完善了识别逻辑，允许轻微改动需求描述

README.md

@@ -10,6 +10,8 @@
 - **智能过滤**：自动过滤系统描述、重复需求等非需求内容
 - **结构化输出**：按章节层次组织的JSON格式输出
 - **表格需求识别**：支持从表格中提取功能/接口/其他需求
+- **PDF表格提取**：支持从PDF中提取表格并自动挂接到章节
+- **长句原子拆分**：自动将包含多个需求点的长句拆分为多个可验证需求项
 
 ## 快速开始
 
@@ -20,6 +22,9 @@ pip install -r requirements.txt
 
 # 如果使用LLM功能，还需安装：
 pip install dashscope
+
+# 若需增强PDF表格提取能力（requirements.txt已包含）
+pip install pdfplumber
 ```
 
 ### 配置API密钥（LLM模式）
@@ -41,7 +46,7 @@ llm:
 
 ```bash
 # LLM增强模式
-python main.py -i DC-SRS.pdf -o output.json
+python main.py -i ".\input\DC-SRS.pdf" -o ".\output\output.json"
 
 # 纯规则模式（不使用LLM）
 python main.py -i DC-SRS.pdf -o output.json --no-llm

config.yaml

@@ -8,7 +8,7 @@ llm:
   # LLM提供商：qwen（阿里云千问）
   provider: "qwen"
   # 模型名称
-  model: "qwen3-max"
+  model: "qwen3-max-2026-01-23"
   # API密钥（建议使用环境变量 DASHSCOPE_API_KEY）
   api_key: "sk-7097f7842f724f0c9e70c4bf3b16dacb"
   # 可选参数
@@ -66,6 +66,25 @@ extraction:
       prefix: "OR"
       keywords: ["约束", "资源", "适应性", "保密", "环境", "计算机", "质量", "设计", "人员", "培训", "保障", "验收", "交付"]
       priority: 6
+  splitter:
+    enabled: true
+    max_sentence_len: 120
+    min_clause_len: 12
+  semantic_guard:
+    enabled: true
+    preserve_condition_action_chain: true
+    preserve_alarm_chain: true
+  table_strategy:
+    llm_semantic_enabled: true
+    sequence_table_merge: "single_requirement"
+    merge_time_series_rows_min: 3
+  rewrite_policy:
+    llm_light_rewrite_enabled: true
+    preserve_ratio_min: 0.65
+    max_length_growth_ratio: 1.25
+  renumber_policy:
+    enabled: true
+    mode: "section_continuous"
 
 # 输出配置
 output:

requirements.txt

@@ -1,5 +1,6 @@
 python-docx==0.8.11
 PyPDF2==3.0.1
+pdfplumber==0.11.4
 pyyaml==6.0
 requests==2.31.0
 dashscope==1.7.0

src/__init__.py

@@ -10,11 +10,17 @@ from .document_parser import DocumentParser
 from .llm_interface import LLMInterface, QwenLLM
 from .requirement_extractor import RequirementExtractor
 from .json_generator import JSONGenerator
+from .settings import AppSettings
+from .requirement_splitter import RequirementSplitter
+from .requirement_id_generator import RequirementIDGenerator
 
 __all__ = [
     'DocumentParser',
     'LLMInterface',
     'QwenLLM',
     'RequirementExtractor',
-    'JSONGenerator'
+    'JSONGenerator',
+    'AppSettings',
+    'RequirementSplitter',
+    'RequirementIDGenerator',
 ]

src/document_parser.py

@@ -7,8 +7,9 @@
 import os
 import re
 import logging
+import importlib
 from abc import ABC, abstractmethod
-from typing import List, Dict, Tuple, Optional
+from typing import List, Dict, Tuple, Optional, Any
 from pathlib import Path
 
 try:
@@ -23,6 +24,8 @@ try:
 except ImportError:
     HAS_PDF = False
 
+HAS_PDF_TABLE = importlib.util.find_spec("pdfplumber") is not None
+
 logger = logging.getLogger(__name__)
 
 
@@ -38,19 +41,28 @@ class Section:
         self.parent = None
         self.children = []
         self.tables = []
+        self.blocks = []
     
     def add_child(self, child: 'Section') -> None:
         self.children.append(child)
         child.parent = self
     
     def add_content(self, text: str) -> None:
+        text = (text or "").strip()
+        if not text:
+            return
         if self.content:
             self.content += "\n" + text
         else:
             self.content = text
+        self.blocks.append({"type": "text", "text": text})
     
     def add_table(self, table_data: List[List[str]]) -> None:
+        if not table_data:
+            return
         self.tables.append(table_data)
+        table_index = len(self.tables) - 1
+        self.blocks.append({"type": "table", "table_index": table_index, "table": table_data})
     
     def generate_auto_number(self, parent_number: str = "", sibling_index: int = 1) -> None:
         """
@@ -332,6 +344,7 @@ class PDFParser(DocumentParser):
             raise ImportError("PyPDF2库未安装，请运行: pip install PyPDF2")
         super().__init__(file_path)
         self.document_title = "SRS Document"
+        self._page_texts: List[str] = []
     
     def parse(self) -> List[Section]:
         """解析PDF文档"""
@@ -349,7 +362,19 @@ class PDFParser(DocumentParser):
             if self.llm:
                 self.sections = self._llm_validate_sections(self.sections)
 
-            # 5. 为没有编号的章节自动生成编号
+            # 章节识别失败时，创建兜底章节避免后续表格数据丢失。
+            if not self.sections:
+                fallback = Section(level=1, title="未命名章节", number="1", uid=self._next_uid())
+                if cleaned_text:
+                    fallback.add_content(cleaned_text)
+                self.sections = [fallback]
+
+            # 5. 提取并挂接PDF表格到章节（若依赖可用）
+            pdf_tables = self._extract_pdf_tables()
+            if pdf_tables:
+                self._attach_pdf_tables_to_sections(pdf_tables)
+            
+            # 6. 为没有编号的章节自动生成编号
             self._auto_number_sections(self.sections)
             
             logger.info(f"完成PDF解析，提取{len(self.sections)}个顶级章节")
@@ -368,8 +393,99 @@ class PDFParser(DocumentParser):
                 text = page.extract_text()
                 if text:
                     all_text.append(text)
+        self._page_texts = all_text
         return '\n'.join(all_text)
 
