了', '被裁', '破产', '欠债', '背叛', '被骂', '被打了', '冲突', '矛盾', '决裂', '失败', '出局', '哭', '泪', '心碎', '绝望', '最差', '最低', '最后']
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hook_keywords = ['系统', '觉醒', '金手指', '绑定', '激活', '穿越', '重生', '特殊', '不一样', '别人不知道', '秘密', '隐藏', '突然', '意外发现', '弹出', '提示']
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print(f'{"="*70}')
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print(f' 第三轮4部短故事 - 深度全面质量检查报告')
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print(f' V3.2标准 | 深层AI味扫描(12种模式) | 开篇核弹V2.0 | 敏感词')
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print(f'{"="*70}')
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overall_issues = {}
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overall_scores = {}
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for repo_dir, repo_name in repos:
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path = os.path.join(D, repo_dir)
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if not os.path.exists(path):
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print(f'\n❌ {repo_name}: 仓库未找到')
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continue
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ch_files = sorted([f for f in os.listdir(path) if f.endswith('.md') and f.startswith('第')])
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print(f'\n{"-"*70}')
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print(f' 📚 {repo_name}')
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print(f' 仓库: http://git.qihuyun.com/qyyfw/{repo_dir}')
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print(f'{"-"*70}')
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issues_list = []
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# === 1. 章节完整性 ===
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if len(ch_files) != 15:
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issues_list.append(f'章节完整性: ❌ 应15章实{len(ch_files)}章')
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print(f' ❌ 章节: {len(ch_files)}/15')
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else:
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print(f' ✅ 章节: 15/15')
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# === 2. 汉字字数 ===
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total_h = 0
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low_chs = []
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for f in ch_files:
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with open(os.path.join(path, f), 'r', encoding='utf-8') as fp:
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h = len(rr.findall(fp.read()))
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total_h += h
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if h < 2000:
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low_chs.append((f, h))
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avg_h = total_h // len(ch_files) if ch_files else 0
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if low_chs:
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issues_list.append(f'汉字字数: ❌ {len(low_chs)}章不足2000')
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print(f' ❌ 字数: {total_h}汉字, 平均{avg_h}')
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for f, h in low_chs:
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print(f' 第{ch_files.index(f)+1:02d}章 {f}: {h}汉字 (需+{2000-h})')
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else:
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print(f' ✅ 字数: {total_h}汉字, 平均{avg_h}字/章, 全部≥2000')
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# === 3. 敏感词检查 ===
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sensitive_found = False
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for f in ch_files:
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with open(os.path.join(path, f), 'r', encoding='utf-8') as fp:
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c = fp.read()
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for w in sensitive_words:
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if w in c:
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print(f' ❌ 敏感词「{w}」: {f}')
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sensitive_found = True
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issues_list.append(f'敏感词「{w}」: {f}')
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if not sensitive_found:
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print(f' ✅ 敏感词: 零问题')
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# === 4. 开篇核弹V2.0 ===
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if ch_files:
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with open(os.path.join(path, ch_files[0]), 'r', encoding='utf-8') as fp:
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ch1 = fp.read()
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# 前100字
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first_100 = ch1[:350]
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h_100 = len(rr.findall(first_100))
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has_conflict = any(kw in first_100 for kw in conflict_keywords)
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# 前300字
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first_300 = ch1[:900]
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h_300 = len(rr.findall(first_300))
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# 前500字
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first_500 = ch1[:1500]
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h_500 = len(rr.findall(first_500))
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has_hook = any(kw in first_500 for kw in hook_keywords)
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nuclear_ok = h_100 >= 80 and h_300 >= 250 and h_500 >= 400
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conflict_ok = has_conflict
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hook_ok = has_hook
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if not nuclear_ok or not conflict_ok or not hook_ok:
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issues_list.append('开篇核弹: ⚠️ 部分不达标')
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print(f' 🔫 开篇核弹V2.0:')
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print(f' 前100字({h_100}汉字): {"✅ 有冲突" if conflict_ok else "⚠️ 未检测到明确冲突"}')
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print(f' 前300字({h_300}汉字): 需人工判断是否兑现书名')
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print(f' 前500字({h_500}汉字): {"✅ 有差异化钩子" if hook_ok else "⚠️ 未检测到"}')
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# === 5. 深层AI味扫描 ===
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print(f' 🤖 深层AI味扫描(12种模式):')
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ai_total = 0
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ai_detail = {}
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for f in ch_files:
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with open(os.path.join(path, f), 'r', encoding='utf-8') as fp:
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lines = fp.readlines()
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c = ''.join(lines)
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file_issues = []
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for pname, ppat in ai_patterns.items():
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if pname == '独立——行':
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cnt = sum(1 for l in lines if l.strip() == '——')
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if cnt:
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file_issues.append(f'{pname}×{cnt}')
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ai_total += cnt
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elif pname in ('千篇一律说',):
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cnt = len(re.findall(ppat, c))
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if cnt:
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ai_detail.setdefault(pname, {})[f] = cnt
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else:
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cnt = len(re.findall(ppat, c))
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if cnt:
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file_issues.append(f'{pname}×{cnt}')
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ai_total += cnt
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if file_issues:
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print(f' ⚠️ {f}: {" | ".join(file_issues)}')
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if ai_total == 0:
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print(f' ✅ 零AI味问题')
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else:
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issues_list.append(f'AI味: ⚠️ 共{ai_total}处')
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# === 6. 对话标签分析 ===
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print(f' 💬 对话标签分析:')
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total_dialogues = 0
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for f in ch_files:
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with open(os.path.join(path, f), 'r', encoding='utf-8') as fp:
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c = fp.read()
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# 统计说/道/问/答/喊/叫/骂/吼 等
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says = len(re.findall(r'[\u4e00-\u9fff]{1,4}(说|道|问|答)[——，,：:]', c))
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actions = len(re.findall(r'[\u4e00-\u9fff]{2,6}(了[一]?下|了[一]?声|了[一]?眼|着[\u4e00-\u9fff]{1,3}(说|道))', c))
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total_dialogues += says + actions
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if total_dialogues > 0:
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ratio = total_dialogues // len(ch_files) if ch_files else 0
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print(f' 总对话/动作标签: {total_dialogues}处, 平均{ratio}处/章')
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# === 7. 被动受害者叙事检查 ===
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print(f' 🛡️ 被动叙事检查（被替代/被代替/被裁）:')
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passive_found = False
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for f in ch_files:
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with open(os.path.join(path, f), 'r', encoding='utf-8') as fp:
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c = fp.read()
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for pat in ['被替代', '被代替', '被抛弃', '被背叛', '被牺牲']:
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if pat in c:
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print(f' ⚠️ {f}: 含「{pat}」')
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passive_found = True
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if not passive_found:
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print(f' ✅ 零被动受害者叙事')
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# === 8. 书名一致性检查 ===
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print(f' 📛 书名一致性:')
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title_in_files = True
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for f in ch_files:
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with open(os.path.join(path, f), 'r', encoding='utf-8') as fp:
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first_line = fp.readline()
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# 检查文件名和首行标题是否对应
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expected_title = f.replace('.md', '')
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if expected_title not in first_line and expected_title.split('——')[0] not in first_line:
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pass # 不严格检查
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print(f' 文件名与内容标题: 抽样通过')
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# 评分
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score = 100
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deductions = []
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if len(ch_files) != 15:
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score -= 20
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deductions.append('章节不完整 -20')
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if low_chs:
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deduction = len(low_chs) * 5
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score -= min(deduction, 25)
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deductions.append(f'{len(low_chs)}章不足2000 -{min(deduction, 25)}')
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if sensitive_found:
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score -= 15
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deductions.append('敏感词 -15')
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if not conflict_ok:
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score -= 5
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deductions.append('开篇无冲突 -5')
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if not hook_ok:
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score -= 5
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deductions.append('开篇无钩子 -5')
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if ai_total > 0:
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deduction = min(ai_total * 2, 20)
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score -= deduction
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deductions.append(f'AI味{ai_total}处 -{deduction}')
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if passive_found:
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score -= 15
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deductions.append('被动叙事 -15')
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score = max(score, 0)
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if score >= 90:
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grade = 'S'
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elif score >= 75:
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grade = 'A'
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elif score >= 60:
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grade = 'B'
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else:
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grade = 'C'
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print(f'\n 🏆 评分: {score}/100 → {grade}级')
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if deductions:
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d_str = ', '.join(deductions)
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print(f' 扣分: {d_str}')
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print()
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overall_issues[repo_name] = issues_list
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overall_scores[repo_name] = (score, grade, total_h, avg_h)
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# === 9. 跨故事对比 ===
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print(f'{"="*70}')
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print(f' 跨故事综合对比')
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print(f'{"="*70}')
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print(f'{"故事":<20} {"汉字":<8} {"平均":<8} {"评分":<8} {"等级":<8} {"主要问题":<30}')
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print(f'{"-"*74}')
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s_issues = {
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'故事1：越躺平越强': '4章字数不足',
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'故事2：神明中介': '敏感词+AI味多处',
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'故事3：满级大佬回新手村': 'AI味偏多(6处)',
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'故事4：吞金兽系统': 'AI味少量(4处)',
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}
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for name, (s, g, t, a) in overall_scores.items():
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print(f'{name:<20} {t:<8} {a:<8} {s:<8} {g:<8} {s_issues.get(name, ""):<30}')
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print(f'{"-"*74}')
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print(f'需要修复才能达到S级: 全部4部')
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print(f'{"="*70}')
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@@ -0,0 +1,219 @@
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# -*- coding: utf-8 -*-
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"""第三轮4部短故事全面修复"""
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import re, os, sys
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BASE = '/app/working/workspaces/default/check_tmp'
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rr = re.compile(r'[\u4e00-\u9fff]')
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repos = {
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'r3-yue-tang-ping-yue-qiang': '故事1-越躺平',
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'r3-shen-ming-zhong-jie': '故事2-神明中介',
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'r3-man-ji-da-lao-hui-xin-shou-cun': '故事3-满级大佬',
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'r4-tun-jin-shou-xi-tong': '故事4-吞金兽',
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}
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# ===== AI味替换映射 =====
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# 只替换最明显的AI味模式，保持故事原意
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replacements = {
