backend

src/cognitive_agent/application/conversation_state.py

@@ -339,6 +339,55 @@ class ConversationStateStore:
             self._persist_presets(conversation_id, state["presets"])
             return deepcopy(state)
 
+    def save_mode_profile(
+        self,
+        conversation_id: str,
+        utterance: str,
+        mode_profile: dict[str, Any],
+        source_actions: list[dict[str, Any]],
+        decision_summary: list[str],
+    ) -> dict[str, Any]:
+        with self._lock:
+            state = self._state(conversation_id)
+            state["turn_count"] += 1
+            state["updated_at"] = _now()
+            state["recent_turns"].append(
+                {
+                    "utterance": utterance,
+                    "actions": deepcopy(source_actions),
+                    "decision_summary": list(decision_summary),
+                    "control_event": "mode_profile_save",
+                    "mode_profile": deepcopy(mode_profile),
+                }
+            )
+            state["recent_turns"] = state["recent_turns"][-6:]
+            return deepcopy(state)
+
+    def record_mode_profile_event(
+        self,
+        conversation_id: str,
+        utterance: str,
+        mode_profile: dict[str, Any],
+        source_actions: list[dict[str, Any]],
+        decision_summary: list[str],
+        control_event: str,
+    ) -> dict[str, Any]:
+        with self._lock:
+            state = self._state(conversation_id)
+            state["turn_count"] += 1
+            state["updated_at"] = _now()
+            state["recent_turns"].append(
+                {
+                    "utterance": utterance,
+                    "actions": deepcopy(source_actions),
+                    "decision_summary": list(decision_summary),
+                    "control_event": control_event,
+                    "mode_profile": deepcopy(mode_profile),
+                }
+            )
+            state["recent_turns"] = state["recent_turns"][-6:]
+            return deepcopy(state)
+
     def reset(self, conversation_id: str) -> dict[str, Any]:
         with self._lock:
             state = _new_state(conversation_id, self._load_presets(conversation_id))