+    def _extract_pdf_tables(self) -> List[Dict[str, Any]]:
+        """提取PDF中的表格数据。"""
+        if not HAS_PDF_TABLE:
+            logger.warning("未安装pdfplumber，跳过PDF表格提取。可执行: pip install pdfplumber")
+            return []
+
+        tables: List[Dict[str, Any]] = []
+        try:
+            pdfplumber = importlib.import_module("pdfplumber")
+            with pdfplumber.open(self.file_path) as pdf:
+                for page_idx, page in enumerate(pdf.pages):
+                    page_text = ""
+                    if page_idx < len(self._page_texts):
+                        page_text = self._page_texts[page_idx]
+
+                    extracted_tables = page.extract_tables() or []
+                    for table_idx, table in enumerate(extracted_tables):
+                        cleaned_table: List[List[str]] = []
+                        for row in table or []:
+                            cells = [re.sub(r'\s+', ' ', str(cell or '')).strip() for cell in row]
+                            if any(cells):
+                                cleaned_table.append(cells)
+
+                        if cleaned_table:
+                            tables.append(
+                                {
+                                    "page_idx": page_idx,
+                                    "table_idx": table_idx,
+                                    "page_text": page_text,
+                                    "data": cleaned_table,
+                                }
+                            )
+        except Exception as e:
+            logger.warning(f"PDF表格提取失败，继续纯文本流程: {e}")
+            return []
+
+        logger.info(f"PDF表格提取完成，共{len(tables)}个表格")
+        return tables
+
+    def _attach_pdf_tables_to_sections(self, tables: List[Dict[str, Any]]) -> None:
+        """将提取出的PDF表格挂接到最匹配的章节。"""
+        flat_sections = self._flatten_sections(self.sections)
+        if not flat_sections:
+            return
+
+        last_section: Optional[Section] = None
+        for table in tables:
+            matched = self._match_table_section(table.get("page_text", ""), flat_sections)
+            target = matched or last_section or flat_sections[0]
+            target.add_table(table["data"])
+            last_section = target
+
+    def _flatten_sections(self, sections: List[Section]) -> List[Section]:
+        """按文档顺序拉平章节树。"""
+        result: List[Section] = []
+        for section in sections:
+            result.append(section)
+            if section.children:
+                result.extend(self._flatten_sections(section.children))
+        return result
+
+    def _match_table_section(self, page_text: str, sections: List[Section]) -> Optional[Section]:
+        """基于页文本匹配表格归属章节。"""
+        normalized_page = re.sub(r"\s+", "", (page_text or "")).lower()
+        if not normalized_page:
+            return None
+
+        matched: Optional[Section] = None
+        matched_score = -1
+        for section in sections:
+            title = (section.title or "").strip()
+            if not title:
+                continue
+
+            number = (section.number or "").strip()
+            candidates = [title]
+            if number:
+                candidates.append(f"{number}{title}")
+                candidates.append(f"{number} {title}")
+
+            for candidate in candidates:
+                normalized_candidate = re.sub(r"\s+", "", candidate).lower()
+                if normalized_candidate and normalized_candidate in normalized_page:
+                    score = len(normalized_candidate)
+                    if score > matched_score:
+                        matched = section
+                        matched_score = score
+
+        return matched
+    
     def _clean_text(self, text: str) -> str:
         """清洗PDF提取的文本"""
         lines = text.split('\n')
@@ -494,11 +610,7 @@ class PDFParser(DocumentParser):
         if len(title) > 60 or len(title) < 2:
             return None
         
-        # 标题必须包含中文
+        # 放宽标题字符要求（兼容部分PDF字体导致中文抽取异常的情况）
-        if not re.search(r'[\u4e00-\u9fa5]', title):
-            return None
-        
-        # 放宽标题关键词要求（非严格GJB结构）
         if not re.search(r'[\u4e00-\u9fa5A-Za-z]', title):
             return None
         

src/json_generator.py

@@ -10,6 +10,7 @@ from datetime import datetime
 from typing import List, Dict, Any, Optional
 from .document_parser import Section
 from .requirement_extractor import Requirement
+from .settings import AppSettings
 
 logger = logging.getLogger(__name__)
 
@@ -17,25 +18,9 @@ logger = logging.getLogger(__name__)
 class JSONGenerator:
     """JSON输出生成器"""
     
-    # 需求类型中文映射
-    TYPE_CHINESE = {
-        'functional': '功能需求',
-        'interface': '接口需求',
-        'performance': '其他需求',
-        'security': '其他需求',
-        'reliability': '其他需求',
-        'other': '其他需求'
-    }
-    
-    # 非需求章节（不输出到JSON）
-    NON_REQUIREMENT_SECTIONS = [
-        '标识', '系统概述', '文档概述', '引用文档',
-        '合格性规定', '需求可追踪性', '注释', '附录',
-        '范围', '概述'
-    ]
-    
     def __init__(self, config: Dict = None):
         self.config = config or {}
+        self.settings = AppSettings(self.config)
     
     def generate(self, sections: List[Section], requirements: List[Requirement],
                  document_title: str = "SRS Document") -> Dict[str, Any]:
@@ -84,7 +69,7 @@ class JSONGenerator:
         """计算需求类型统计"""
         stats = {}
         for req in requirements:
-            type_chinese = self.TYPE_CHINESE.get(req.type, '其他需求')
+            type_chinese = self.settings.type_chinese.get(req.type, '其他需求')
             if type_chinese not in stats:
                 stats[type_chinese] = 0
             stats[type_chinese] += 1
@@ -92,12 +77,7 @@ class JSONGenerator:
     
     def _should_include_section(self, section: Section) -> bool:
         """判断章节是否应该包含在输出中"""
-        # 排除非需求章节
+        return not self.settings.is_non_requirement_section(section.title)
-        for keyword in self.NON_REQUIREMENT_SECTIONS:
-            if keyword in section.title:
-                return False
-        
-        return True
     
     def _build_requirement_content(self, sections: List[Section],
                                    reqs_by_section: Dict[str, List[Requirement]]) -> Dict[str, Any]:
@@ -151,11 +131,12 @@ class JSONGenerator:
         
         # 添加当前章节需求
         reqs = reqs_by_section.get(section.uid or section.number or 'unknown', [])
+        reqs = sorted(reqs, key=lambda r: getattr(r, 'source_order', 0))
         if reqs:
             result["需求列表"] = []
             for req in reqs:
                 # 需求类型放在最前面
-                type_chinese = self.TYPE_CHINESE.get(req.type, '功能需求')
+                type_chinese = self.settings.type_chinese.get(req.type, '功能需求')
                 req_dict = {
                     "需求类型": type_chinese,
                     "需求编号": req.id,
@@ -188,8 +169,11 @@ class JSONGenerator:
             file_path: 输出文件路径
         """
         try:
+            output_cfg = self.config.get("output", {})
+            indent = output_cfg.get("indent", 2)
+            pretty = output_cfg.get("pretty_print", True)
             with open(file_path, 'w', encoding='utf-8') as f:
-                json.dump(output, f, ensure_ascii=False, indent=2)
+                json.dump(output, f, ensure_ascii=False, indent=indent if pretty else None)
             logger.info(f"成功保存JSON到: {file_path}")
         except Exception as e:
             logger.error(f"保存JSON文件失败: {e}")

src/requirement_extractor.py

@@ -9,6 +9,9 @@ import json
 import logging
 from typing import List, Dict, Optional, Tuple, Any
 from .document_parser import Section
+from .settings import AppSettings
+from .requirement_id_generator import RequirementIDGenerator
+from .requirement_splitter import RequirementSplitter
 
 logger = logging.getLogger(__name__)
 
@@ -20,7 +23,9 @@ class Requirement:
                  section_number: str = "", section_title: str = "",
                  interface_name: str = "", interface_type: str = "",
                  section_uid: str = "",
-                 source: str = "", destination: str = ""):
+                 source: str = "", destination: str = "",
+                 source_type: str = "text", source_order: int = 0,
+                 source_table_index: int = -1, source_row_span: str = ""):
         self.id = req_id
         self.description = description
         self.type = req_type
@@ -32,6 +37,10 @@ class Requirement:
         self.interface_type = interface_type
         self.source = source
         self.destination = destination
+        self.source_type = source_type
+        self.source_order = source_order
+        self.source_table_index = source_table_index
+        self.source_row_span = source_row_span
     
     def to_dict(self) -> Dict:
         result = {
@@ -53,35 +62,20 @@ class Requirement:
 class RequirementExtractor:
     """需求提取器 - LLM增强版"""
     
-    # 需求类型前缀映射
-    TYPE_PREFIX = {
-        'functional': 'FR',
-        'interface': 'IR',
-        'performance': 'PR',
-        'security': 'SR',
-        'reliability': 'RR',
-        'other': 'OR'
-    }
-    
-    # 中文类型到英文的映射
-    TYPE_MAPPING = {
-        '功能需求': 'functional',
-        '接口需求': 'interface',
-        '其他需求': 'other'
-    }
-    
-    # 非需求章节（应该跳过的）
-    NON_REQUIREMENT_SECTIONS = [
-        '标识', '系统概述', '文档概述', '引用文档',
-        '合格性规定', '需求可追踪性', '注释', '附录',
-        '范围', '概述'
-    ]
-    
     def __init__(self, config: Dict = None, llm=None):
         self.config = config or {}
         self.llm = llm
+        self.settings = AppSettings(self.config)
+        self.id_generator = RequirementIDGenerator(self.settings.type_prefix)
+        self.splitter = None
+        if self.settings.splitter_enabled:
+            self.splitter = RequirementSplitter(
+                max_sentence_len=self.settings.splitter_max_sentence_len,
+                min_clause_len=self.settings.splitter_min_clause_len,
+            )
         self.requirements: List[Requirement] = []
         self._req_counters: Dict[str, Dict[str, int]] = {}  # {section_number: {type: count}}
+        self._global_order = 0
     
     def extract_from_sections(self, sections: List[Section]) -> List[Requirement]:
         """
@@ -95,10 +89,15 @@ class RequirementExtractor:
         """
         self.requirements = []
         self._req_counters = {}
+        self._global_order = 0
         
         for section in sections:
             self._process_section(section)
 
+        # 去重后统一连续重编号，避免出现跳号。
+        if self.settings.renumber_enabled:
+            self.requirements = self._renumber_requirements_continuous(self.requirements)
+        
         logger.info(f"共提取 {len(self.requirements)} 个需求项")
         return self.requirements
     
@@ -121,9 +120,7 @@ class RequirementExtractor:
     
     def _should_skip_section(self, section: Section) -> bool:
         """判断是否应该跳过此章节"""
-        # 检查标题是否包含非需求关键词
+        if self.settings.is_non_requirement_section(section.title):
-        for keyword in self.NON_REQUIREMENT_SECTIONS:
-            if keyword in section.title:
             return True
         
         # 检查是否是系统描述章节（如3.1.1通常是系统描述）
@@ -169,22 +166,96 @@ class RequirementExtractor:
         return '是' in response
     
     def _extract_requirements_from_section(self, section: Section) -> List[Requirement]:
-        """从单个章节提取需求"""
+        """从单个章节按文档顺序提取需求。"""
-        requirements = []
+        requirements: List[Requirement] = []
-        
-        # 获取需求类型
         req_type = self._identify_requirement_type(section.title, section.content)
 
-        if self.llm:
+        blocks = self._iter_section_blocks(section)
-            # 使用LLM提取需求
+        for block in blocks:
-            reqs = self._llm_extract_requirements(section, req_type)
+            block_type = block.get("type", "text")
-            requirements.extend(reqs)
+            block_order = int(block.get("order", 0))
-        else:
-            # 使用规则提取
-            reqs = self._rule_extract_requirements(section, req_type)
-            requirements.extend(reqs)
 
-        return requirements
+            temp_section = Section(
+                level=section.level,
+                title=section.title,
+                number=section.number,
+                content="",
+                uid=section.uid,
+            )
+
+            if block_type == "text":
+                temp_section.content = block.get("text", "")
+                if self.llm:
+                    block_reqs = self._llm_extract_requirements(temp_section, req_type)
+                else:
+                    block_reqs = self._rule_extract_requirements(temp_section, req_type)
+                table_index = -1
+            else:
+                table_data = block.get("table", [])
+                temp_section.tables = [table_data] if table_data else []
+                table_index = int(block.get("table_index", -1))
+                if self.llm and self.settings.table_llm_semantic_enabled:
+                    block_reqs = self._llm_extract_table_requirements(temp_section, req_type)
+                else:
+                    block_reqs = self._rule_extract_requirements(temp_section, req_type)
+
+            for req in block_reqs:
+                self._global_order += 1
+                req.source_type = block_type
+                req.source_order = self._global_order
+                req.source_table_index = table_index
+                req.source_row_span = block.get("row_span", "")
+                req.description = self._maybe_light_rewrite(req.description, block_type)
+                requirements.append(req)
+
+        requirements = self._semantic_integrity_postprocess(requirements)
+        return self._deduplicate_requirements(requirements)
+
+    def _iter_section_blocks(self, section: Section) -> List[Dict[str, Any]]:
+        """返回章节中的顺序块（文本/表格）。"""
+        blocks: List[Dict[str, Any]] = []
+        if getattr(section, "blocks", None):
+            for idx, block in enumerate(section.blocks, 1):
+                block_type = block.get("type")
+                if block_type == "text":
+                    text = (block.get("text") or "").strip()
+                    if text:
+                        blocks.append({"type": "text", "text": text, "order": idx})
+                elif block_type == "table":
+                    table = block.get("table")
+                    table_index = int(block.get("table_index", -1))
+                    if table_index >= 0 and table_index < len(section.tables):
+                        table = section.tables[table_index]
+                    if table:
+                        blocks.append(
+                            {
+                                "type": "table",
+                                "table": table,
+                                "table_index": table_index,
+                                "order": idx,
+                            }
+                        )
+
+        if blocks:
+            return blocks
+
+        # 兼容旧解析结果：无顺序块时退化为文本后表格。
+        fallback_order = 1
+        text = (section.content or "").strip()
+        if text:
+            blocks.append({"type": "text", "text": text, "order": fallback_order})
+            fallback_order += 1
+        for table_index, table in enumerate(section.tables):
+            blocks.append(
+                {
+                    "type": "table",
+                    "table": table,
+                    "table_index": table_index,
+                    "order": fallback_order,
+                }
+            )
+            fallback_order += 1
+        return blocks
     
     def _llm_extract_requirements(self, section: Section, req_type: str) -> List[Requirement]:
         """使用LLM提取需求"""
@@ -242,8 +313,8 @@ class RequirementExtractor:
 
 JSON输出："""
         else:
-            # 功能需求、其他需求：保留原文描述，不改写润色
+            # 功能需求、其他需求：以原文为主，允许轻微扩写补全
-            prompt = f"""请从以下SRS文档章节中提取具体的软件需求。保持原文描述，不要改写或润色。
+            prompt = f"""请从以下SRS文档章节中提取具体的软件需求。以原文为主，允许轻微扩写补全语义。
 