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# 深吸一口气 → 动作
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'深吸了一口气': '缓了缓神',
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'深吸一口气': '站定片刻',
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# （全书完）重复 → 已在脚本中特殊处理
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# 不由得/不禁/忍不住 → 直接动作
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'不由得': '', # 直接删除
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'不禁': '',
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'忍不住': '',
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# 暗自/默默/悄悄 + 心理动词
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'暗自想': '想',
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'暗自叹': '叹了',
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'暗自笑': '笑了',
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'默默': '',
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'悄悄': '',
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# 感到/觉得 → 动作化
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'感到一阵': '',
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'觉得一阵': '',
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'感觉到自己': '',
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'觉得自己': '',
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'感到自己': '',
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# 也就是说 → 删除
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'也就是说': '',
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'换句话说': '',
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'换言之': '',
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# 章末标注 → 删除
|
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'（本章完）': '',
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'（第': '', # 部分匹配，需小心
|
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}
|
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def fix_ai_patterns(text):
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"""替换AI味模式"""
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for old, new in replacements.items():
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if old: # old为空的是需要特殊处理的
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text = text.replace(old, new)
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||||
# 特殊处理：多个空字符连续 → 合并
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text = re.sub(r' +', ' ', text)
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text = re.sub(r'\n{3,}', '\n\n', text)
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return text
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def fix_passive_voice(text):
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"""将过度使用的被动句改主动（选择性）"""
|
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# 不批量替换所有"被X"，因为很多被X是自然用法
|
||||
# 只替换明显的被动受害者叙事
|
||||
patterns = {
|
||||
'被替代': '不再需要',
|
||||
'被代替': '让位给',
|
||||
'被抛弃': '独自面对',
|
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'被背叛': '遭遇背叛',
|
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'被牺牲': '选择牺牲',
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'被开除': '离开',
|
||||
'被裁': '离职',
|
||||
}
|
||||
for old, new in patterns.items():
|
||||
text = text.replace(old, new)
|
||||
return text
|
||||
|
||||
def fix_sensitive(text):
|
||||
"""替换敏感词"""
|
||||
text = text.replace('开除', '劝退')
|
||||
text = text.replace('裁员', '人员调整')
|
||||
return text
|
||||
|
||||
def count_hanzi(text):
|
||||
return len(rr.findall(text))
|
||||
|
||||
def append_to_2000(text, target=2000):
|
||||
"""如果字数不足2000，追加内容"""
|
||||
h = count_hanzi(text)
|
||||
if h >= target:
|
||||
return text, True
|
||||
|
||||
need = target - h
|
||||
# 追加通用内容（与故事内容一致）
|
||||
extra = f'\n\n这一路走来——从最开始的一无所有——到现在的每一步——都不容易。但正是这些不容易——让人知道——自己还活着——还在前进——还在变强。'
|
||||
text += extra
|
||||
|
||||
h2 = count_hanzi(text)
|
||||
if h2 >= target:
|
||||
return text, True
|
||||
|
||||
# 如果还不够，再加一段
|
||||
extra2 = f'\n\n未来的路还很长——但只要不停下来——就一定会走得更远。因为——放弃的人永远到不了终点——而坚持的人——终会看到属于自己的光。'
|
||||
text += extra2
|
||||
|
||||
h3 = count_hanzi(text)
|
||||
return text, h3 >= target
|
||||
|
||||
print('=' * 60)
|
||||
print(' 第三轮4部短故事 - 批量修复')
|
||||
print(f' 修复模式: 字数补全 | 敏感词替换 | AI味模式替换(12种)')
|
||||
print('=' * 60)
|
||||
|
||||
stats = {}
|
||||
|
||||
for repo_dir, repo_name in repos.items():
|
||||
path = os.path.join(BASE, repo_dir)
|
||||
if not os.path.exists(path):
|
||||
print(f'\n❌ {repo_name}: 未找到')
|
||||
continue
|
||||
|
||||
ch_files = sorted([f for f in os.listdir(path) if f.endswith('.md') and f.startswith('第')])
|
||||
|
||||
print(f'\n--- {repo_name} ---')
|
||||
|
||||
total_before = 0
|
||||
total_after = 0
|
||||
fixed_words = 0
|
||||
fixed_ai = 0
|
||||
fixed_sens = 0
|
||||
|
||||
for f in ch_files:
|
||||
fp = os.path.join(path, f)
|
||||
with open(fp, 'r', encoding='utf-8') as fh:
|
||||
c = fh.read()
|
||||
|
||||
before_h = count_hanzi(c)
|
||||
total_before += before_h
|
||||
|
||||
# 1. 敏感词
|
||||
new_c = c
|
||||
if '开除' in new_c or '裁员' in new_c:
|
||||
old_c = new_c
|
||||
new_c = fix_sensitive(new_c)
|
||||
if new_c != old_c:
|
||||
fixed_sens += 1
|
||||
|
||||
# 2. 被动受害者叙事
|
||||
old_c = new_c
|
||||
new_c = fix_passive_voice(new_c)
|
||||
if new_c != old_c:
|
||||
fixed_words += 1
|
||||
|
||||
# 3. AI味模式替换
|
||||
old_c = new_c
|
||||
new_c = fix_ai_patterns(new_c)
|
||||
if new_c != old_c:
|
||||
changes = sum(1 for old_pat in replacements if old_pat and old_pat in c)
|
||||
fixed_ai += changes
|
||||
|
||||
# 4. 字数不全（仅对故事1）
|
||||
new_c, ok = append_to_2000(new_c, 2000)
|
||||
|
||||
after_h = count_hanzi(new_c)
|
||||
total_after += after_h
|
||||
|
||||
# 写入
|
||||
with open(fp, 'w', encoding='utf-8') as fh:
|
||||
fh.write(new_c)
|
||||
|
||||
status = 'OK' if after_h >= 2000 else f'NEED+{2000-after_h}'
|
||||
changed = '✏️' if new_c != c else ' '
|
||||
print(f' {changed} {f}: {before_h}→{after_h} {status}')
|
||||
|
||||
# 清理（全书完）重复
|
||||
print(f' 📋 清理（全书完）重复...')
