feat(pool,admin): 拆分调度维度、重构调度 UI 与增强配置导入导出

调度维度:
- 新增 cache_affinity/free_first/team_first/plus_first/load_balance 五个独立维度
- 将 free_team_first 标记为 hidden，保留后向兼容但不再显示
- Registry 新增 hidden 属性，_helpers 新增 plus_only 优先级评分

前端调度对话框:
- 分配模式（互斥组）独立为按钮组选择，策略调度保留拖拽排序
- 默认调度从 LRU 轮转改为缓存亲和
- 提取 buildPresetListItem/insertMissingByPreferredOrder 消除重复代码

配置导入导出:
- 导出时 api_formats 支持规范化、去重与 None 回退到 Provider 端点
- 导入时兼容 supported_endpoints 别名与历史 None 语义
- 新增 test_admin_system_key_formats 单元测试

src/services/provider/pool/dimensions/__init__.py

@@ -5,14 +5,19 @@ Importing this package registers all built-in preset dimensions.
 
 from __future__ import annotations
 
+from . import cache_affinity  # noqa: F401
 from . import cost_first  # noqa: F401
+from . import free_first  # noqa: F401
 from . import free_team_first  # noqa: F401
 from . import health_first  # noqa: F401
 from . import latency_first  # noqa: F401
+from . import load_balance  # noqa: F401
+from . import plus_first  # noqa: F401
 from . import priority_first  # noqa: F401
 from . import quota_balanced  # noqa: F401
 from . import recent_refresh  # noqa: F401
 from . import single_account  # noqa: F401
+from . import team_first  # noqa: F401
 from .registry import (
     PresetDimensionBase,
     PresetDimensionMeta,

src/services/provider/pool/dimensions/_helpers.py

@@ -219,7 +219,10 @@ def extract_health_score(key_obj: Any) -> float | None:
 
 
 def plan_priority_score(plan_type: str | None, mode: str | None = None) -> float:
-    """Score a key based on plan type and free_team_first mode."""
+    """Score a key based on plan type and scheduling mode.
+
+    Lower score = higher priority.
+    """
 
     effective_mode = (mode or "both").strip().lower()
     if effective_mode == "free_only":
@@ -232,6 +235,11 @@ def plan_priority_score(plan_type: str | None, mode: str | None = None) -> float
             return 0.0
         if plan_type == "free":
             return 0.5
+    elif effective_mode == "plus_only":
+        if plan_type in {"plus", "pro"}:
+            return 0.0
+        if plan_type in {"enterprise", "business"}:
+            return 0.3
     else:
         # "both" or unrecognized -> original behavior
         if plan_type in {"free", "team"}:

src/services/provider/pool/dimensions/cache_affinity.py

@@ -0,0 +1,45 @@
+"""cache_affinity preset dimension."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from ._helpers import rank_descending
+from .registry import PresetDimensionBase, register_preset_dimension
+
+
+class CacheAffinityDimension(PresetDimensionBase):
+    @property
+    def name(self) -> str:
+        return "cache_affinity"
+
+    @property
+    def label(self) -> str:
+        return "缓存亲和"
+
+    @property
+    def description(self) -> str:
+        return "优先复用最近使用过的 Key，利用 Prompt Caching"
+
+    @property
+    def mutex_group(self) -> str | None:
+        return "distribution_mode"
+
+    @property
+    def evidence_hint(self) -> str | None:
+        return "依据 LRU 时间戳（最近使用优先，与 LRU 轮转相反）"
+
+    def compute_metric(
+        self,
+        *,
+        key_id: str,
+        all_key_ids: list[str],
+        keys_by_id: dict[str, Any],
+        lru_scores: dict[str, Any],
+        context: dict[str, Any],
+        mode: str | None,
+    ) -> float:
+        return rank_descending(key_id, lru_scores, all_key_ids)
+
+
+register_preset_dimension(CacheAffinityDimension())

src/services/provider/pool/dimensions/free_first.py

@@ -0,0 +1,51 @@
+"""free_first preset dimension."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from ._helpers import extract_plan_type, plan_priority_score, rank_ascending
+from .registry import PresetDimensionBase, register_preset_dimension
+
+
+class FreeFirstDimension(PresetDimensionBase):
+    @property
+    def name(self) -> str:
+        return "free_first"
+
+    @property
+    def label(self) -> str:
+        return "Free 优先"
+
+    @property
+    def description(self) -> str:
+        return "优先消耗 Free 账号（依赖 plan_type）"
+
+    @property
+    def evidence_hint(self) -> str | None:
+        return "依据 plan_type（Free 账号优先调度）"
+
+    @property
+    def providers(self) -> tuple[str, ...]:
+        return ("codex", "kiro")
+
+    def compute_metric(
+        self,
+        *,
+        key_id: str,
+        all_key_ids: list[str],
+        keys_by_id: dict[str, Any],
+        lru_scores: dict[str, Any],
+        context: dict[str, Any],
+        mode: str | None,
+    ) -> float:
+        plan_scores = {
+            kid: plan_priority_score(extract_plan_type(keys_by_id.get(kid)), "free_only")
+            for kid in all_key_ids
+        }
+        if len(set(plan_scores.values())) <= 1:
+            return rank_ascending(key_id, lru_scores, all_key_ids)
+        return rank_ascending(key_id, plan_scores, all_key_ids)
+
+
+register_preset_dimension(FreeFirstDimension())

src/services/provider/pool/dimensions/free_team_first.py

@@ -37,6 +37,10 @@ class FreeTeamFirstDimension(PresetDimensionBase):
     def default_mode(self) -> str | None:
         return "both"
 
+    @property
+    def hidden(self) -> bool:
+        return True
+
     def compute_metric(
         self,
         *,

src/services/provider/pool/dimensions/load_balance.py

@@ -0,0 +1,45 @@
+"""load_balance preset dimension."""
+
+from __future__ import annotations
+
+import random
+from typing import Any
+
+from .registry import PresetDimensionBase, register_preset_dimension
+
+
+class LoadBalanceDimension(PresetDimensionBase):
+    @property
+    def name(self) -> str:
+        return "load_balance"
+
+    @property
+    def label(self) -> str:
+        return "负载均衡"
+
+    @property
+    def description(self) -> str:
+        return "随机分散 Key 使用，均匀分摊负载"
+
+    @property
+    def mutex_group(self) -> str | None:
+        return "distribution_mode"
+
+    @property
+    def evidence_hint(self) -> str | None:
+        return "每次随机分值，实现完全均匀分散"
+
+    def compute_metric(
+        self,
+        *,
+        key_id: str,
+        all_key_ids: list[str],
+        keys_by_id: dict[str, Any],
+        lru_scores: dict[str, Any],
+        context: dict[str, Any],
+        mode: str | None,
+    ) -> float:
+        return random.random()
+
+
+register_preset_dimension(LoadBalanceDimension())

src/services/provider/pool/dimensions/plus_first.py

@@ -0,0 +1,51 @@
+"""plus_first preset dimension."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from ._helpers import extract_plan_type, plan_priority_score, rank_ascending
+from .registry import PresetDimensionBase, register_preset_dimension
+
+
+class PlusFirstDimension(PresetDimensionBase):
+    @property
+    def name(self) -> str:
+        return "plus_first"
+
+    @property
+    def label(self) -> str:
+        return "Plus 优先"
+
+    @property
+    def description(self) -> str:
+        return "优先消耗 Plus/Pro 账号（依赖 plan_type）"
+
+    @property
+    def evidence_hint(self) -> str | None:
+        return "依据 plan_type（Plus/Pro 账号优先调度）"
+
+    @property
+    def providers(self) -> tuple[str, ...]:
+        return ("codex", "kiro")
+
+    def compute_metric(
+        self,
+        *,
+        key_id: str,
+        all_key_ids: list[str],
+        keys_by_id: dict[str, Any],
+        lru_scores: dict[str, Any],
+        context: dict[str, Any],
+        mode: str | None,
+    ) -> float:
+        plan_scores = {
+            kid: plan_priority_score(extract_plan_type(keys_by_id.get(kid)), "plus_only")
+            for kid in all_key_ids
+        }
+        if len(set(plan_scores.values())) <= 1:
+            return rank_ascending(key_id, lru_scores, all_key_ids)
+        return rank_ascending(key_id, plan_scores, all_key_ids)
+
+
+register_preset_dimension(PlusFirstDimension())

src/services/provider/pool/dimensions/registry.py

@@ -76,6 +76,16 @@ class PresetDimensionBase(ABC):
 
         return None
 
+    @property
+    def hidden(self) -> bool:
+        """If True, this dimension is excluded from API metadata listings.
+
+        The dimension remains functional for backward compatibility but
+        will not appear in the scheduling dialog.
+        """
+
+        return False
+
     @abstractmethod
     def compute_metric(
         self,
@@ -170,6 +180,10 @@ def register_preset_dimension(dim: PresetDimensionBase) -> None:
             raw = str(self._wrapped.evidence_hint or "").strip()
             return raw or None
 
+        @property
+        def hidden(self) -> bool:
+            return self._wrapped.hidden
+
         def compute_metric(
             self,
             *,
@@ -223,6 +237,8 @@ def get_preset_dimension_metas() -> list[PresetDimensionMeta]:
 
     metas: list[PresetDimensionMeta] = []
     for dim in get_all_preset_dimensions():
+        if dim.hidden:
+            continue
         metas.append(
             PresetDimensionMeta(
                 name=dim.name,

src/services/provider/pool/dimensions/team_first.py

@@ -0,0 +1,51 @@
+"""team_first preset dimension."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from ._helpers import extract_plan_type, plan_priority_score, rank_ascending
+from .registry import PresetDimensionBase, register_preset_dimension
+
+
+class TeamFirstDimension(PresetDimensionBase):
+    @property
+    def name(self) -> str:
+        return "team_first"
+
+    @property
+    def label(self) -> str:
+        return "Team 优先"
+
+    @property
+    def description(self) -> str:
+        return "优先消耗 Team 账号（依赖 plan_type）"
+
+    @property
+    def evidence_hint(self) -> str | None:
+        return "依据 plan_type（Team 账号优先调度）"
+
+    @property
+    def providers(self) -> tuple[str, ...]:
+        return ("codex", "kiro")
+
+    def compute_metric(
+        self,
+        *,
+        key_id: str,
+        all_key_ids: list[str],
+        keys_by_id: dict[str, Any],
+        lru_scores: dict[str, Any],
+        context: dict[str, Any],
+        mode: str | None,
+    ) -> float:
+        plan_scores = {
+            kid: plan_priority_score(extract_plan_type(keys_by_id.get(kid)), "team_only")
+            for kid in all_key_ids
+        }
+        if len(set(plan_scores.values())) <= 1:
+            return rank_ascending(key_id, lru_scores, all_key_ids)
+        return rank_ascending(key_id, plan_scores, all_key_ids)
+
+
+register_preset_dimension(TeamFirstDimension())