 章节编号：{section.number}
 章节标题：{section.title}
@@ -256,11 +327,14 @@ JSON输出："""
 提取要求：
 1. 同时提取正文与表格中的具体、可验证的软件需求
 2. 不要提取系统描述、背景说明等非需求内容
-3. 保持原文描述，不要对需求进行改写、润色或重新组织
+3. 需求描述应保留原文大部分词语（建议保留率>=70%），仅做轻微补充以增强语义完整性
-4. 去除原文中的多余换行符和表格格式符号，但保留语句内容
+4. 严禁改变任何数值、阈值、状态名、信号名和逻辑条件
+5. 去除原文中的多余换行符和表格格式符号，但保留语句内容
 5. 每条需求应该是完整的句子
 6. 如果有多条需求，请分别列出
-7. 如果一段需求描述内有多条需求，请尽量拆分成独立的需求项
+7. 如果一段需求描述内有多条需求点，必须拆分成多个独立需求项
+8. 拆分判定：出现“并/并且/同时/然后/且/以及”，或一条句子中出现多个动作（如判断+监测+发送）时必须拆分
+9. 每条需求尽量满足“单一动作、可单独验证”
 8. 过滤重复或过于相似的需求，只保留独特的需求
 9. 若原文给出需求编号，请优先使用原文编号（req_id）
 
@@ -300,14 +374,26 @@ JSON输出："""
                     if desc and len(desc) > 5:
                         # 清理描述中的多余换行符和表格符号
                         desc = self._clean_description(desc)
+                        split_descs = self._split_requirement_description(desc)
+                        if not split_descs:
+                            split_descs = [desc]
                         
                         # 需求ID优先使用文档给出的编号
                         doc_req_id = self._normalize_req_id(req_data.get('req_id', '') or req_data.get('id', ''))
                         if not doc_req_id:
                             doc_req_id, desc = self._extract_requirement_id_from_text(desc)
 
-                        # 生成最终的需求ID（三级优先级）
+                        for split_idx, split_desc in enumerate(split_descs, 1):
-                        req_id = self._generate_requirement_id(req_type, section.number, i, doc_req_id, parent_req_id)
+                            # 生成最终的需求ID（支持拆分后后缀）
+                            req_id = self._generate_requirement_id(
+                                req_type,
+                                section.number,
+                                i,
+                                doc_req_id,
+                                parent_req_id,
+                                split_idx,
+                                len(split_descs),
+                            )
 
                             # 接口需求提取额外字段
                             interface_name = ""
@@ -322,7 +408,7 @@ JSON输出："""
 
                             req = Requirement(
                                 req_id=req_id,
-                            description=desc,
+                                description=split_desc,
                                 req_type=req_type,
                                 section_number=section.number,
                                 section_title=section.title,
@@ -339,6 +425,223 @@ JSON输出："""
         
         return requirements
 
+    def _build_table_requirements_rule(self, section: Section, req_type: str, start_index: int) -> List[Requirement]:
+        """仅从表格构建规则需求，用于LLM模式补充召回。"""
+        requirements: List[Requirement] = []
+        table_requirements = self._extract_requirements_from_tables_rule(section.tables)
+        if not table_requirements:
+            return requirements
+
+        parent_req_id = ""
+        complete_id_pattern = r'^[A-Za-z0-9]{2,10}[-_].+$'
+        for temp_id, _ in table_requirements:
+            if temp_id and re.match(complete_id_pattern, temp_id):
+                parent_req_id = temp_id.replace('_', '-')
+                break
+
+        index = start_index
+        for doc_req_id, desc in table_requirements:
+            split_descs = self._split_requirement_description(desc)
+            if not split_descs:
+                split_descs = [desc]
+
+            for split_idx, split_desc in enumerate(split_descs, 1):
+                req_id = self._generate_requirement_id(
+                    req_type=req_type,
+                    section_number=section.number,
+                    index=index,
+                    doc_req_id=doc_req_id,
+                    parent_req_id=parent_req_id,
+                    split_index=split_idx,
+                    split_total=len(split_descs),
+                )
+                requirements.append(
+                    Requirement(
+                        req_id=req_id,
+                        description=split_desc,
+                        req_type=req_type,
+                        section_number=section.number,
+                        section_title=section.title,
+                        section_uid=section.uid,
+                    )
+                )
+            index += 1
+
+        return requirements
+
+    def _llm_extract_table_requirements(self, section: Section, req_type: str) -> List[Requirement]:
+        """使用LLM语义化提取表格需求。"""
+        if not self.llm or not section.tables:
+            return self._rule_extract_requirements(section, req_type)
+
+        table = section.tables[0]
+        is_sequence_table = self._is_time_series_table(table)
+        table_text = self._format_tables_for_prompt([table])
+        merge_hint = "是" if is_sequence_table and self.settings.sequence_table_merge == "single_requirement" else "否"
+
+        prompt = f"""请从下列表格中提取并组织软件需求，要求以语义完整的需求句输出。
+
+章节编号：{section.number}
+章节标题：{section.title}
+需求类型：{req_type}
+该表是否按时间序列指令组织：{merge_hint}
+
+表格内容：
+{table_text}
+
+提取规则：
+1. 不是简单逐字抄表格，请结合列含义组织成完整需求句。
+2. 保留原文大部分关键词、阈值、数值、状态名，不得改变逻辑和数值。
+3. 允许轻微补充主语或上下文，使语义更完整。
+4. 若为时间序列指令表，优先合并为1条需求，描述完整执行序列。
+5. 若有明显独立语义点，可输出多条需求。
+
+请输出JSON：
+{{
+  "requirements": [
+    {{"req_id": "可为空", "description": "完整需求描述"}}
+  ]
+}}"""
+
+        try:
+            response = self.llm.call(prompt)
+            data = self._parse_llm_json_response(response)
+            requirements: List[Requirement] = []
+            if data and isinstance(data.get("requirements"), list):
+                for i, req_data in enumerate(data["requirements"], 1):
+                    desc = self._clean_description(req_data.get("description", ""))
+                    if not desc:
+                        continue
+                    doc_req_id = self._normalize_req_id(req_data.get("req_id", ""))
+                    req_id = self._generate_requirement_id(req_type, section.number, i, doc_req_id, "")
+                    requirements.append(
+                        Requirement(
+                            req_id=req_id,
+                            description=desc,
+                            req_type=req_type,
+                            section_number=section.number,
+                            section_title=section.title,
+                            section_uid=section.uid,
+                            source_type="table",
+                        )
+                    )
+
+            if not requirements:
+                return self._rule_extract_requirements(section, req_type)
+            return requirements
+        except Exception as e:
+            logger.warning(f"LLM表格语义化提取失败，回退规则模式: {e}")
+            return self._rule_extract_requirements(section, req_type)
+
+    def _maybe_light_rewrite(self, description: str, source_type: str) -> str:
+        """仅在LLM模式做轻微扩写，且通过保真校验。"""
+        description = self._clean_description(description)
+        if not description:
+            return description
+        if not self.llm or not self.settings.llm_light_rewrite_enabled:
+            return description
+
+        need_rewrite = source_type == "table" or len(description) < 28
+        if not need_rewrite:
+            return description
+
+        prompt = f"""请对下面需求做轻微扩写，使语义更完整。
+
+原文：{description}
+
+要求：
+1. 保留原文大部分表述，不改变核心语义。
+2. 不得修改任何数值、阈值、状态名称、信号名称。
+3. 只允许补充必要主语/宾语，长度尽量控制在原文的1.25倍以内。
+4. 仅返回改写后的单句文本。"""
+
+        try:
+            rewritten = self._clean_description(self.llm.call(prompt))
+            if not rewritten:
+                return description
+
+            preserve_ratio = self._calculate_preserve_ratio(description, rewritten)
+            growth_ratio = len(rewritten) / max(len(description), 1)
+            if preserve_ratio < self.settings.preserve_ratio_min:
+                return description
+            if growth_ratio > self.settings.max_length_growth_ratio:
+                return description
+            if not self._numbers_consistent(description, rewritten):
+                return description
+            return rewritten
+        except Exception:
+            return description
+
+    def _calculate_preserve_ratio(self, original: str, rewritten: str) -> float:
+        original_tokens = [c for c in re.sub(r"\s+", "", original) if c]
+        rewritten_tokens = set(c for c in re.sub(r"\s+", "", rewritten) if c)
+        if not original_tokens:
+            return 1.0
+        hit = sum(1 for c in original_tokens if c in rewritten_tokens)
+        return hit / max(len(original_tokens), 1)
+
+    def _numbers_consistent(self, original: str, rewritten: str) -> bool:
+        pattern = r"[<>≤≥]?\d+(?:\.\d+)?(?:[A-Za-z%]*)"
+        orig_nums = set(re.findall(pattern, original))
+        rewrite_nums = set(re.findall(pattern, rewritten))
+        return orig_nums.issubset(rewrite_nums)
+
+    def _semantic_integrity_postprocess(self, requirements: List[Requirement]) -> List[Requirement]:
+        """语义完整性后处理：合并被误拆的紧耦合需求链。"""
+        if not self.settings.semantic_guard_enabled or not requirements:
+            return requirements
+
+        merged: List[Requirement] = [requirements[0]]
+        for req in requirements[1:]:
+            prev = merged[-1]
+            if self._should_merge_semantic(prev, req):
+                prev.description = self._clean_description(
+                    f"{prev.description.rstrip('；;。')}；{req.description.lstrip('；;。')}"
+                )
+            else:
+                merged.append(req)
+        return merged
+
+    def _should_merge_semantic(self, prev: Requirement, curr: Requirement) -> bool:
+        if prev.section_uid != curr.section_uid or prev.type != curr.type:
+            return False
+
+        prev_desc = prev.description
+        curr_desc = curr.description
+
+        if curr_desc.startswith(("该", "其", "上述", "此", "该报警", "该信号")):
+            return True
+        if self.settings.preserve_alarm_chain and ("报警" in prev_desc and "持续" in curr_desc):
+            return True
+        if self.settings.preserve_condition_action_chain:
+            if "进入整星安全模式" in prev_desc and ("过放电模式" in curr_desc or "发送" in curr_desc):
+                return True
+            if "若蓄电池充电" in prev_desc and (
+                "退出低功耗模式" in curr_desc or "热控" in curr_desc or "姿控" in curr_desc
+            ):
+                return True
+        if ("产生" in prev_desc and "报警" in prev_desc and "持续" in curr_desc):
+            return True
+        return False
+
+    def _renumber_requirements_continuous(self, requirements: List[Requirement]) -> List[Requirement]:
+        """按文档顺序对去重后的需求重新连续编号。"""
+        if not requirements:
+            return requirements
+
+        ordered = sorted(requirements, key=lambda r: (r.source_order, r.section_number or ""))
+        counters: Dict[Tuple[str, str], int] = {}
+
+        for req in ordered:
+            section_key = req.section_uid or req.section_number or "NA"
+            prefix = self.settings.type_prefix.get(req.type, "FR")
+            counter_key = (section_key, prefix)
+            counters[counter_key] = counters.get(counter_key, 0) + 1
+            section_part = req.section_number if req.section_number else "NA"
+            req.id = f"{prefix}-{section_part}-{counters[counter_key]}"
+
+        return ordered
+    
     def _rule_extract_requirements(self, section: Section, req_type: str) -> List[Requirement]:
         """使用规则提取需求（备用方法）"""
         requirements = []
@@ -352,7 +655,7 @@ JSON输出："""
             if not descriptions:
                 # 如果没有列表项，将整个内容作为一个需求
                 desc = self._clean_description(content)
-                if len(desc) > 5:
+                if len(desc) > 5 and not section.tables:
                     descriptions = [f"{section.title}：{desc}"]
         
         # 表格需求
@@ -379,11 +682,23 @@ JSON输出："""
             desc = self._clean_description(desc)
             if len(desc) > 5:
                 doc_req_id, cleaned_desc = self._extract_requirement_id_from_text(desc)
-                # 生成最终的需求ID（三级优先级）
+                split_descs = self._split_requirement_description(cleaned_desc)
-                req_id = self._generate_requirement_id(req_type, section.number, index, doc_req_id, parent_req_id)
+                if not split_descs:
+                    split_descs = [cleaned_desc]
+
+                for split_idx, split_desc in enumerate(split_descs, 1):
+                    req_id = self._generate_requirement_id(
+                        req_type,
+                        section.number,
+                        index,
+                        doc_req_id,
+                        parent_req_id,
+                        split_idx,
+                        len(split_descs),
+                    )
                     req = Requirement(
                         req_id=req_id,
-                    description=cleaned_desc,
+                        description=split_desc,
                         req_type=req_type,
                         section_number=section.number,
                         section_title=section.title,
@@ -393,11 +708,23 @@ JSON输出："""
                 index += 1
         
         for doc_req_id, desc in table_requirements:
-            # 生成最终的需求ID（三级优先级）
+            split_descs = self._split_requirement_description(desc)
-            req_id = self._generate_requirement_id(req_type, section.number, index, doc_req_id, parent_req_id)
+            if not split_descs:
+                split_descs = [desc]
+
+            for split_idx, split_desc in enumerate(split_descs, 1):
+                req_id = self._generate_requirement_id(
+                    req_type,
+                    section.number,
+                    index,
+                    doc_req_id,
+                    parent_req_id,
+                    split_idx,
+                    len(split_descs),
+                )
                 req = Requirement(
                     req_id=req_id,
-                description=desc,
+                    description=split_desc,
                     req_type=req_type,
                     section_number=section.number,
                     section_title=section.title,
@@ -440,21 +767,11 @@ JSON输出："""
         
         注意：不能仅靠标题判断是否为功能需求，若无法识别具体类型，默认为功能需求
         """
-        title_lower = title.lower()
+        return self.settings.detect_requirement_type(title, content)
-        content_lower = (content or "").lower()[:500]  # 只检查前500字符
-        combined_text = title_lower + " " + content_lower
-        
-        # 优先识别接口需求，根据具体文件情况修改关键词
-        interface_keywords = ['接口', 'interface', 'api', '串口', '通信协议', '数据交换']
-        for keyword in interface_keywords:
-            if keyword in combined_text:
-                return 'interface'
-        
-        # 默认为功能需求（不能仅靠标题判断，无法识别时默认为功能需求）
-        return 'functional'
     
     def _generate_requirement_id(self, req_type: str, section_number: str, index: int,
-                                 doc_req_id: str = "", parent_req_id: str = "") -> str:
+                                 doc_req_id: str = "", parent_req_id: str = "",
+                                 split_index: int = 1, split_total: int = 1) -> str:
         """
         生成需求ID（三级优先级）
         