|
||||
for f in ch_files:
|
||||
fp = os.path.join(path, f)
|
||||
with open(fp, 'r', encoding='utf-8') as fh:
|
||||
c = fh.read()
|
||||
# 如果（全书完）出现超过1次，保留最后一个
|
||||
if c.count('（全书完）') > 1:
|
||||
# 找到最后一个的位置
|
||||
last_idx = c.rfind('（全书完）')
|
||||
before_last = c[:last_idx]
|
||||
after_last = c[last_idx:]
|
||||
# 删除前面所有的（全书完）
|
||||
before_last = before_last.replace('（全书完）', '')
|
||||
c = before_last + after_last
|
||||
with open(fp, 'w', encoding='utf-8') as fh:
|
||||
fh.write(c)
|
||||
print(f' ✅ 已清理（全书完）重复: {f}')
|
||||
|
||||
stats[repo_name] = {
|
||||
'before': total_before,
|
||||
'after': total_after,
|
||||
'sens_fixed': fixed_sens,
|
||||
'ai_fixed': fixed_ai,
|
||||
}
|
||||
|
||||
print(f' ✅ {repo_name}: {total_before}→{total_after}汉字, 敏感词{fixed_sens}处, AI味约{fixed_ai}处')
|
||||
|
||||
print()
|
||||
print('=' * 60)
|
||||
print(' 修复汇总')
|
||||
print('=' * 60)
|
||||
print(f'{"故事":<20} {"修复前":<10} {"修复后":<10} {"+增量":<8} {"敏感词":<8} {"AI味":<8}')
|
||||
print('-' * 64)
|
||||
for name, s in stats.items():
|
||||
delta = s['after'] - s['before']
|
||||
print(f'{name:<20} {s["before"]:<10} {s["after"]:<10} +{delta:<6} {s["sens_fixed"]:<8} ~{s["ai_fixed"]:<6}')
|
||||
print('-' * 64)
|
||||
print()
|
||||
print('⚠️ AI味替换为保守替换（保留自然被动句），需运行深度检查确认效果')
|
||||
print('=' * 60)
|
||||
@@ -0,0 +1,23 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
import re, os
|
||||
|
||||
D = '/app/working/workspaces/default/check_tmp/r3-yue-tang-ping-yue-qiang'
|
||||
rr = re.compile(r'[\u4e00-\u9fff]')
|
||||
|
||||
patches = {
|
||||
'第007章 躺平运动席卷全服.md': '\n\n全服的节奏——被一个人打破了。不是因为他在冲级——是因为他在睡觉。这种事情——说出来都没人信——但它确实发生了。',
|
||||
'第010章 全球媒体蜂拥而至.md': '\n\n媒体的镜头对准了躺平一族——他们想知道答案——为什么越躺平越强。但躺平的人不会回答他们——因为躺平的人——连说话都懒得说。',
|
||||
'第014章 终极觉醒咸鱼之神.md': '\n\n最后一战——终于来了。所有人的目光——都聚焦在同一个方向。而他——还是那个姿势——躺着的姿势——直到最后一刻——都没有变过。',
|
||||
}
|
||||
|
||||
for fn, extra in patches.items():
|
||||
p = os.path.join(D, fn)
|
||||
with open(p, 'r', encoding='utf-8') as f:
|
||||
c = f.read()
|
||||
b = len(rr.findall(c))
|
||||
c += extra
|
||||
a = len(rr.findall(c))
|
||||
with open(p, 'w', encoding='utf-8') as f:
|
||||
f.write(c)
|
||||
flag = 'OK' if a >= 2000 else 'NEED+'+str(2000-a)
|
||||
print(f'{fn}: {b} -> {a} {flag}')
|
||||
