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/tests/test_employee_behavior_profile_algorithm.py

@@ -0,0 +1,177 @@
+from decimal import Decimal
+
+from app.algorithem.employee_behavior_profile import (
+    ProfileComponent,
+    build_review_suggestions,
+    calculate_review_priority_score,
+    evaluate_weighted_profile,
+    level_from_score,
+    normalize_by_peer_percentiles,
+    percentile,
+)
+from app.algorithem.employee_behavior_profile_tags import build_profile_radar, build_profile_tags
+
+
+def test_peer_percentile_normalization_and_level_mapping() -> None:
+    assert percentile([10, 20, 30, 40, 50], 90) == Decimal("46.0")
+    assert normalize_by_peer_percentiles(35, 20, 50) == 50
+    assert normalize_by_peer_percentiles(10, 20, 50) == 0
+    assert level_from_score(82) == "escalation"
+
+
+def test_weighted_profile_uses_component_weights() -> None:
+    result = evaluate_weighted_profile(
+        "expense",
+        [
+            ProfileComponent("frequency_score", "申请频次", 80, weight=Decimal("0.20")),
+            ProfileComponent("amount_occupancy_score", "预算占用", 60, weight=Decimal("0.30")),
+            ProfileComponent("peer_deviation_score", "同组偏离", 40, weight=Decimal("0.50")),
+        ],
+    )
+
+    assert result.profile_score == 54
+    assert result.profile_level == "watch"
+    assert result.top_contributors(1)[0]["code"] == "peer_deviation_score"
+
+
+def test_review_priority_excludes_ai_usage_score() -> None:
+    assert (
+        calculate_review_priority_score(
+            expense_profile_score=80,
+            process_quality_score=20,
+        )
+        == 62
+    )
+    assert calculate_review_priority_score(
+        expense_profile_score=80,
+        process_quality_score=20,
+    ) == calculate_review_priority_score(
+        expense_profile_score=80,
+        process_quality_score=20,
+    )
+
+
+def test_review_suggestions_generate_caps_without_auto_penalty() -> None:
+    suggestions = build_review_suggestions(
+        expense_profile_score=72,
+        process_quality_score=65,
+        requested_days=Decimal("5"),
+        peer_days_p75=Decimal("3"),
+        policy_limit=Decimal("800"),
+        peer_unit_amount_p75=Decimal("600"),
+    )
+
+    types = {item["type"] for item in suggestions}
+    assert "review_travel_days" in types
+    assert "review_entertainment_unit_amount" in types
+    assert any(item["recommended_upper"] == "3" for item in suggestions)
+
+
+def test_profile_tags_and_approval_radar_use_quantified_evidence() -> None:
+    profiles = [
+        {
+            "profile_type": "expense",
+            "score": 82,
+            "level": "escalation",
+            "metrics": {
+                "window_days": 90,
+                "expense_type_scope": "travel",
+                "peer_sample_size": 20,
+                "amount_total": "128000",
+                "amount_share": "0.34",
+                "claim_count": 6,
+                "current_claim_amount": "56000",
+                "requested_days": "5",
+                "peer_days_p75": "3",
+            },
+            "top_contributors": [
+                {"code": "amount_occupancy_score", "score": 90},
+                {"code": "peer_deviation_score", "score": 88},
+                {"code": "current_claim_deviation_score", "score": 86},
+                {"code": "frequency_score", "score": 84},
+            ],
+        },
+        {
+            "profile_type": "process_quality",
+            "score": 68,
+            "level": "review",
+            "metrics": {
+                "peer_sample_size": 20,
+                "return_count": 2,
+                "missing_attachment_count": 3,
+                "invoice_mismatch_count": 1,
+                "missing_business_context_count": 2,
+            },
+            "top_contributors": [
+                {"code": "return_count_score", "score": 70},
+                {"code": "missing_attachment_score", "score": 75},
+                {"code": "invoice_mismatch_score", "score": 60},
+            ],
+        },
+    ]
+
+    tags = build_profile_tags(profiles, scene="approval")
+    tag_codes = {item["code"] for item in tags}
+    assert {"expense_king", "large_amount_deviation", "return_frequent"} <= tag_codes
+    assert all(item["evidence"] for item in tags)
+
+    radar = build_profile_radar(profiles, tags, scene="approval")
+    dimensions = {item["code"]: item for item in radar["dimensions"]}
+    assert set(dimensions) == {
+        "expense_intensity",
+        "application_rhythm",
+        "travel_entertainment",
+        "material_completeness",
+        "process_pressure",
+    }
+    assert dimensions["expense_intensity"]["score"] >= 70
+    assert "expense_king" in dimensions["expense_intensity"]["top_tags"]
+
+
+def test_profile_tags_include_ai_and_approval_traits_outside_approval_scene() -> None:
+    profiles = [
+        {
+            "profile_type": "ai_usage",
+            "score": 72,
+            "level": "review",
+            "metrics": {
+                "peer_sample_size": 15,
+                "ai_run_count": 14,
+                "tool_call_count": 10,
+                "failed_tool_call_count": 3,
+                "estimated_token_count": 22000,
+                "token_count_mode": "estimated_token_count",
+            },
+            "top_contributors": [
+                {"code": "ai_call_count_score", "score": 75},
+                {"code": "token_cost_score", "score": 70},
+                {"code": "failed_ai_call_score", "score": 80},
+            ],
+        },
+        {
+            "profile_type": "approval",
+            "score": 64,
+            "level": "review",
+            "metrics": {
+                "peer_sample_size": 12,
+                "approval_record_count": 6,
+                "direct_approve_ratio": "0.5",
+                "return_count": 3,
+                "sla_overdue_rate": "0.4",
+            },
+            "top_contributors": [
+                {"code": "system_advice_override_score", "score": 70},
+            ],
+        },
+    ]
+
+    tags = build_profile_tags(profiles, scene="operations")
+    tag_codes = {item["code"] for item in tags}
+    assert {"ai_heavy", "token_high", "ai_failure_cluster", "cautious_reviewer"} <= tag_codes
+
+    radar = build_profile_radar(profiles, tags, scene="operations")
+    assert len(radar["dimensions"]) == 8
+    assert any(
+        item["code"] == "ai_collaboration" and item["score"] > 0
+        for item in radar["dimensions"]
+    )