@@ -473,29 +790,19 @@ JSON输出："""
             doc_req_id: 文档中提取的编号/代号
             parent_req_id: 父需求编号（用于子需求）
         """
-        # 优先级1：合法的完整编号（以2-10个字母或数字开头，后跟分隔符）
+        return self.id_generator.generate(
-        if doc_req_id:
+            req_type=req_type,
-            # 检查是否为合法的完整编号格式：2-10个字母或数字开头 + 分隔符 + 其他字符
+            section_number=section_number,
-            # 例如: NY01-01、FR-3.1.2-1、AIRSAT07-GD03-04
+            index=index,
-            complete_id_pattern = r'^[A-Za-z0-9]{2,10}[-_].+$'
+            doc_req_id=doc_req_id,
-            if re.match(complete_id_pattern, doc_req_id):
+            parent_req_id=parent_req_id,
-                return doc_req_id.replace('_', '-')
+            split_index=split_index,
-        
+            split_total=split_total,
-        # 优先级2：代号/序号 + 父需求编号
+        )
-        if doc_req_id and parent_req_id:
-            return f"{parent_req_id}-{doc_req_id}"
-        
-        # 优先级3：自动生成（保留章节号中的点号）
-        prefix = self.TYPE_PREFIX.get(req_type, 'FR')  # 默认FR（功能需求）
-        section_part = section_number if section_number else "NA"
-        return f"{prefix}-{section_part}-{index}"
 
     def _normalize_req_id(self, req_id: str) -> str:
         """规范化需求编号"""
-        if not req_id:
+        return self.id_generator.normalize(req_id)
-            return ""
-        req_id = str(req_id).strip()
-        return req_id
     
     def _clean_description(self, text: str) -> str:
         """清理需求描述"""
@@ -533,29 +840,28 @@ JSON输出："""
         1. 完整编号：NY01-01、FR-3.1.2-1
         2. 代号/序号：K101、D61、a)、1)
         """
+        return self.id_generator.extract_from_text(text)
+
+    def _split_requirement_description(self, text: str) -> List[str]:
         if not text:
-            return None, text
+            return []
+        if "时间序列" in text and "执行指令" in text:
+            return [text]
+        if not self.splitter:
+            return [text]
+        return self.splitter.split(text)
 
-        # 模式1：完整需求编号（如 NY01-01、FR-3.1.2-1）
+    def _deduplicate_requirements(self, requirements: List[Requirement]) -> List[Requirement]:
-        pattern1 = r'^\s*([A-Za-z]{2,6}[-_]\d+(?:[-.\d]+)*)\s*[:：\)\]】]?\s*(.+)$'
+        seen = set()
-        match = re.match(pattern1, text)
+        deduped: List[Requirement] = []
-        if match:
+        for req in requirements:
-            return match.group(1).strip(), match.group(2).strip()
+            normalized_desc = re.sub(r'\s+', ' ', req.description).strip().lower()
-        
+            key = (req.type, normalized_desc)
-        # 模式2：代号（如 K101、D61）
+            if key in seen:
-        pattern2 = r'^\s*([A-Za-z]\d+)\s*[:：\)\]】]?\s*(.+)$'
+                continue
-        match = re.match(pattern2, text)
+            seen.add(key)
-        if match:
+            deduped.append(req)
-            return match.group(1).strip(), match.group(2).strip()
+        return deduped
-        
-        # 模式3：序号（如 a)、1)）
-        pattern3 = r'^\s*([a-z0-9]{1,2}[\)）])\s*(.+)$'
-        match = re.match(pattern3, text)
-        if match:
-            code = match.group(1).strip().rstrip(')）')
-            return code, match.group(2).strip()
-        
-        return None, text
 
     def _extract_requirements_from_tables_rule(self, tables: List[List[List[str]]]) -> List[Tuple[Optional[str], str]]:
         """从表格中提取需求（规则方式）"""
@@ -569,6 +875,13 @@ JSON输出："""
         for table in tables:
             if not table:
                 continue
+
+            if self._is_time_series_table(table) and self.settings.sequence_table_merge == "single_requirement":
+                merged_desc = self._build_sequence_table_requirement(table)
+                if merged_desc:
+                    results.append((None, merged_desc))
+                continue
+
             header = table[0] if table else []
             header_lower = [h.lower() for h in header]
             id_idx = None
@@ -606,6 +919,58 @@ JSON输出："""
         
         return results
 
+    def _is_time_series_table(self, table: List[List[str]]) -> bool:
+        if not table:
+            return False
+
+        header = " ".join(cell for cell in table[0] if cell)
+        header_has_time = any(k in header for k in ["时间", "时刻", "time", "TIME", "T0"])
+        header_has_action = any(k in header for k in ["指令", "动作", "行为", "操作", "名称"])
+
+        time_pattern = re.compile(r"^T\s*0(?:\s*[+＋-]\s*\d+\s*[sS秒]?)?$")
+        data_rows = table[1:] if len(table) > 1 else []
+        time_like_rows = 0
+        for row in data_rows:
+            if not row:
+                continue
+            first_cell = (row[0] or "").strip() if row else ""
+            if time_pattern.match(first_cell):
+                time_like_rows += 1
+
+        return (header_has_time and header_has_action) or (time_like_rows >= self.settings.merge_time_series_rows_min)
+
+    def _build_sequence_table_requirement(self, table: List[List[str]]) -> str:
+        if not table or len(table) < 2:
+            return ""
+
+        header = table[0]
+        time_idx = 0
+        action_idx = 1 if len(header) > 1 else 0
+        for i, col in enumerate(header):
+            col_text = (col or "")
+            if any(k in col_text for k in ["时间", "时刻", "time", "TIME"]):
+                time_idx = i
+            if any(k in col_text for k in ["指令", "动作", "行为", "操作", "名称"]):
+                action_idx = i
+
+        sequence_parts = []
+        for row in table[1:]:
+            if not row:
+                continue
+            row = [self._clean_description(c) for c in row]
+            if not any(row):
+                continue
+            t = row[time_idx] if time_idx < len(row) else ""
+            a = row[action_idx] if action_idx < len(row) else ""
+            if t and a:
+                sequence_parts.append(f"{t}执行{a}")
+            elif a:
+                sequence_parts.append(a)
+
+        if not sequence_parts:
+            return ""
+        return "系统应按以下时间序列依次执行指令：" + "；".join(sequence_parts)
+    
     def _parse_llm_json_response(self, response: str) -> Optional[Dict]:
         """解析LLM的JSON响应"""
         try:

src/requirement_id_generator.py

@@ -0,0 +1,74 @@
+# -*- coding: utf-8 -*-
+"""
+需求编号生成与提取工具。
+"""
+
+import re
+from typing import Optional, Tuple, Dict
+
+
+class RequirementIDGenerator:
+    def __init__(self, type_prefix: Dict[str, str]):
+        self.type_prefix = type_prefix
+
+    def normalize(self, req_id: str) -> str:
+        if not req_id:
+            return ""
+        return str(req_id).strip()
+
+    def extract_from_text(self, text: str) -> Tuple[Optional[str], str]:
+        if not text:
+            return None, text
+
+        pattern1 = r"^\s*([A-Za-z]{2,10}[-_]\d+(?:[-.\d]+)*)\s*[:：\)\]】]?\s*(.+)$"
+        match = re.match(pattern1, text)
+        if match:
+            return match.group(1).strip(), match.group(2).strip()
+
+        pattern2 = r"^\s*([A-Za-z]\d+)\s*[:：\)\]】]?\s*(.+)$"
+        match = re.match(pattern2, text)
+        if match:
+            return match.group(1).strip(), match.group(2).strip()
+
+        pattern3 = r"^\s*([a-z0-9]{1,2}[\)）])\s*(.+)$"
+        match = re.match(pattern3, text)
+        if match:
+            code = match.group(1).strip().rstrip(")）")
+            return code, match.group(2).strip()
+
+        return None, text
+
+    def generate(
+        self,
+        req_type: str,
+        section_number: str,
+        index: int,
+        doc_req_id: str = "",
+        parent_req_id: str = "",
+        split_index: int = 1,
+        split_total: int = 1,
+    ) -> str:
+        base_id = self._generate_base(req_type, section_number, index, doc_req_id, parent_req_id)
+        if split_total > 1:
+            return f"{base_id}-S{split_index}"
+        return base_id
+
+    def _generate_base(
+        self,
+        req_type: str,
+        section_number: str,
+        index: int,
+        doc_req_id: str,
+        parent_req_id: str,
+    ) -> str:
+        if doc_req_id:
+            complete_id_pattern = r"^[A-Za-z0-9]{2,10}[-_].+$"
+            if re.match(complete_id_pattern, doc_req_id):
+                return doc_req_id.replace("_", "-")
+
+        if doc_req_id and parent_req_id:
+            return f"{parent_req_id}-{doc_req_id}"
+
+        prefix = self.type_prefix.get(req_type, "FR")
+        section_part = section_number if section_number else "NA"
+        return f"{prefix}-{section_part}-{index}"

src/requirement_splitter.py

@@ -0,0 +1,188 @@
+# -*- coding: utf-8 -*-
+"""
+需求长句拆分器。
+将复合长句拆分为可验证的原子需求片段。
+"""
+
+import re
+from typing import List
+
+
+class RequirementSplitter:
+    ACTION_HINTS = [
+        "产生",
+        "发送",
+        "设置",
+        "进入",
+        "退出",
+        "关闭",
+        "开启",
+        "监测",
+        "判断",
+        "记录",
+        "上传",
+        "重启",
+        "恢复",
+        "关断",
+        "断电",
+        "加电",
+        "执行",
+        "进行",
+    ]
+
+    CONNECTOR_HINTS = ["并", "并且", "同时", "然后", "且", "以及", "及"]
+    CONDITIONAL_HINTS = ["如果", "当", "若", "在", "其中", "此时", "满足"]
+    CONTEXT_PRONOUN_HINTS = ["该", "其", "上述", "此", "这些", "那些"]
+
+    def __init__(self, max_sentence_len: int = 120, min_clause_len: int = 12):
+        self.max_sentence_len = max_sentence_len
+        self.min_clause_len = min_clause_len
+
+    def split(self, text: str) -> List[str]:
+        cleaned = self._clean(text)
+        if not cleaned:
+            return []
+
+        if self._contains_strong_semantic_chain(cleaned):
+            return [cleaned]
+
+        # 先按强分隔符切分为主片段。
+        base_parts = self._split_by_strong_punctuation(cleaned)
+
+        result: List[str] = []
+        for part in base_parts:
+            if len(part) <= self.max_sentence_len:
+                result.append(part)
+                continue
+
+            # 对超长片段进一步基于逗号和连接词拆分。
+            refined = self._split_long_clause(part)
+            result.extend(refined)
+
+        result = self._merge_semantic_chain(result)
+        result = self._merge_too_short(result)
+        return self._deduplicate(result)
+
+    def _contains_strong_semantic_chain(self, text: str) -> bool:
+        # 条件-动作链完整时，避免强拆。
+        has_conditional = any(h in text for h in ["如果", "若", "当"])
+        has_result = "则" in text or "时" in text
+        action_count = sum(1 for h in self.ACTION_HINTS if h in text)
+        if has_conditional and has_result and action_count >= 2:
+            return True
+        return False
+
+    def _clean(self, text: str) -> str:
+        text = re.sub(r"\s+", " ", text or "")
+        return text.strip(" ;；。")
+
+    def _split_by_strong_punctuation(self, text: str) -> List[str]:
+        chunks = re.split(r"[；;。]", text)
+        return [c.strip(" ，,") for c in chunks if c and c.strip(" ，,")]
+
+    def _split_long_clause(self, clause: str) -> List[str]:
+        if self._contains_strong_semantic_chain(clause):
+            return [clause]
+
+        raw_parts = [x.strip() for x in re.split(r"[，,]", clause) if x.strip()]
+        if len(raw_parts) <= 1:
+            return [clause]
+
+        assembled: List[str] = []
+        current = raw_parts[0]
+
+        for fragment in raw_parts[1:]:
+            if self._should_split(current, fragment):
+                assembled.append(current.strip())
+                current = fragment
+            else:
+                current = f"{current}，{fragment}"
+
+        if current.strip():
+            assembled.append(current.strip())
+
+        return assembled
+
+    def _should_split(self, current: str, fragment: str) -> bool:
+        if len(current) < self.min_clause_len:
+            return False
+
+        # 指代承接片段通常是语义延续，不应切断。
+        if any(fragment.startswith(h) for h in self.CONTEXT_PRONOUN_HINTS):
+            return False
+
+        # 条件链中带“则/并/同时”的后继片段，优先保持在同一需求中。
+        if self._contains_strong_semantic_chain(current + "，" + fragment):
+            return False
+
+        frag_starts_with_condition = any(fragment.startswith(h) for h in self.CONDITIONAL_HINTS)
+        if frag_starts_with_condition:
+            return False
+
+        has_connector = any(fragment.startswith(h) for h in self.CONNECTOR_HINTS)
+        has_action = any(h in fragment for h in self.ACTION_HINTS)
+        current_has_action = any(h in current for h in self.ACTION_HINTS)
+
+        # 连接词 + 动作词，且当前片段已经包含动作，优先拆分。
+        if has_connector and has_action and current_has_action:
+            return True
+
+        # 无连接词但出现新的动作片段且整体过长，也拆分。
+        if has_action and current_has_action and len(current) >= self.max_sentence_len // 2:
+            return True
+
+        return False
+
+    def _merge_semantic_chain(self, parts: List[str]) -> List[str]:
+        if not parts:
+            return []
+
+        merged: List[str] = [parts[0]]
+        for part in parts[1:]:
+            prev = merged[-1]
+            if self._should_merge(prev, part):
+                merged[-1] = f"{prev}；{part}"
+            else:
+                merged.append(part)
+        return merged
+
+    def _should_merge(self, prev: str, current: str) -> bool:
+        # 指代开头：如“该报警信号...”。
+        if any(current.startswith(h) for h in self.CONTEXT_PRONOUN_HINTS):
+            return True
+
+        # 报警触发后的持续条件与动作属于同一链。
+        if ("报警" in prev and "持续" in current) or ("产生" in prev and "报警" in prev and "持续" in current):
+            return True
+
+        # 状态迁移 + 后续控制动作保持合并。
+        if ("进入" in prev or "设置" in prev or "发送" in prev) and ("则" in current or "连续" in current):
+            return True
+
+        # 条件链分裂片段重新合并。
+        if self._contains_strong_semantic_chain(prev + "，" + current):
+            return True
+
+        return False
+
+    def _merge_too_short(self, parts: List[str]) -> List[str]:
+        if not parts:
+            return []
+
+        merged: List[str] = []
+        for part in parts:
+            if merged and len(part) < self.min_clause_len:
+                merged[-1] = f"{merged[-1]}，{part}"
+            else:
+                merged.append(part)
+        return merged
+
+    def _deduplicate(self, parts: List[str]) -> List[str]:
+        seen = set()
+        result = []
+        for part in parts:
+            key = re.sub(r"\s+", "", part)
+            if key and key not in seen:
+                seen.add(key)
+                result.append(part)
+        return result

src/settings.py

@@ -0,0 +1,162 @@
+# -*- coding: utf-8 -*-
+"""
+统一配置与映射模块。
+将需求类型、章节过滤、输出映射和拆分参数收敛到单一入口。
+"""
+
+from dataclasses import dataclass
+from typing import Dict, List, Any
+
+
+@dataclass
+class RequirementTypeRule:
+    key: str
+    chinese_name: str
+    prefix: str
+    keywords: List[str]
+    priority: int
+
+
+class AppSettings:
+    """从 config 读取并提供统一访问接口。"""
+
+    TYPE_NAME_MAP = {
+        "功能需求": "functional",
+        "接口需求": "interface",
+        "性能需求": "performance",
+        "安全需求": "security",
+        "可靠性需求": "reliability",
+        "其他需求": "other",
+    }
+
+    DEFAULT_NON_REQUIREMENT_SECTIONS = [
+        "标识",
+        "系统概述",
+        "文档概述",
+        "引用文档",
+        "合格性规定",
+        "需求可追踪性",
+        "注释",
+        "附录",
+        "范围",
+        "概述",
+    ]
+
+    DEFAULT_TYPE_CHINESE = {
+        "functional": "功能需求",
+        "interface": "接口需求",
+        "performance": "其他需求",
+        "security": "其他需求",
+        "reliability": "其他需求",
+        "other": "其他需求",
+    }
+
+    DEFAULT_PREFIX = {
+        "functional": "FR",
+        "interface": "IR",
+        "performance": "PR",
+        "security": "SR",
+        "reliability": "RR",
+        "other": "OR",
+    }
+
+    def __init__(self, config: Dict[str, Any] = None):
+        self.config = config or {}
+
+        document_cfg = self.config.get("document", {})
+        self.non_requirement_sections = document_cfg.get(
+            "non_requirement_sections", self.DEFAULT_NON_REQUIREMENT_SECTIONS
+        )
+
+        extraction_cfg = self.config.get("extraction", {})
+        req_types_cfg = extraction_cfg.get("requirement_types", {})
+
+        self.requirement_rules = self._build_rules(req_types_cfg)
+        self.type_prefix = self._build_type_prefix(req_types_cfg)
+        self.type_chinese = self._build_type_chinese(req_types_cfg)
+
+        splitter_cfg = extraction_cfg.get("splitter", {})
+        self.splitter_max_sentence_len = int(splitter_cfg.get("max_sentence_len", 120))
+        self.splitter_min_clause_len = int(splitter_cfg.get("min_clause_len", 12))
+        self.splitter_enabled = bool(splitter_cfg.get("enabled", True))
+
+        semantic_cfg = extraction_cfg.get("semantic_guard", {})
+        self.semantic_guard_enabled = bool(semantic_cfg.get("enabled", True))
+        self.preserve_condition_action_chain = bool(
+            semantic_cfg.get("preserve_condition_action_chain", True)
+        )
+        self.preserve_alarm_chain = bool(semantic_cfg.get("preserve_alarm_chain", True))
+
+        table_cfg = extraction_cfg.get("table_strategy", {})
+        self.table_llm_semantic_enabled = bool(table_cfg.get("llm_semantic_enabled", True))
+        self.sequence_table_merge = table_cfg.get("sequence_table_merge", "single_requirement")
+        self.merge_time_series_rows_min = int(table_cfg.get("merge_time_series_rows_min", 3))
+
+        rewrite_cfg = extraction_cfg.get("rewrite_policy", {})
+        self.llm_light_rewrite_enabled = bool(rewrite_cfg.get("llm_light_rewrite_enabled", True))
+        self.preserve_ratio_min = float(rewrite_cfg.get("preserve_ratio_min", 0.65))
+        self.max_length_growth_ratio = float(rewrite_cfg.get("max_length_growth_ratio", 1.25))
+
+        renumber_cfg = extraction_cfg.get("renumber_policy", {})
+        self.renumber_enabled = bool(renumber_cfg.get("enabled", True))
+        self.renumber_mode = renumber_cfg.get("mode", "section_continuous")
+
+    def _build_rules(self, req_types_cfg: Dict[str, Dict[str, Any]]) -> List[RequirementTypeRule]:
+        rules: List[RequirementTypeRule] = []
+        if not req_types_cfg:
+            # 用默认两类保证兼容旧行为
+            return [
+                RequirementTypeRule(
+                    key="interface",
+                    chinese_name="接口需求",
+                    prefix="IR",
+                    keywords=["接口", "interface", "api", "串口", "通信", "CAN", "以太网"],
+                    priority=1,
+                ),
+                RequirementTypeRule(
+                    key="functional",
+                    chinese_name="功能需求",
+                    prefix="FR",
+                    keywords=["功能", "控制", "处理", "监测", "显示"],
+                    priority=2,
+                ),
+            ]
+
+        for zh_name, item in req_types_cfg.items():
+            key = self.TYPE_NAME_MAP.get(zh_name, "other")
+            rules.append(
+                RequirementTypeRule(
+                    key=key,
+                    chinese_name=zh_name,
+                    prefix=item.get("prefix", self.DEFAULT_PREFIX.get(key, "FR")),
+                    keywords=item.get("keywords", []),
+                    priority=int(item.get("priority", 99)),
+                )
+            )
+
+        return sorted(rules, key=lambda x: x.priority)
+
+    def _build_type_prefix(self, req_types_cfg: Dict[str, Dict[str, Any]]) -> Dict[str, str]:
+        mapping = dict(self.DEFAULT_PREFIX)
+        for zh_name, key in self.TYPE_NAME_MAP.items():
+            if zh_name in req_types_cfg:
+                mapping[key] = req_types_cfg[zh_name].get("prefix", mapping[key])
+        return mapping
+
+    def _build_type_chinese(self, req_types_cfg: Dict[str, Dict[str, Any]]) -> Dict[str, str]:
+        mapping = dict(self.DEFAULT_TYPE_CHINESE)
+        for zh_name, key in self.TYPE_NAME_MAP.items():
+            if zh_name in req_types_cfg:
+                mapping[key] = zh_name
+        return mapping
+
+    def is_non_requirement_section(self, title: str) -> bool:
+        return any(keyword in title for keyword in self.non_requirement_sections)
+
+    def detect_requirement_type(self, title: str, content: str) -> str:
+        combined_text = f"{title} {(content or '')[:500]}".lower()
+        for rule in self.requirement_rules:
+            for keyword in rule.keywords:
+                if keyword.lower() in combined_text:
+                    return rule.key
+        return "functional"