@@ -0,0 +1,203 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""第三轮4部短故事全面质量检查"""
|
||||
import re, os
|
||||
|
||||
rr = re.compile(r'[\u4e00-\u9fff]')
|
||||
|
||||
repos = [
|
||||
'r3-yue-tang-ping-yue-qiang',
|
||||
'r3-shen-ming-zhong-jie',
|
||||
'r3-man-ji-da-lao-hui-xin-shou-cun',
|
||||
'r4-tun-jin-shou-xi-tong',
|
||||
]
|
||||
|
||||
repo_names = {
|
||||
'r3-yue-tang-ping-yue-qiang': '故事1：越躺平越强',
|
||||
'r3-shen-ming-zhong-jie': '故事2：神明中介',
|
||||
'r3-man-ji-da-lao-hui-xin-shou-cun': '故事3：满级大佬回新手村',
|
||||
'r4-tun-jin-shou-xi-tong': '故事4：吞金兽系统',
|
||||
}
|
||||
|
||||
results = {}
|
||||
|
||||
for repo in repos:
|
||||
D = os.path.join('/app/working/workspaces/default/check_tmp', repo)
|
||||
name = repo_names[repo]
|
||||
|
||||
print(f'\n{"="*60}')
|
||||
print(f' {name}')
|
||||
print(f' 仓库: http://git.qihuyun.com/qyyfw/{repo}')
|
||||
print(f'{"="*60}')
|
||||
|
||||
# 1. 文件完整性
|
||||
files = sorted([f for f in os.listdir(D) if f.endswith('.md') and '简介' in f])
|
||||
ch_files = sorted([f for f in os.listdir(D) if f.endswith('.md') and f.startswith('第')])
|
||||
|
||||
print(f'\n 📋 章节完整性: {len(ch_files)}章')
|
||||
if len(ch_files) != 15:
|
||||
print(f' ❌ 应15章，实{len(ch_files)}章')
|
||||
else:
|
||||
print(f' ✅ 15章齐全')
|
||||
|
||||
# 2. 汉字字数
|
||||
print(f'\n 📊 汉字字数:')
|
||||
total_h = 0
|
||||
all_ok = True
|
||||
low_chs = []
|
||||
for f in ch_files:
|
||||
with open(os.path.join(D, f), 'r', encoding='utf-8') as fp:
|
||||
h = len(rr.findall(fp.read()))
|
||||
total_h += h
|
||||
if h < 2000:
|
||||
all_ok = False
|
||||
low_chs.append((f, h))
|
||||
print(f' ❌ {f}: {h} (不足2000)')
|
||||
|
||||
avg = total_h // len(ch_files) if ch_files else 0
|
||||
if all_ok:
|
||||
print(f' ✅ 全部≥2000，总计{total_h}汉字，平均{avg}字/章')
|
||||
else:
|
||||
print(f' ⚠️ {len(low_chs)}章不足2000，总计{total_h}汉字')
|
||||
for f, h in low_chs:
|
||||
print(f' 需补+{2000-h}: {f}')
|
||||
|
||||
# 3. 敏感词
|
||||
print(f'\n 🔍 敏感词检查:')
|
||||
sensitive_words = ['裁员', '被裁', '辞退', '集团', '企业']
|
||||
# '公司'特殊处理：仅标记负面上下文
|
||||
has_issues = False
|
||||
for f in ch_files:
|
||||
with open(os.path.join(D, f), 'r', encoding='utf-8') as fp:
|
||||
c = fp.read()
|
||||
for w in sensitive_words:
|
||||
if w in c:
|
||||
print(f' ❌ {f}: 含敏感词「{w}」')
|
||||
has_issues = True
|
||||
|
||||
# 检查'公司'是否出现在负面上下文
|
||||
for f in ch_files:
|
||||
with open(os.path.join(D, f), 'r', encoding='utf-8') as fp:
|
||||
lines = fp.readlines()
|
||||
for i, l in enumerate(lines):
|
||||
if '公司' in l:
|
||||
# 简单检查上下文是否负面
|
||||
negative_indicators = ['倒闭', '破产', '裁员', '压榨', '剥削', '违法', '纠纷', '官司', '欠薪', '跑路']
|
||||
context = l + (lines[i+1] if i+1 < len(lines) else '') + (lines[i-1] if i > 0 else '')
|
||||
for ni in negative_indicators:
|
||||
if ni in context:
|
||||
print(f' ⚠️ {f}: 「公司」在负面上下文(行{i+1}) - {ni}')
|
||||
has_issues = True
|
||||
break
|
||||
|
||||
if not has_issues:
|
||||
print(f' ✅ 零敏感词问题')
|
||||
|
||||
# 4. AI味检查
|
||||
print(f'\n 🤖 AI味检查:')
|
||||
ai_issues = []
|
||||
for f in ch_files:
|
||||
with open(os.path.join(D, f), 'r', encoding='utf-8') as fp:
|
||||
lines = fp.readlines()
|
||||
c = ''.join(lines)
|
||||
issues = []
|
||||
|
||||
cnt = c.count('燃点')
|
||||
if cnt: issues.append(f'燃点×{cnt}')
|
||||
|
||||
cnt = len(re.findall('深吸[了一]*口气', c))
|
||||
if cnt > 2: issues.append(f'深吸气×{cnt}(>2)')
|
||||
elif cnt > 0: issues.append(f'深吸气×{cnt}')
|
||||
|
||||
cnt = len(re.findall('(握紧|攥紧)', c))
|
||||
if cnt > 4: issues.append(f'握紧/攥紧×{cnt}(>4)')
|
||||
elif cnt > 0: issues.append(f'握紧/攥紧×{cnt}')
|
||||
|
||||
cnt = sum(1 for l in lines if l.strip() == '——' and len(l.strip()) == 2)
|
||||
if cnt: issues.append(f'独立——行×{cnt}')
|
||||
|
||||
cnt = c.count('（全书完）')
|
||||
if cnt > 1: issues.append(f'（全书完）重复×{cnt}')
|
||||
|
||||
cnt = len(re.findall(r'第.*章.*完|本.*章.*完|本章完', c))
|
||||
if cnt: issues.append(f'章末标注×{cnt}')
|
||||
|
||||
cnt = len(re.findall('(在我看来|不得不说|毫不夸张地说|可想而知|毋庸置疑)', c))
|
||||
if cnt > 3: issues.append(f'meta自嘲×{cnt}(>3)')
|
||||
elif cnt > 0: issues.append(f'meta自嘲×{cnt}')
|
||||
|
||||
if issues:
|
||||
ai_issues.append(f'{f}: {" | ".join(issues)}')
|
||||
|
||||
if ai_issues:
|
||||
for s in ai_issues:
|
||||
print(f' ⚠️ {s}')
|
||||
else:
|
||||
print(f' ✅ 零AI味问题')
|
||||
|
||||
# 5. 开篇核弹 (第1章前500字)
|
||||
print(f'\n 💣 开篇核弹V2.0检查:')
|
||||
if ch_files:
|
||||
with open(os.path.join(D, ch_files[0]), 'r', encoding='utf-8') as fp:
|
||||
ch1 = fp.read()
|
||||
|
||||
first_100 = ch1[:300] # about 100 hanzi
|
||||
first_300 = ch1[:900] # about 300 hanzi
|
||||
first_500 = ch1[:1500] # about 500 hanzi
|
||||
|
||||
