新增修改

final/scripts/f_net1125.py

@@ -0,0 +1,112 @@
+# f_net1125.py  ← 边关系已算完，只算任务网络
+from __future__ import annotations
+import numpy as np
+from typing import Dict, List, Tuple
+
+NodeID = str
+TaskScore = float          # 网络效能得分 [0,1]
+
+
+class TaskNetworkEvaluator:
+    """
+    输入：
+        nodes     - 节点数据（含 function_vector / performance / temporal）
+        relations - 已算好的全部边关系
+          格式：relations[("IS","DEF001","CMD001")] = 0.87
+    """
+    def __init__(self,
+                 nodes: Dict[NodeID, Dict],
+                 relations: Dict[Tuple[str, NodeID, NodeID], float],
+                 weights: Dict[str, Dict[str, float]] = None):
+        self.nodes = nodes
+        self.rels = relations
+        self.w = weights
+
+    # ---------- 工具 ----------
+    def _avg(self, scores: List[float]) -> float:
+        return float(np.mean(scores)) if scores else 0.0
+
+    def _collect_func(self, key: str) -> List[float]:
+        """所有节点指定功能值"""
+        return [n["function_vector"][key] for n in self.nodes.values()]
+
+    def _collect_rel(self, tag: str) -> List[float]:
+        """预存关系强度列表"""
+        return [v for k, v in self.rels.items() if k[0] == tag]
+
+    def _collect_resp(self) -> List[float]:
+        """响应时间得分"""
+        return [np.exp(-0.1 * n["temporal"]["response_time"]) for n in self.nodes.values()]
+
+    # ---------- 2.1 综合防御 ----------
+    def eval_defense(self) -> TaskScore:
+        w = self.w["defense"]
+        f_ic = self._avg(self._collect_func("f_IC"))
+        f_ia = self._avg(self._collect_func("f_IA"))
+        l_is = self._avg(self._collect_rel("IS"))
+        t_resp = self._avg(self._collect_resp())
+        l_cc = self._avg(self._collect_rel("CC"))
+        return w["w1"] * f_ic + w["w2"] * f_ia + w["w3"] * l_is + w["w4"] * t_resp + w["w5"] * l_cc
+
+    # ---------- 2.2 火力打击 ----------
+    def eval_fire(self) -> TaskScore:
+        w = self.w["fire"]
+        f_cs = self._avg(self._collect_func("f_CS"))
+        f_ia = self._avg(self._collect_func("f_IA"))
+        l_co = self._avg(self._collect_rel("CO"))
+        perf_cs = self._avg([n["performance"]["core_performance"] * n["function_vector"]["f_CS"]
+                             for n in self.nodes.values()])
+        l_is = self._avg(self._collect_rel("IS"))
+        return w["w1"] * f_cs + w["w2"] * f_ia + w["w3"] * l_co + w["w4"] * perf_cs + w["w5"] * l_is
+
+    # ---------- 2.3 后勤保障 ----------
+    def eval_logistics(self) -> TaskScore:
+        w = self.w["logistics"]
+        f_cps = self._avg(self._collect_func("f_CPS"))
+        f_dp = self._avg(self._collect_func("f_DP"))
+        l_pd = self._avg(self._collect_rel("PD"))
+        p_rel = self._avg([n["performance"]["mtbf"] / 1000 for n in self.nodes.values()])
+        f_it = self._avg(self._collect_func("f_IT"))
+        return w["w1"] * f_cps + w["w2"] * f_dp + w["w3"] * l_pd + w["w4"] * p_rel + w["w5"] * f_it
+
+    # ---------- 2.4 医疗救援 ----------
+    def eval_medical(self) -> TaskScore:
+        w = self.w["medical"]
+        f_cps = self._avg(self._collect_func("f_CPS"))
+        t_resp = self._avg(self._collect_resp())
+        f_it = self._avg(self._collect_func("f_IT"))
+        l_sf = self._avg(self._collect_rel("SF"))
+        fresh = self._avg([np.exp(-0.1 * n["temporal"]["data_age"]) for n in self.nodes.values()])
+        return w["w1"] * f_cps + w["w2"] * t_resp + w["w3"] * f_it + w["w4"] * l_sf + w["w5"] * fresh
+
+    # ---------- 2.5 紧急疏散 ----------
+    def eval_evacuation(self) -> TaskScore:
+        w = self.w["evacuation"]
+        f_cc = self._avg(self._collect_func("f_CC"))
+        f_it = self._avg(self._collect_func("f_IT"))
+        l_cc = self._avg(self._collect_rel("CC"))
+        f_cps = self._avg(self._collect_func("f_CPS"))
+        t_resp = self._avg(self._collect_resp())
+        return w["w1"] * f_cc + w["w2"] * f_it + w["w3"] * l_cc + w["w4"] * f_cps + w["w5"] * t_resp
+
+    # ---------- 一键评估 ----------
+    def eval_all(self) -> Dict[str, TaskScore]:
+        results = {
+            "defense": self.eval_defense(),
+            "fire": self.eval_fire(),
+            "logistics": self.eval_logistics(),
+            "medical": self.eval_medical(),
+            "evacuation": self.eval_evacuation(),
+        }
+
+        # 计算五个维度的分数
+        dimensions = {
+            "function": self._avg(self._collect_func("f_IC")),
+            "relationship": self._avg(self._collect_rel("IS")),
+            "performance": self._avg([n["performance"]["core_performance"] for n in self.nodes.values()]),
+            "temporal": self._avg(self._collect_resp()),
+            "interaction": self._avg(self._collect_rel("SF"))
+        }
+
+        results["dimensions"] = dimensions
+        return results