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/control_effect.py

@@ -0,0 +1,77 @@
+"""Control effect analysis for risk rules, sampling, and digital employees."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any
+
+HIGH_LEVELS = {"high", "critical"}
+
+
+@dataclass(slots=True)
+class ControlEffectSummary:
+    before_count: int
+    after_count: int
+    risk_count_delta: int
+    average_score_delta: float
+    high_rate_delta: float
+    confirmation_rate_delta: float
+    false_positive_rate_delta: float
+
+    def as_dict(self) -> dict[str, Any]:
+        return {
+            "before_count": self.before_count,
+            "after_count": self.after_count,
+            "risk_count_delta": self.risk_count_delta,
+            "average_score_delta": self.average_score_delta,
+            "high_rate_delta": self.high_rate_delta,
+            "confirmation_rate_delta": self.confirmation_rate_delta,
+            "false_positive_rate_delta": self.false_positive_rate_delta,
+        }
+
+
+class ControlEffectAnalyzer:
+    def compare(
+        self,
+        before: list[dict[str, Any]],
+        after: list[dict[str, Any]],
+    ) -> ControlEffectSummary:
+        before_metrics = _metrics(before)
+        after_metrics = _metrics(after)
+        return ControlEffectSummary(
+            before_count=before_metrics["count"],
+            after_count=after_metrics["count"],
+            risk_count_delta=after_metrics["count"] - before_metrics["count"],
+            average_score_delta=round(after_metrics["average_score"] - before_metrics["average_score"], 4),
+            high_rate_delta=round(after_metrics["high_rate"] - before_metrics["high_rate"], 4),
+            confirmation_rate_delta=round(
+                after_metrics["confirmation_rate"] - before_metrics["confirmation_rate"],
+                4,
+            ),
+            false_positive_rate_delta=round(
+                after_metrics["false_positive_rate"] - before_metrics["false_positive_rate"],
+                4,
+            ),
+        )
+
+
+def _metrics(items: list[dict[str, Any]]) -> dict[str, Any]:
+    count = len(items)
+    if count == 0:
+        return {
+            "count": 0,
+            "average_score": 0.0,
+            "high_rate": 0.0,
+            "confirmation_rate": 0.0,
+            "false_positive_rate": 0.0,
+        }
+    confirmed = sum(1 for item in items if item.get("feedback_status") == "confirmed")
+    false_positive = sum(1 for item in items if item.get("feedback_status") == "false_positive")
+    reviewed = confirmed + false_positive
+    return {
+        "count": count,
+        "average_score": sum(int(item.get("risk_score") or 0) for item in items) / count,
+        "high_rate": sum(1 for item in items if item.get("risk_level") in HIGH_LEVELS) / count,
+        "confirmation_rate": confirmed / reviewed if reviewed else 0.0,
+        "false_positive_rate": false_positive / reviewed if reviewed else 0.0,
+    }