refactor(config): 重构配置类以支持动态元数据字段

配置类 ThesisConfig 现在使用 metadata 字典直接透传 TOML 配置，
无需为每个变量单独声明字段。新增模板变量只需修改 TOML 文件，
无需修改 Python 代码。

BREAKING CHANGE: 配置文件结构发生改变，从单独字段改为统一的
metadata 节点。

transit/config.py

@@ -6,17 +6,15 @@ import tomllib
 
 @dataclass
 class ThesisConfig:
-    """论文配置数据（学生信息、元数据等，不包含正文内容）。"""
+    """论文配置数据。
 
-    student_name: str = "<None>"
-    student_id: str = "<None>"
-    college: str = "<None>"
-    major: str = "<None>"
-    class_: str = "<None>"
-    advisor: str = "<None>"
-    advisor_title: str = "<None>"
-    title: str = "<None>"
+    ``metadata`` 直接透传 TOML 的 ``[metadata]`` 节，不再为每个变量声明字段。
+    新增模板变量只需改 TOML，无需修改 Python。
+    """
 
+    metadata: dict = field(default_factory=dict)
+
+    # 以下字段仍有业务逻辑，保留为显式属性
     title_from_md: bool = True
     body_start_keywords: list[str] = field(default_factory=lambda: ["绪论", "引言"])
     body_end_keywords: list[str] = field(
@@ -27,17 +25,8 @@ class ThesisConfig:
     reference_style: str = "列出段落1"
 
     def to_dict(self) -> dict:
-        """转成模板渲染用的扁平字典，排除 options 命名空间。"""
-        return {
-            "student_name": self.student_name,
-            "student_id": self.student_id,
-            "college": self.college,
-            "major": self.major,
-            "class": self.class_,
-            "advisor": self.advisor,
-            "advisor_title": self.advisor_title,
-            "title": self.title,
-        }
+        """透传 metadata（模板变量来源）。"""
+        return self.metadata
 
 
 def load_config(path: str | Path) -> ThesisConfig:
@@ -50,14 +39,7 @@ def load_config(path: str | Path) -> ThesisConfig:
     opts = raw.get("options", {})
 
     return ThesisConfig(
-        student_name=meta.get("student_name", "<None>"),
-        student_id=meta.get("student_id", "<None>"),
-        college=meta.get("college", "<None>"),
-        major=meta.get("major", "<None>"),
-        class_=meta.get("class", "<None>"),
-        advisor=meta.get("advisor", "<None>"),
-        advisor_title=meta.get("advisor_title", "<None>"),
-        title=meta.get("title", "<None>"),
+        metadata=meta,
         title_from_md=opts.get("title_from_md", True),
         body_start_keywords=opts.get("body_start_keywords", ["绪论", "引言"]),
         body_end_keywords=opts.get(

transit/renderer.py

@@ -15,7 +15,8 @@ from .body import body_to_paragraphs, replace_placeholder
 from .references import references_to_paragraphs
 
 
-_TEXT_FIELDS = [
+# 解析器可能产生的字段（用于填充报告）
+_PARSER_FIELDS = [
     "title",
     "abstact_cn_context",
     "abstract_cn_keywords",
@@ -24,13 +25,7 @@ _TEXT_FIELDS = [
     "acknowledgement",
     "reference",
     "appendix",
-    "student_name",
-    "student_id",
-    "college",
-    "major",
-    "class",
-    "advisor",
-    "advisor_title",
+    "body_md",
 ]
 
 
@@ -81,12 +76,11 @@ def generate_thesis(
         body_end_kw=config.body_end_keywords,
     )
 
-    # 3. 合并配置 → 上下文（配置优先）
+    # 3. 合并配置 → 上下文（配置填充解析器未产生的空白）
     for k, v in config.to_dict().items():
         if k == "title" and config.title_from_md and context.get("title"):
             continue  # 以 markdown 标题为准
-        if v != "<None>":
-            context[k] = v
+        context.setdefault(k, v)
 
     # 4. 用 defaultdict 兜底缺失键
     ctx = defaultdict(lambda: "<None>", context)
@@ -129,17 +123,18 @@ def generate_thesis(
 
     print(f"[完成] 论文生成完成: {output_path}")
 
-    # 10. 字段填充报告
+    # 10. 字段填充报告（动态收集所有模板与解析字段）
+    report_fields = list(dict.fromkeys([*config.metadata.keys(), *_PARSER_FIELDS]))
     print("\n--- 字段填充情况 ---")
-    for key in _TEXT_FIELDS:
-        val = ctx[key]
+    for key in report_fields:
+        val = ctx.get(key, "<None>")
         if val == "<None>":
             print(f"  [缺失] {key}")
         else:
             preview = str(val)[:60].replace("\n", " ")
             print(f"  [OK] {key}: {preview}...")
 
-    missing = [k for k in _TEXT_FIELDS if ctx[k] == "<None>"]
+    missing = [k for k in report_fields if ctx.get(k, "<None>") == "<None>"]
     if missing:
         print("\n[警告] 以下字段缺失，已填充 '<None>'：")
         for f in missing: