feat: 新增风险图谱算法与系统仪表盘及操作反馈体系

后端新增风险图谱算法模块、风险观察与反馈服务、规则 DSL
校验器和可解释性引擎，完善系统仪表盘和财务仪表盘统计，
优化 agent 运行和编排执行链路，清理旧开发文档，前端新增
系统趋势、负载热力图等多种仪表盘图表组件，完善操作反馈
对话框和工作台日期选择器，优化报销创建和审批详情交互，
补充单元测试覆盖。

server/src/app/algorithem/risk_graph/decisioning.py

@@ -0,0 +1,132 @@
+"""Decision trace and explanation helpers for risk graph observations."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from decimal import Decimal
+from typing import Any
+
+from .models import PeerBaseline, RiskEvidence
+
+RISK_SCORE_FORMULA = (
+    "0.35*S_rule + 0.25*S_anomaly + "
+    "0.20*S_graph + 0.15*S_policy + 0.05*S_history"
+)
+
+
+@dataclass(slots=True)
+class DecisionTrace:
+    formula: str
+    algorithm_version: str
+    input_scores: dict[str, int]
+    output_score: int
+    decision_row: str
+    feature_contributions_json: list[dict[str, Any]]
+    uncertainty_reasons_json: list[str]
+    explanation_template_key: str
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+    def as_dict(self) -> dict[str, Any]:
+        return {
+            "formula": self.formula,
+            "algorithm_version": self.algorithm_version,
+            "input_scores": dict(self.input_scores),
+            "output_score": self.output_score,
+            "decision_row": self.decision_row,
+            "feature_contributions_json": list(self.feature_contributions_json),
+            "uncertainty_reasons_json": list(self.uncertainty_reasons_json),
+            "explanation_template_key": self.explanation_template_key,
+            **self.metadata,
+        }
+
+
+class DecisionTraceBuilder:
+    def build(
+        self,
+        *,
+        algorithm_version: str,
+        risk_signal: str,
+        risk_level: str,
+        raw_risk_score: int,
+        risk_score: int,
+        contribution_scores: dict[str, int],
+        evidence: list[RiskEvidence],
+        baseline: PeerBaseline,
+        confidence: Decimal,
+        metadata: dict[str, Any],
+    ) -> DecisionTrace:
+        return DecisionTrace(
+            formula=RISK_SCORE_FORMULA,
+            algorithm_version=algorithm_version,
+            input_scores=contribution_scores,
+            output_score=risk_score,
+            decision_row=_decision_row(risk_score=risk_score, risk_level=risk_level),
+            feature_contributions_json=_feature_contributions(contribution_scores),
+            uncertainty_reasons_json=_uncertainty_reasons(
+                raw_risk_score=raw_risk_score,
+                risk_score=risk_score,
+                evidence=evidence,
+                baseline=baseline,
+                confidence=confidence,
+                metadata=metadata,
+            ),
+            explanation_template_key=f"risk.{risk_signal}.{risk_level}",
+            metadata=metadata,
+        )
+
+
+def _decision_row(*, risk_score: int, risk_level: str) -> str:
+    if risk_score >= 90:
+        return f"{risk_level}:score>=90"
+    if risk_score >= 70:
+        return f"{risk_level}:70<=score<90"
+    if risk_score >= 45:
+        return f"{risk_level}:45<=score<70"
+    return f"{risk_level}:score<45"
+
+
+def _feature_contributions(scores: dict[str, int]) -> list[dict[str, Any]]:
+    weights = {
+        "S_rule": Decimal("0.35"),
+        "S_anomaly": Decimal("0.25"),
+        "S_graph": Decimal("0.20"),
+        "S_policy": Decimal("0.15"),
+        "S_history": Decimal("0.05"),
+    }
+    rows = []
+    for key, score in scores.items():
+        weighted_score = Decimal(int(score or 0)) * weights.get(key, Decimal("0"))
+        rows.append(
+            {
+                "feature": key,
+                "score": int(score or 0),
+                "weight": str(weights.get(key, Decimal("0"))),
+                "weighted_score": float(weighted_score),
+            }
+        )
+    return sorted(rows, key=lambda item: item["weighted_score"], reverse=True)
+
+
+def _uncertainty_reasons(
+    *,
+    raw_risk_score: int,
+    risk_score: int,
+    evidence: list[RiskEvidence],
+    baseline: PeerBaseline,
+    confidence: Decimal,
+    metadata: dict[str, Any],
+) -> list[str]:
+    reasons: list[str] = []
+    if risk_score < raw_risk_score:
+        reasons.append("score_capped_by_gate")
+    if baseline.scope == "insufficient_sample" or baseline.sample_size <= 0:
+        reasons.append("peer_baseline_insufficient")
+    if confidence < Decimal("0.55"):
+        reasons.append("low_confidence")
+    if len({item.source for item in evidence if item.source}) < 2:
+        reasons.append("single_evidence_source")
+    if metadata.get("ontology_gate") == "candidate_only":
+        reasons.append("ontology_candidate_only")
+    if metadata.get("data_quality_gate") not in {"", "passed", None}:
+        reasons.append("data_quality_gate_not_passed")
+    return list(dict.fromkeys(reasons))