h_100 = len(rr.findall(first_100))
|
||||
h_300 = len(rr.findall(first_300))
|
||||
h_500 = len(rr.findall(first_500))
|
||||
|
||||
print(f' 前100字区域({h_100}汉字): {"✅" if h_100 >= 50 else "❌"}')
|
||||
print(f' 前300字区域({h_300}汉字): {"✅" if h_300 >= 200 else "❌"}')
|
||||
print(f' 前500字区域({h_500}汉字): {"✅" if h_500 >= 400 else "❌"}')
|
||||
|
||||
# 检查前100字是否有冲突
|
||||
conflict_keywords = ['分手', '被甩', '完蛋', '完了', '被裁', '开除', '被骂', '破产', '欠债', '被嘲', '被笑', '被打了', '冲突', '矛盾', '决裂', '背叛', '被坑', '被骗']
|
||||
has_conflict = any(kw in first_100 for kw in conflict_keywords)
|
||||
print(f' 前100字出冲突: {"✅ 有" if has_conflict else "⚠️ 未检测到明确冲突"}')
|
||||
|
||||
# 检查前300字是否兑现书名
|
||||
print(f' 前300字兑现书名: 需人工判断标题核心要素是否出现')
|
||||
|
||||
# 差异化钩子
|
||||
spec_hooks = ['系统', '觉醒', '金手指', '绑定', '激活', '穿越', '重生', '特殊能力', '不一样', '别人不知道']
|
||||
has_hook = any(kw in first_500 for kw in spec_hooks)
|
||||
print(f' 前500字差异化钩子: {"✅ 有" if has_hook else "⚠️ 未检测到明确钩子"}')
|
||||
|
||||
# 6. 剧情连贯性抽样
|
||||
print(f'\n 🔗 剧情连贯性(抽样):')
|
||||
sample_gaps = []
|
||||
for i in range(1, len(ch_files)):
|
||||
with open(os.path.join(D, ch_files[i-1]), 'r', encoding='utf-8') as fp:
|
||||
prev_end = fp.readlines()[-5:]
|
||||
with open(os.path.join(D, ch_files[i]), 'r', encoding='utf-8') as fp:
|
||||
curr_start = fp.readlines()[:5]
|
||||
|
||||
prev_last = ''.join(prev_end)[-200:]
|
||||
curr_first = ''.join(curr_start)[:200]
|
||||
|
||||
# 简单检查是否有明显断裂（主题跳跃）
|
||||
# 仅做提示
|
||||
print(f' 章与章之间剧情连续(需人工通读确认)')
|
||||
|
||||
# 综合评级
|
||||
score = 0
|
||||
if len(ch_files) == 15: score += 20
|
||||
if all_ok: score += 30
|
||||
if not has_issues: score += 20
|
||||
if not ai_issues: score += 20
|
||||
|
||||
grade = 'S' if score >= 85 else ('A' if score >= 70 else 'B')
|
||||
print(f'\n 🏆 综合评分: {score}/90 → {grade}级')
|
||||
results[repo] = {'score': score, 'grade': grade, 'total_h': total_h, 'avg': avg}
|
||||
|
||||
print(f'\n{"="*60}')
|
||||
print(f' 第三轮4部短故事全面检查报告')
|
||||
print(f'{"="*60}')
|
||||
print(f'{"故事":<20} {"汉字":<8} {"平均":<8} {"评分":<8} {"等级":<8}')
|
||||
print(f'{"-"*52}')
|
||||
for repo, r in results.items():
|
||||
n = repo_names[repo]
|
||||
print(f'{n:<20} {r["total_h"]:<8} {r["avg"]:<8} {r["score"]:<8} {r["grade"]:<8}')
|
||||
print(f'{"-"*52}')
|
||||
Reference in new issue
Block a user