feat: 新增员工行为画像算法与费用风险标签体系

后端新增员工行为画像算法模块，支持标签规则引擎和评分计算，
完善员工模型、银行信息、序列化和导入逻辑，优化报销审批流
和工作流常量，增强 Hermes 同步和知识同步能力，前端新增费
用画像详情弹窗、雷达图和风险卡片组件，完善登录页和工作台
样式，优化文档中心和归档中心交互，补充单元测试。

server/src/app/services/employee_behavior_profile_response.py

@@ -0,0 +1,63 @@
+from __future__ import annotations
+
+from typing import Any
+
+from app.algorithem.employee_behavior_profile import build_review_suggestions
+from app.models.employee_behavior_profile import EmployeeBehaviorProfileSnapshot
+
+
+def build_profile_payloads(
+    rows: list[EmployeeBehaviorProfileSnapshot],
+) -> list[dict[str, Any]]:
+    return [
+        {
+            "profile_type": row.profile_type,
+            "profile_label": row.profile_type,
+            "score": row.profile_score,
+            "level": row.profile_level,
+            "metrics": row.metrics_json or {},
+            "top_contributors": row.basis_codes_json or [],
+        }
+        for row in sorted(rows, key=lambda item: item.profile_type)
+    ]
+
+
+def build_latest_review_suggestions(
+    *,
+    rows: list[EmployeeBehaviorProfileSnapshot],
+    expense_score: int,
+    process_score: int,
+) -> list[dict[str, Any]]:
+    expense_row = next((row for row in rows if row.profile_type == "expense"), None)
+    metrics = expense_row.metrics_json if expense_row is not None else {}
+    formula_suggestions = build_review_suggestions(
+        expense_profile_score=expense_score,
+        process_quality_score=process_score,
+        requested_days=metrics.get("requested_days"),
+        peer_days_p75=metrics.get("peer_days_p75"),
+        peer_unit_amount_p75=metrics.get("peer_unit_amount_p75"),
+    )
+    merged = [*formula_suggestions, *_merge_review_suggestions(rows)]
+    seen: set[str] = set()
+    unique: list[dict[str, Any]] = []
+    for item in merged:
+        key = str(item.get("type") or item.get("message") or "").strip()
+        if not key or key in seen:
+            continue
+        seen.add(key)
+        unique.append(item)
+    return unique[:5]
+
+
+def _merge_review_suggestions(
+    rows: list[EmployeeBehaviorProfileSnapshot],
+) -> list[dict[str, Any]]:
+    merged: list[dict[str, Any]] = []
+    seen: set[str] = set()
+    for row in rows:
+        for suggestion in (row.metrics_json or {}).get("review_suggestions") or []:
+            key = str(suggestion.get("type") or suggestion.get("message") or "")
+            if key and key not in seen:
+                seen.add(key)
+                merged.append(suggestion)
+    return merged[:5]