src/cognitive_agent/application/mode_profile.py

@@ -0,0 +1,435 @@
+"""Repository-backed cabin mode profiles."""
+
+from __future__ import annotations
+
+import hashlib
+import re
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+import yaml
+
+
+_WHITESPACE = re.compile(r"\s+")
+_NON_ASCII_SLUG = re.compile(r"[^a-z0-9]+")
+
+
+def _normalize_match_text(value: str) -> str:
+    return _WHITESPACE.sub("", value.casefold())
+
+
+def _clean_name(value: str) -> str:
+    cleaned = value.strip().strip("，。,.!！")
+    if not cleaned:
+        raise ValueError("mode name must not be empty")
+    if len(cleaned) > 64:
+        raise ValueError("mode name must be at most 64 characters")
+    return cleaned
+
+
+def _mode_aliases(name: str, aliases: list[str] | None = None) -> list[str]:
+    values = [name, *(aliases or [])]
+    if name.endswith("模式") and len(name) > 2:
+        values.append(name.removesuffix("模式"))
+    else:
+        values.append(f"{name}模式")
+
+    normalized: set[str] = set()
+    unique = []
+    for value in values:
+        candidate = str(value).strip()
+        key = _normalize_match_text(candidate)
+        if not candidate or key in normalized:
+            continue
+        normalized.add(key)
+        unique.append(candidate)
+    return unique
+
+
+def _slugify_mode_id(name: str) -> str:
+    ascii_slug = _NON_ASCII_SLUG.sub("-", name.casefold()).strip("-")
+    digest = hashlib.sha256(name.encode("utf-8")).hexdigest()[:10]
+    if ascii_slug:
+        return ascii_slug if len(ascii_slug) >= 3 else f"{ascii_slug}-{digest[:6]}"
+    return f"mode-{digest}"
+
+
+@dataclass(frozen=True)
+class ModeOperation:
+    domain: str
+    capability: str
+    target_scope: str
+    parameters: dict[str, Any]
+    operation: str = "set"
+    reason: str = ""
+
+    @classmethod
+    def from_dict(cls, document: dict[str, Any]) -> "ModeOperation":
+        parameters = document.get("parameters", {})
+        if not isinstance(parameters, dict):
+            raise ValueError("mode operation parameters must be an object")
+        return cls(
+            domain=str(document["domain"]),
+            capability=str(document["capability"]),
+            target_scope=str(document["target_scope"]),
+            operation=str(document.get("operation", "set")),
+            parameters=dict(parameters),
+            reason=str(document.get("reason", "")),
+        )
+
+    def to_request(self, mode_name: str) -> dict[str, Any]:
+        return {
+            "domain": self.domain,
+            "capability": self.capability,
+            "target_scope": self.target_scope,
+            "operation": self.operation,
+            "parameters": [
+                {"name": name, "value": value}
+                for name, value in self.parameters.items()
+            ],
+            "reason": self.reason or f"Matched mode profile {mode_name}.",
+        }
+
+    def to_yaml(self) -> dict[str, Any]:
+        return {
+            "domain": self.domain,
+            "capability": self.capability,
+            "target_scope": self.target_scope,
+            "operation": self.operation,
+            "parameters": dict(self.parameters),
+            "reason": self.reason,
+        }
+
+
+@dataclass(frozen=True)
+class ModeProfile:
+    id: str
+    name: str
+    aliases: tuple[str, ...]
+    description: str
+    icon: str
+    operations: tuple[ModeOperation, ...]
+    source: str
+    common: bool = True
+    vehicle_ids: tuple[str, ...] = ()
+
+    @classmethod
+    def from_yaml(cls, path: Path, profile_root: Path | None = None) -> "ModeProfile":
+        document = yaml.safe_load(path.read_text(encoding="utf-8"))
+        if not isinstance(document, dict):
+            raise ValueError(f"{path} must contain a YAML object")
+        operations = document.get("operations", [])
+        if not isinstance(operations, list) or not operations:
+            raise ValueError(f"{path} must contain at least one operation")
+        name = _clean_name(str(document["name"]))
+        common, vehicle_ids = _mode_scope(document, path, profile_root)
+        aliases = _mode_aliases(
+            name,
+            [str(alias) for alias in document.get("aliases", [])],
+        )
+        return cls(
+            id=str(document["id"]),
+            name=name,
+            aliases=tuple(aliases),
+            description=str(document.get("description", "")),
+            icon=str(document.get("icon", "bookmark")),
+            operations=tuple(ModeOperation.from_dict(item) for item in operations),
+            source=str(path),
+            common=common,
+            vehicle_ids=vehicle_ids,
+        )
+
+    def public_dict(self) -> dict[str, Any]:
+        return {
+            "id": self.id,
+            "name": self.name,
+            "aliases": list(self.aliases),
+            "description": self.description,
+            "icon": self.icon,
+            "domains": sorted({operation.domain for operation in self.operations}),
+            "operation_count": len(self.operations),
+            "scope": {
+                "common": self.common,
+                "vehicle_ids": list(self.vehicle_ids),
+            },
+        }
+
+
+class ModeProfileRegistry:
+    """Load, match, and persist long-term cabin mode profiles."""
+
+    def __init__(self, profile_root: Path):
+        self.profile_root = Path(profile_root)
+        self._profiles: list[ModeProfile] = []
+        self.reload()
+
+    def reload(self) -> None:
+        if not self.profile_root.exists():
+            self._profiles = []
+            return
+        self._profiles = [
+            ModeProfile.from_yaml(path, self.profile_root)
+            for path in sorted(self.profile_root.rglob("*.yaml"))
+            if path.is_file()
+        ]
+
+    def profiles(self) -> list[ModeProfile]:
+        return list(self._profiles)
+
+    def public_modes(self, vehicle_id: str | None = None) -> list[dict[str, Any]]:
+        return [
+            profile.public_dict()
+            for profile in self._profiles_for_vehicle(vehicle_id)
+        ]
+
+    def match_text(
+        self,
+        utterance: str,
+        vehicle_id: str | None = None,
+    ) -> list[ModeProfile]:
+        haystack = _normalize_match_text(utterance)
+        selected = {
+            profile.id: profile
+            for profile in self._profiles_for_vehicle(vehicle_id)
+        }
+        matches = []
+        matched_ids: set[str] = set()
+        for profile in self._visible_candidates_for_vehicle(vehicle_id):
+            candidates = [profile.id, profile.name, *profile.aliases]
+            if any(
+                candidate
+                and _normalize_match_text(str(candidate)) in haystack
+                for candidate in candidates
+            ):
+                if profile.id in matched_ids:
+                    continue
+                matched_ids.add(profile.id)
+                matches.append(selected[profile.id])
+        return matches
+
+    @staticmethod
+    def requests_for_profiles(profiles: list[ModeProfile]) -> list[dict[str, Any]]:
+        return [
+            operation.to_request(profile.name)
+            for profile in profiles
+            for operation in profile.operations
+        ]
+
+    def save_mode(
+        self,
+        name: str,
+        operations: list[dict[str, Any] | ModeOperation],
+        mode_id: str | None = None,
+        aliases: list[str] | None = None,
+        vehicle_id: str | None = None,
+    ) -> ModeProfile:
+        clean_name = _clean_name(name)
+        canonical_operations = [
+            operation
+            if isinstance(operation, ModeOperation)
+            else ModeOperation.from_dict(operation)
+            for operation in operations
+        ]
+        if not canonical_operations:
+            raise ValueError("mode profile must contain at least one operation")
+        profile_id = mode_id or _slugify_mode_id(clean_name)
+        scope = (
+            {"vehicle_ids": [vehicle_id]}
+            if vehicle_id
+            else {"common": True, "vehicle_ids": []}
+        )
+        document = {
+            "id": profile_id,
+            "name": clean_name,
+            "aliases": _mode_aliases(clean_name, aliases),
+            "description": "用户固化的长期模式配置。",
+            "icon": "bookmark",
+            "scope": scope,
+            "operations": [
+                operation.to_yaml() for operation in canonical_operations
+            ],
+        }
+        path = (
+            self.profile_root / "vehicles" / vehicle_id / "user" / f"{profile_id}.yaml"
+            if vehicle_id
+            else self.profile_root / "common" / "user" / f"{profile_id}.yaml"
+        )
+        path.parent.mkdir(parents=True, exist_ok=True)
+        temporary = path.with_suffix(path.suffix + ".tmp")
+        temporary.write_text(
+            yaml.safe_dump(document, allow_unicode=True, sort_keys=False),
+            encoding="utf-8",
+        )
+        temporary.replace(path)
+        self.reload()
+        return ModeProfile.from_yaml(path, self.profile_root)
+
+    def update_mode(
+        self,
+        profile: ModeProfile,
+        operations: list[dict[str, Any] | ModeOperation],
+    ) -> ModeProfile:
+        canonical_updates = _canonical_operations(operations)
+        if not canonical_updates:
+            raise ValueError("mode profile update must contain at least one operation")
+
+        path = Path(profile.source)
+        if not path.is_file():
+            raise ValueError(f"mode profile source does not exist: {path}")
+        backup_path = _initial_backup_path(path)
+        if not backup_path.exists():
+            backup_path.write_text(path.read_text(encoding="utf-8"), encoding="utf-8")
+
+        merged_operations = _merge_operations(profile.operations, canonical_updates)
+        document = _mode_profile_document(profile, merged_operations)
+        _replace_yaml(path, document)
+        self.reload()
+        return ModeProfile.from_yaml(path, self.profile_root)
+
+    def restore_initial_mode(self, profile: ModeProfile) -> ModeProfile:
+        path = Path(profile.source)
+        backup_path = _initial_backup_path(path)
+        if not backup_path.is_file():
+            raise ValueError(f"no initial mode backup found: {backup_path}")
+        path.parent.mkdir(parents=True, exist_ok=True)
+        temporary = path.with_suffix(path.suffix + ".tmp")
+        temporary.write_text(backup_path.read_text(encoding="utf-8"), encoding="utf-8")
+        temporary.replace(path)
+        self.reload()
+        return ModeProfile.from_yaml(path, self.profile_root)
+
+    def _profiles_for_vehicle(self, vehicle_id: str | None) -> list[ModeProfile]:
+        selected: dict[str, ModeProfile] = {}
+        order: list[str] = []
+        for profile in self._visible_candidates_for_vehicle(vehicle_id):
+            if profile.id not in selected:
+                order.append(profile.id)
+                selected[profile.id] = profile
+                continue
+            current = selected[profile.id]
+            if _profile_rank(profile, vehicle_id) >= _profile_rank(current, vehicle_id):
+                selected[profile.id] = profile
+        return [selected[profile_id] for profile_id in order]
+
+    def _visible_candidates_for_vehicle(
+        self,
+        vehicle_id: str | None,
+    ) -> list[ModeProfile]:
+        return [
+            profile
+            for profile in self._profiles
+            if _is_visible_for_vehicle(profile, vehicle_id)
+        ]
+
+
+def _canonical_operations(
+    operations: list[dict[str, Any] | ModeOperation],
+) -> list[ModeOperation]:
+    return [
+        operation
+        if isinstance(operation, ModeOperation)
+        else ModeOperation.from_dict(operation)
+        for operation in operations
+    ]
+
+
+def _operation_signature(operation: ModeOperation) -> tuple[str, str, str]:
+    return (operation.domain, operation.capability, operation.target_scope)
+
+
+def _merge_operations(
+    existing: tuple[ModeOperation, ...],
+    updates: list[ModeOperation],
+) -> list[ModeOperation]:
+    merged = list(existing)
+    positions = {
+        _operation_signature(operation): index
+        for index, operation in enumerate(merged)
+    }
+    for operation in updates:
+        signature = _operation_signature(operation)
+        index = positions.get(signature)
+        if index is None:
+            positions[signature] = len(merged)
+            merged.append(operation)
+            continue
+        merged[index] = operation
+    return merged
+
+
+def _initial_backup_path(path: Path) -> Path:
+    return path.with_suffix(path.suffix + ".initial.bak")
+
+
+def _mode_profile_document(
+    profile: ModeProfile,
+    operations: list[ModeOperation],
+) -> dict[str, Any]:
+    scope: dict[str, Any] = {
+        "common": profile.common,
+        "vehicle_ids": list(profile.vehicle_ids),
+    }
+    return {
+        "id": profile.id,
+        "name": profile.name,
+        "aliases": list(profile.aliases),
+        "description": profile.description,
+        "icon": profile.icon,
+        "scope": scope,
+        "operations": [operation.to_yaml() for operation in operations],
+    }
+
+
+def _replace_yaml(path: Path, document: dict[str, Any]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    temporary = path.with_suffix(path.suffix + ".tmp")
+    temporary.write_text(
+        yaml.safe_dump(document, allow_unicode=True, sort_keys=False),
+        encoding="utf-8",
+    )
+    temporary.replace(path)
+
+
+def _mode_scope(
+    document: dict[str, Any],
+    path: Path,
+    profile_root: Path | None,
+) -> tuple[bool, tuple[str, ...]]:
+    scope = document.get("scope")
+    if isinstance(scope, dict):
+        vehicle_ids = tuple(
+            str(vehicle_id).strip()
+            for vehicle_id in scope.get("vehicle_ids", [])
+            if str(vehicle_id).strip()
+        )
+        common = bool(scope.get("common", not vehicle_ids))
+        return common, vehicle_ids
+
+    if profile_root is not None:
+        try:
+            parts = path.relative_to(profile_root).parts
+        except ValueError:
+            parts = ()
+        if len(parts) >= 3 and parts[0] == "vehicles":
+            return False, (parts[1],)
+        if parts and parts[0] == "common":
+            return True, ()
+    return True, ()
+
+
+def _is_visible_for_vehicle(
+    profile: ModeProfile,
+    vehicle_id: str | None,
+) -> bool:
+    if vehicle_id is None:
+        return True
+    return profile.common or vehicle_id in profile.vehicle_ids
+
+
+def _profile_rank(profile: ModeProfile, vehicle_id: str | None) -> int:
+    if vehicle_id is not None and vehicle_id in profile.vehicle_ids:
+        return 2
+    if profile.common:
+        return 1
+    return 0

src/cognitive_agent/application/model_plan_adapter.py

@@ -10,6 +10,7 @@ from cognitive_agent.application.seat_calibration import (
     ANGLE_CALIBRATIONS,
     angle_to_position_pct,
 )
+from cognitive_agent.application.vehicle_profile import VehicleProfile
 
 
 SEAT_CAPABILITIES = {
@@ -23,6 +24,7 @@ SEAT_MOTION_CAPABILITIES = {
     "seat.set_legrest_height": "legrest_height",
     "seat.set_legrest_extension": "legrest_extension",
 }
+SEAT_RELATIVE_PHYSICAL_PARAMETERS = {"distance_mm", "angle_delta_deg"}
 SAVE_PRESET_CAPABILITY = "seat.save_posture_preset"
 APPLY_PRESET_CAPABILITY = "seat.apply_posture_preset"
 HEX_COLOR = re.compile(r"^#[0-9A-Fa-f]{6}$")
@@ -45,6 +47,7 @@ class ModelPlanAdapter:
         self,
         plan: dict[str, Any],
         context: dict[str, Any] | None = None,
+        vehicle_profile: VehicleProfile | None = None,
     ) -> dict[str, Any]:
         actions: list[dict[str, Any]] = []
         domain_actions: list[dict[str, Any]] = []
@@ -73,6 +76,11 @@ class ModelPlanAdapter:
             }
             allowed_parameters = {item["name"] for item in capability.get("parameters", [])}
             unknown_parameters = set(parameters) - allowed_parameters
+            if (
+                domain == "seat"
+                and capability_name in SEAT_MOTION_CAPABILITIES
+            ):
+                unknown_parameters -= SEAT_RELATIVE_PHYSICAL_PARAMETERS
             if unknown_parameters:
                 ambiguities.append(
                     {
@@ -162,6 +170,7 @@ class ModelPlanAdapter:
                         operation_parameters,
                         ambiguities,
                         context,
+                        vehicle_profile,
                     )
                     if action is not None:
                         actions.append(action)
@@ -174,7 +183,7 @@ class ModelPlanAdapter:
                 )
                 continue
             action = self._seat_action(
-                index, request, parameters, ambiguities, context
+                index, request, parameters, ambiguities, context, vehicle_profile
             )
             if action is not None:
                 actions.append(action)
@@ -367,6 +376,7 @@ class ModelPlanAdapter:
         parameters: dict[str, Any],
         ambiguities: list[dict[str, str]],
         context: dict[str, Any],
+        vehicle_profile: VehicleProfile | None = None,
     ) -> dict[str, Any] | None:
         target = request.get("target_scope")
         if target not in {"driver", "passenger"}:
@@ -381,21 +391,68 @@ class ModelPlanAdapter:
         capability = request["capability"]
         if capability in SEAT_MOTION_CAPABILITIES:
             axis = SEAT_MOTION_CAPABILITIES[capability]
+            physical_action = ModelPlanAdapter._relative_physical_motion_action(
+                index,
+                request,
+                parameters,
+                ambiguities,
+                context,
+                target,
+                axis,
+                vehicle_profile,
+            )
+            if physical_action is not None:
+                return physical_action
             angle = parameters.get("angle_deg")
-            if angle is not None and capability in ANGLE_CALIBRATIONS:
-                calibration = ANGLE_CALIBRATIONS[capability]
-                if (
-                    not isinstance(angle, (int, float))
-                    or isinstance(angle, bool)
-                    or not calibration.minimum_deg <= float(angle) <= calibration.maximum_deg
-                ):
+            if angle is not None:
+                if not isinstance(angle, (int, float)) or isinstance(angle, bool):
                     ambiguities.append(
                         {
                             "field": f"requests[{index}].parameters.angle_deg",
-                            "question": (
-                                f"{capability} 的夹角必须在 "
-                                f"{calibration.minimum_deg:g}-{calibration.maximum_deg:g} 度之间。"
-                            ),
+                            "question": f"{capability} 的夹角必须是数字。",
+                        }
+                    )
+                    return None
+                profile_motor = (
+                    vehicle_profile.seat_motors.get(axis)
+                    if vehicle_profile is not None
+                    else None
+                )
+                if profile_motor is not None and profile_motor.display_angle:
+                    if not profile_motor.contains_display_angle(float(angle)):
+                        display_angle = profile_motor.display_angle
+                        ambiguities.append(
+                            {
+                                "field": f"requests[{index}].parameters.angle_deg",
+                                "question": (
+                                    f"{capability} 的夹角必须在 "
+                                    f"{display_angle['zero_deg']:g}-{display_angle['max_deg']:g} 度之间。"
+                                ),
+                            }
+                        )
+                        return None
+                    position_pct = profile_motor.position_pct_for_display_angle(
+                        float(angle)
+                    )
+                elif capability in ANGLE_CALIBRATIONS:
+                    calibration = ANGLE_CALIBRATIONS[capability]
+                    if not calibration.minimum_deg <= float(angle) <= calibration.maximum_deg:
+                        ambiguities.append(
+                            {
+                                "field": f"requests[{index}].parameters.angle_deg",
+                                "question": (
+                                    f"{capability} 的夹角必须在 "
+                                    f"{calibration.minimum_deg:g}-{calibration.maximum_deg:g} 度之间。"
+                                ),
+                            }
+                        )
+                        return None
+                    position_pct = angle_to_position_pct(capability, float(angle))
+                else:
+                    ambiguities.append(
+                        {
+                            "field": f"requests[{index}].parameters.position_pct",
+                            "question": "当前车型未给出该座椅轴的角度标定，需要 0-100 的绝对百分比。",
                         }
                     )
                     return None
@@ -406,7 +463,7 @@ class ModelPlanAdapter:
                     "action_type": "position",
                     "target": target,
                     "axis": axis,
-                    "position_pct": angle_to_position_pct(capability, float(angle)),
+                    "position_pct": position_pct,
                     "angle_deg": exact_angle,
                 }
                 return ModelPlanAdapter._preserve_relative_motion(
@@ -460,6 +517,157 @@ class ModelPlanAdapter:
             ),
         }
 
+    @staticmethod
+    def _relative_physical_motion_action(
+        index: int,
+        request: dict[str, Any],
+        parameters: dict[str, Any],
+        ambiguities: list[dict[str, str]],
+        context: dict[str, Any],
+        target: str,
+        axis: str,
+        vehicle_profile: VehicleProfile | None,
+    ) -> dict[str, Any] | None:
+        physical_parameters = [
+            name
+            for name in ("distance_mm", "angle_delta_deg")
+            if parameters.get(name) is not None
+        ]
+        if not physical_parameters:
+            return None
+        if len(physical_parameters) > 1:
+            ambiguities.append(
+                {
+                    "field": f"requests[{index}].parameters",
+                    "question": "单个座椅轴相对移动只能提供一种物理增量。",
+                }
+            )
+            return None
+        if vehicle_profile is None:
+            ambiguities.append(
+                {
+                    "field": f"requests[{index}].parameters.position_pct",
+                    "question": "缺少车型座椅量程，无法把相对物理移动换算为绝对百分比。",
+                }
+            )
+            return None
+
+        parameter_name = physical_parameters[0]
+        delta = parameters[parameter_name]
+        if (
+            not isinstance(delta, (int, float))
+            or isinstance(delta, bool)
+            or float(delta) <= 0
+        ):
+            ambiguities.append(
+                {
+                    "field": f"requests[{index}].parameters.{parameter_name}",
+                    "question": "座椅相对物理移动量必须是正数。",
+                }
+            )
+            return None
+
+        motor = vehicle_profile.seat_motors.get(axis)
+        if motor is None:
+            ambiguities.append(
+                {
+                    "field": f"requests[{index}].capability",
+                    "question": f"车型 {vehicle_profile.name} 未配置 {axis} 轴量程。",
+                }
+            )
+            return None
+        expected_unit = "mm" if parameter_name == "distance_mm" else "degree"
+        if motor.unit != expected_unit:
+            ambiguities.append(
+                {
+                    "field": f"requests[{index}].parameters.{parameter_name}",
+                    "question": (
+                        f"{axis} 轴使用 {motor.unit} 标定，不能用 {expected_unit} 增量换算。"
+                    ),
+                }
+            )
+            return None
+
+        direction = str(request.get("operation", "set"))
+        if direction not in {"increase", "decrease"}:
+            ambiguities.append(
+                {
+                    "field": f"requests[{index}].operation",
+                    "question": "相对物理移动需要 increase 或 decrease 方向。",
+                }
+            )
+            return None
+        previous = (
+            context.get("devices", {})
+            .get(target, {})
+            .get("position", {})
+            .get(axis)
+        )
+        if not isinstance(previous, dict):
+            ambiguities.append(
+                {
+                    "field": f"requests[{index}].target_scope",
+                    "question": "缺少当前座椅姿态，无法安全执行相对物理移动。",
+                }
+            )
+            return None
+        current_pct = previous.get("position_pct")
+        if (
+            not isinstance(current_pct, (int, float))
+            or isinstance(current_pct, bool)
+        ):
+            ambiguities.append(
+                {
+                    "field": f"requests[{index}].target_scope",
+                    "question": "当前座椅百分比位置无效，无法安全执行相对物理移动。",
+                }
+            )
+            return None
+
+        position_pct = vehicle_profile.relative_target_pct(
+            axis,
+            current_pct=current_pct,
+            delta=float(delta),
+            direction=direction,
+        )
+        action: dict[str, Any] = {
+            "action_type": "position",
+            "target": target,
+            "axis": axis,
+            "position_pct": position_pct,
+        }
+        if parameter_name == "angle_delta_deg":
+            action["angle_deg"] = ModelPlanAdapter._relative_target_angle(
+                previous,
+                motor,
+                position_pct,
+                float(delta),
+                direction,
+            )
+        return ModelPlanAdapter._preserve_relative_motion(
+            index, request, action, context, ambiguities
+        )
+
+    @staticmethod
+    def _relative_target_angle(
+        previous: dict[str, Any],
+        motor: Any,
+        position_pct: int,
+        delta_deg: float,
+        direction: str,
+    ) -> int | float:
+        start_angle = previous.get("angle_deg")
+        if (
+            isinstance(start_angle, (int, float))
+            and not isinstance(start_angle, bool)
+        ):
+            signed_delta = delta_deg if direction == "increase" else -delta_deg
+            return _clean_number(float(start_angle) + signed_delta)
+        display_angle = motor.display_angle_for_pct(position_pct)
+        if display_angle is None:
+            return _clean_number(delta_deg)
+        return _clean_number(display_angle)
+
     @staticmethod
     def _preserve_relative_motion(
         index: int,
@@ -530,3 +738,7 @@ class ModelPlanAdapter:
             )
         action["motion_intent"] = motion_intent
         return action
+
+
+def _clean_number(value: float) -> int | float:
+    return int(value) if float(value).is_integer() else round(float(value), 1)

src/cognitive_agent/application/vehicle_profile.py

@@ -0,0 +1,166 @@
+"""Vehicle-specific seat calibration profiles."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+import yaml
+
+
+@dataclass(frozen=True)
+class SeatMotorProfile:
+    id: str
+    name: str
+    unit: str
+    range: float
+    reference_zero: str
+    positive_direction: str
+    hall_total_pulses: int
+    display_angle: dict[str, Any] | None = None
+
+    def delta_to_pct(self, delta: float) -> float:
+        if self.range <= 0:
+            raise ValueError(f"motor {self.id} range must be positive")
+        return float(delta) / self.range * 100
+
+    def display_angle_for_pct(self, position_pct: int) -> float | None:
+        if not self.display_angle:
+            return None
+        zero = float(self.display_angle["zero_deg"])
+        maximum = float(self.display_angle["max_deg"])
+        return zero + (maximum - zero) * _clamp_pct(position_pct) / 100
+
+    def position_pct_for_display_angle(self, angle_deg: int | float) -> int:
+        if not self.display_angle:
+            raise ValueError(f"motor {self.id} does not define a display angle range")
+        zero = float(self.display_angle["zero_deg"])
+        maximum = float(self.display_angle["max_deg"])
+        if maximum <= zero:
+            raise ValueError(f"motor {self.id} display angle range must be positive")
+        normalized = (float(angle_deg) - zero) / (maximum - zero)
+        return _clamp_pct(round(normalized * 100))
+
+    def contains_display_angle(self, angle_deg: int | float) -> bool:
+        if not self.display_angle:
+            return False
+        zero = float(self.display_angle["zero_deg"])
+        maximum = float(self.display_angle["max_deg"])
+        return zero <= float(angle_deg) <= maximum
+
+    def public_dict(self) -> dict[str, Any]:
+        payload: dict[str, Any] = {
+            "id": self.id,
+            "name": self.name,
+            "unit": self.unit,
+            "range": _public_number(self.range),
+            "reference_zero": self.reference_zero,
+            "positive_direction": self.positive_direction,
+            "hall_total_pulses": self.hall_total_pulses,
+        }
+        if self.display_angle is not None:
+            payload["display_angle"] = dict(self.display_angle)
+        return payload
+
+
+@dataclass(frozen=True)
+class VehicleProfile:
+    id: str
+    name: str
+    seat_motors: dict[str, SeatMotorProfile]
+
+    def relative_target_pct(
+        self,
+        axis: str,
+        *,
+        current_pct: int | float,
+        delta: int | float,
+        direction: str,
+    ) -> int:
+        motor = self.seat_motors[axis]
+        delta_pct = motor.delta_to_pct(float(delta))
+        if direction == "decrease":
+            delta_pct = -delta_pct
+        elif direction != "increase":
+            raise ValueError(f"unsupported relative direction: {direction}")
+        return _clamp_pct(round(float(current_pct) + delta_pct))
+
+    def display_angle_for_pct(self, axis: str, position_pct: int) -> float | None:
+        return self.seat_motors[axis].display_angle_for_pct(position_pct)
+
+    def public_dict(self) -> dict[str, Any]:
+        return {
+            "id": self.id,
+            "name": self.name,
+            "seat": {
+                "motors": {
+                    key: motor.public_dict()
+                    for key, motor in self.seat_motors.items()
+                }
+            },
+        }
+
+
+class VehicleProfileRegistry:
+    def __init__(self, profile_root: Path) -> None:
+        self.profile_root = profile_root
+        self._profiles = self._load_profiles(profile_root)
+
+    def get(self, vehicle_id: str | None) -> VehicleProfile:
+        resolved = vehicle_id or self.default().id
+        try:
+            return self._profiles[resolved]
+        except KeyError as exc:
+            raise ValueError(f"unknown vehicle profile: {resolved}") from exc
+
+    def default(self) -> VehicleProfile:
+        if "denza_n7" in self._profiles:
+            return self._profiles["denza_n7"]
+        try:
+            return next(iter(self._profiles.values()))
+        except StopIteration as exc:
+            raise ValueError(f"no vehicle profiles found in {self.profile_root}") from exc
+
+    def public_profiles(self) -> list[dict[str, Any]]:
+        return [profile.public_dict() for profile in self._profiles.values()]
+
+    @staticmethod
+    def _load_profiles(profile_root: Path) -> dict[str, VehicleProfile]:
+        profiles: dict[str, VehicleProfile] = {}
+        for path in sorted(profile_root.glob("*.yaml")):
+            data = yaml.safe_load(path.read_text(encoding="utf-8")) or {}
+            profile = _parse_vehicle_profile(data)
+            profiles[profile.id] = profile
+        return profiles
+
+
+def _parse_vehicle_profile(data: dict[str, Any]) -> VehicleProfile:
+    seat = data.get("seat") or {}
+    motors = seat.get("motors") or {}
+    seat_motors = {
+        str(axis): SeatMotorProfile(
+            id=str(axis),
+            name=str(payload["name"]),
+            unit=str(payload["unit"]),
+            range=float(payload["range"]),
+            reference_zero=str(payload["reference_zero"]),
+            positive_direction=str(payload["positive_direction"]),
+            hall_total_pulses=int(payload["hall_total_pulses"]),
+            display_angle=payload.get("display_angle"),
+        )
+        for axis, payload in motors.items()
+    }
+    return VehicleProfile(
+        id=str(data["id"]),
+        name=str(data["name"]),
+        seat_motors=seat_motors,
+    )
+
+
+def _clamp_pct(value: int | float) -> int:
+    return max(0, min(100, int(value)))
+
+
+def _public_number(value: float) -> int | float:
+    return int(value) if value.is_integer() else value

