feat(asl): Implement full-text screening core LLM service and validation system (Day 1-3)

Core Components:
- PDFStorageService with Dify/OSS adapters
- LLM12FieldsService with Nougat-first + dual-model + 3-layer JSON parsing
- PromptBuilder for dynamic prompt assembly
- MedicalLogicValidator with 5 rules + fault tolerance
- EvidenceChainValidator for citation integrity
- ConflictDetectionService for dual-model comparison

Prompt Engineering:
- System Prompt (6601 chars, Section-Aware strategy)
- User Prompt template (PICOS context injection)
- JSON Schema (12 fields constraints)
- Cochrane standards (not loaded in MVP)

Key Innovations:
- 3-layer JSON parsing (JSON.parse + json-repair + code block extraction)
- Promise.allSettled for dual-model fault tolerance
- safeGetFieldValue for robust field extraction
- Mixed CN/EN token calculation

Integration Tests:
- integration-test.ts (full test)
- quick-test.ts (quick test)
- cached-result-test.ts (fault tolerance test)

Documentation Updates:
- Development record (Day 2-3 summary)
- Quality assurance strategy (full-text screening)
- Development plan (progress update)
- Module status (v1.1 update)
- Technical debt (10 new items)

Test Results:
- JSON parsing success rate: 100%
- Medical logic validation: 5/5 passed
- Dual-model parallel processing: OK
- Cost per PDF: CNY 0.10

Files: 238 changed, 14383 insertions(+), 32 deletions(-)
Docs: docs/03-涓氬姟妯″潡/ASL-AI鏅鸿兘鏂囩尞/05-寮€鍙戣褰?2025-11-22_Day2-Day3_LLM鏈嶅姟涓庨獙璇佺郴缁熷紑鍙?md

docs/03-业务模块/ASL-AI智能文献/01-需求分析/全文复筛及全文提取模版.txt

@@ -0,0 +1,27 @@
+全文复筛及提取模版
+
+1. 文献来源：
+2. 研究类型
+3.  研究设计细节
+       （1）随访时间
+       （2）数据来源
+4.  疾病诊断标准
+5.  人群特征
+        （1）样本量
+        （2）人口统计学
+6.   基线数据
+         （1）主要功能指标（如果是肾病，那么就是肾功能指标）
+          （2）合并症
+7.   干预措施
+         （1）药物类别
+           （2）剂量与疗程，与药物类别相对应
+8.  对照措施
+9.  结局指标
+         （1）主要结局
+          （2）次要结局
+10.  统计方法
+11.  质量评价
+12.  其他
+      （1）数据来源
+      （2）与Protocol匹配度
+       （3）利益冲突