src/cognitive_agent/infrastructure/llm/openai_responses.py

@@ -129,6 +129,13 @@ and current state. The absence of a named preset is never, by itself, an ambigui
 assumptions in decision_summary. Safety validation and hardware eligibility are downstream concerns;
 do not withhold a dry-run candidate merely because a capability is marked high risk.
 
+Runtime context may include mode_profiles. These are trusted long-term mode configuration data,
+not new capabilities. If the user names one of those profiles, the deterministic runtime will inject
+that profile's operations after planning. In that case, return only the user's explicit additional
+goals that are not already represented by the named profile, and mention the named profile in
+decision_summary. If the user names no existing profile, synthesize a temporary plan from atomic
+capabilities as usual.
+
 Return every understood goal in requests using an exact capability name from the registry. Use
 target_scope and operation to preserve the user's intent. Put concrete capability parameters in the
 parameters list when they are known or defensibly inferred. Do not drop catalog_only requests; the

src/cognitive_agent/server.py

@@ -145,9 +145,16 @@ class SeatDemoHandler(BaseHTTPRequestHandler):
                 str(payload.get("provider", "auto")),
                 str(payload["conversation_id"]) if payload.get("conversation_id") else None,
                 str(payload.get("execution_mode", "dry_run")),
-                str(payload["approval_token"])
-                if payload.get("approval_token")
-                else None,
+                approval_token=(
+                    str(payload["approval_token"])
+                    if payload.get("approval_token")
+                    else None
+                ),
+                vehicle_id=(
+                    str(payload["vehicle_id"])
+                    if payload.get("vehicle_id")
+                    else None
+                ),
             )
             self._json(result, HTTPStatus.CREATED)
         except HardwareSafetyError as exc: