pythonocc-step-editor/step_editor/scdm_feature_mapper.py

from __future__ import annotations

import math
from collections.abc import Iterable, Mapping
from pathlib import Path

from .scdm_capabilities import capability_definition, planned_capability_keys, productized_capability_keys
from .scdm_schema import SCDM_CACHE_SCHEMA_VERSION, payload_backend_version, payload_model_fingerprint, read_json, utc_now, write_json


SCDM_FEATURE_CACHE_REVISION = 4


def map_scdm_raw_features(raw_payload: Mapping[str, object]) -> dict[str, object]:
    objects = []
    diagnostics: dict[str, object] = {
        "discovered_not_productized": [],
        "planned_not_productized": [],
        "blocked": [],
    }
    raw_diagnostics = _mapping(raw_payload.get("diagnostics"))
    available_commands = raw_diagnostics.get("availableCommands")
    available_command_names = _available_command_names(available_commands)
    if isinstance(available_commands, list):
        diagnostics["backend_commands"] = [dict(item) for item in available_commands if isinstance(item, Mapping)]
    face_adjacency = raw_diagnostics.get("faceAdjacency")
    if isinstance(face_adjacency, list):
        diagnostics["face_adjacency"] = [dict(item) for item in face_adjacency if isinstance(item, Mapping)]
    edge_geometry_summary = raw_diagnostics.get("edgeGeometrySummary")
    if isinstance(edge_geometry_summary, Mapping):
        diagnostics["edge_geometry_summary"] = dict(edge_geometry_summary)
    feature_inventory = raw_diagnostics.get("featureInventory")
    if isinstance(feature_inventory, Mapping):
        diagnostics["feature_inventory"] = {
            str(key): dict(value)
            for key, value in feature_inventory.items()
            if isinstance(value, Mapping)
        }
    component_instances = raw_diagnostics.get("componentInstances")
    if isinstance(component_instances, list):
        diagnostics["component_instances"] = [dict(item) for item in component_instances if isinstance(item, Mapping)]
    raw_summary = raw_payload.get("summary")
    if isinstance(raw_summary, Mapping):
        diagnostics["raw_summary"] = dict(raw_summary)
    raw_objects = raw_payload.get("objects")
    if not isinstance(raw_objects, list):
        raw_objects = []
    cylindrical_group_objects = _cylindrical_face_group_objects(raw_objects)
    derived_objects = _derived_feature_objects([*raw_objects, *cylindrical_group_objects])
    if derived_objects:
        diagnostics["derived_feature_candidates"] = [
            {
                "objectId": _object_id(item),
                "objectType": str(item.get("objectType") or ""),
                "geometrySignature": geometry_signature(item),
                "rawLimitations": list(item.get("rawLimitations") or []),
            }
            for item in derived_objects
            if isinstance(item, Mapping)
        ]
    diagnostics["geometry_candidate_hints"] = _geometry_candidate_hints(
        [*raw_objects, *cylindrical_group_objects, *derived_objects],
        raw_diagnostics,
    )

    for raw_object in [*raw_objects, *derived_objects]:
        if not isinstance(raw_object, Mapping):
            continue
        capability_keys = productized_capability_keys(raw_object)
        planned_keys = planned_capability_keys(raw_object)
        capabilities = [_capability_payload(key, raw_object, available_command_names=available_command_names) for key in capability_keys]
        capabilities = [item for item in capabilities if item is not None]
        normalized_object = {
            "objectId": _object_id(raw_object),
            "objectType": str(raw_object.get("objectType") or ""),
            "sourceBackendId": str(raw_object.get("backendId") or ""),
            "geometrySignature": geometry_signature(raw_object),
            "capabilities": capabilities,
            "blockReason": str(raw_object.get("blockReason") or ""),
        }
        if capabilities:
            objects.append(normalized_object)
        else:
            diagnostics["discovered_not_productized"].append(
                {
                    "objectId": normalized_object["objectId"],
                    "objectType": normalized_object["objectType"],
                    "sourceBackendId": normalized_object["sourceBackendId"],
                    "reason": "no_productized_capability",
                }
            )
        for key in planned_keys:
            definition = capability_definition(key)
            diagnostics["planned_not_productized"].append(
                {
                    "objectId": normalized_object["objectId"],
                    "objectType": normalized_object["objectType"],
                    "sourceBackendId": normalized_object["sourceBackendId"],
                    "capabilityKey": key,
                    "displayName": definition.display_name if definition else key,
                    "roadmapStage": definition.roadmap_stage if definition else "",
                    "reason": definition.block_reason if definition else "Planned capability is not productized.",
                }
            )
        if normalized_object["blockReason"]:
            diagnostics["blocked"].append(
                {
                    "objectId": normalized_object["objectId"],
                    "objectType": normalized_object["objectType"],
                    "reason": normalized_object["blockReason"],
                }
            )

    for raw_object in cylindrical_group_objects:
        capability_keys = productized_capability_keys(raw_object)
        capabilities = [_capability_payload(key, raw_object, available_command_names=available_command_names) for key in capability_keys]
        capabilities = [item for item in capabilities if item is not None]
        normalized_object = {
            "objectId": _object_id(raw_object),
            "objectType": str(raw_object.get("objectType") or ""),
            "sourceBackendId": str(raw_object.get("backendId") or ""),
            "geometrySignature": geometry_signature(raw_object),
            "capabilities": capabilities,
            "blockReason": str(raw_object.get("blockReason") or ""),
        }
        if capabilities:
            objects.append(normalized_object)
        else:
            diagnostics["discovered_not_productized"].append(
                {
                    "objectId": normalized_object["objectId"],
                    "objectType": normalized_object["objectType"],
                    "sourceBackendId": normalized_object["sourceBackendId"],
                    "reason": "no_productized_capability",
                }
            )

    return {
        "schemaVersion": SCDM_CACHE_SCHEMA_VERSION,
        "mapperRevision": SCDM_FEATURE_CACHE_REVISION,
        "source": "SCDM",
        "createdAt": utc_now(),
        "modelFingerprint": payload_model_fingerprint(raw_payload),
        "backendVersion": payload_backend_version(raw_payload),
        "objects": objects,
        "diagnostics": diagnostics,
    }


def _geometry_candidate_hints(
    raw_objects: list[object],
    raw_diagnostics: Mapping[str, object],
) -> list[dict[str, object]]:
    inventory = _mapping(raw_diagnostics.get("featureInventory"))
    object_counts = _normalized_count_dict(inventory.get("objectTypeCounts"))
    surface_counts = _normalized_count_dict(inventory.get("surfaceTypeCounts"))
    curve_counts = _normalized_count_dict(inventory.get("curveTypeCounts"))
    operation_counts = _normalized_count_dict(inventory.get("operationCounts"))
    scanned = _scan_raw_feature_counts(raw_objects)
    object_counts = _merge_count_dicts(object_counts, scanned["objectTypeCounts"])
    surface_counts = _merge_count_dicts(surface_counts, scanned["surfaceTypeCounts"])
    curve_counts = _merge_count_dicts(curve_counts, scanned["curveTypeCounts"])
    operation_counts = _merge_count_dicts(operation_counts, scanned["operationCounts"])

    edge_summary = _mapping(raw_diagnostics.get("edgeGeometrySummary"))
    edge_kind_counts = _normalized_count_dict(edge_summary.get("edgeKindCounts"))
    circular_edges = _count_value(edge_summary.get("circularEdgeCount")) or edge_kind_counts.get("circular", 0)
    linear_edges = edge_kind_counts.get("linear", 0)
    radius_buckets = edge_summary.get("circularRadiusBuckets")
    radius_bucket_count = len(radius_buckets) if isinstance(radius_buckets, list) else 0
    cylinder_faces = surface_counts.get("cylinder", 0)
    planar_faces = surface_counts.get("plane", 0)
    round_objects = object_counts.get("round", 0) + object_counts.get("fillet", 0)
    slot_objects = object_counts.get("slot", 0) + object_counts.get("obround_slot", 0) + object_counts.get("rectangular_slot", 0)
    boss_objects = object_counts.get("boss", 0) + object_counts.get("cylindrical_boss", 0) + object_counts.get("rectangular_boss", 0)
    chamfer_objects = object_counts.get("chamfer", 0)
    pattern_objects = object_counts.get("pattern", 0) + object_counts.get("linear_pattern", 0)
    shell_objects = object_counts.get("shell", 0) + object_counts.get("thin_wall", 0)

    hints: list[dict[str, object]] = []
    if slot_objects:
        _add_hint_group(
            hints,
            ("slot.width", "slot.depth", "slot.position"),
            slot_objects,
            "medium",
            "SCDM raw 已给出槽类对象；进入 S7 第二批真实命令和样例回测。",
            "scdm-object-type",
        )
    elif cylinder_faces and circular_edges and linear_edges:
        _add_hint_group(
            hints,
            ("slot.width", "slot.depth", "slot.position"),
            min(cylinder_faces, circular_edges),
            "low",
            "当前模型含圆柱面、圆边和直边，具备槽/长圆孔分类材料；还需要确认具体槽组和编辑命令。",
            "scdm-geometry-summary",
        )

    if boss_objects:
        _add_hint_group(
            hints,
            ("boss.height", "boss.diameter", "boss.position"),
            boss_objects,
            "medium",
            "SCDM raw 已给出凸台类对象；进入 S7 第三批真实命令和样例回测。",
            "scdm-object-type",
        )
    elif cylinder_faces and planar_faces:
        _add_hint_group(
            hints,
            ("boss.height", "boss.diameter", "boss.position"),
            cylinder_faces,
            "low",
            "当前模型含圆柱面和平面，具备凸台/外圆分类材料；还需要区分孔、外圆、槽和圆角。",
            "scdm-geometry-summary",
        )

    round_candidate_count = (
        round_objects
        or operation_counts.get("change_round_radius", 0)
        or operation_counts.get("delete_round_or_chamfer", 0)
    )
    if round_candidate_count:
        _add_hint_group(
            hints,
            ("round.radius", "feature.delete_round_or_chamfer"),
            round_candidate_count,
            "medium",
            "SCDM raw 已给出圆角/倒圆候选命令；执行器和专门质量校验仍待回测。",
            "scdm-command-candidate",
        )
    elif circular_edges and radius_bucket_count:
        _add_hint_group(
            hints,
            ("round.radius",),
            circular_edges,
            "low",
            "当前模型含圆边和半径分组，具备圆角链分类材料；还需要确认支撑面和真实 Round 命令。",
            "scdm-edge-summary",
        )

    if chamfer_objects:
        _add_hint_group(
            hints,
            ("chamfer.distance", "feature.delete_round_or_chamfer"),
            chamfer_objects,
            "medium",
            "SCDM raw 已给出倒角对象；进入 S7 第四批真实命令和样例回测。",
            "scdm-object-type",
        )
    elif planar_faces and linear_edges and circular_edges:
        _add_hint_group(
            hints,
            ("chamfer.distance",),
            min(planar_faces, linear_edges),
            "low",
            "当前模型含大量平面和直边，具备倒角分类材料；还需要确认倒角斜面和相邻支撑面。",
            "scdm-geometry-summary",
        )

    if pattern_objects:
        _add_hint_group(
            hints,
            ("pattern.spacing", "pattern.instance_position"),
            pattern_objects,
            "medium",
            "SCDM raw 已给出阵列对象；进入 S7 第五批真实命令和样例回测。",
            "scdm-object-type",
        )
    elif radius_bucket_count and circular_edges >= 4:
        _add_hint_group(
            hints,
            ("pattern.spacing", "pattern.instance_position"),
            radius_bucket_count,
            "low",
            "当前模型含重复圆边半径分组，具备孔/圆角阵列识别材料；还需要确认实例分组和间距方向。",
            "scdm-edge-summary",
        )

    if shell_objects:
        _add_hint_group(
            hints,
            ("shell.thickness",),
            shell_objects,
            "medium",
            "SCDM raw 已给出壳体/薄壁对象；进入 S7 第五批真实命令和样例回测。",
            "scdm-object-type",
        )
    elif planar_faces >= 2:
        _add_hint_group(
            hints,
            ("shell.thickness",),
            planar_faces,
            "low",
            "当前模型含多组平面，具备薄壁/相对面厚度分类材料；还需要确认成对壁面和偏移方向。",
            "scdm-geometry-summary",
        )
    return hints


def _add_hint_group(
    hints: list[dict[str, object]],
    keys: tuple[str, ...],
    count: int,
    confidence: str,
    reason: str,
    source: str,
) -> None:
    evidence_count = max(0, int(count))
    if evidence_count <= 0:
        return
    for key in keys:
        definition = capability_definition(key)
        if definition is None or definition.productized:
            continue
        hints.append(
            {
                "capabilityKey": key,
                "displayName": definition.display_name,
                "roadmapStage": definition.roadmap_stage,
                "evidenceCount": evidence_count,
                "confidence": confidence,
                "source": source,
                "reason": reason,
            }
        )


def _scan_raw_feature_counts(raw_objects: list[object]) -> dict[str, dict[str, int]]:
    result = {
        "objectTypeCounts": {},
        "surfaceTypeCounts": {},
        "curveTypeCounts": {},
        "operationCounts": {},
    }
    for raw_object in raw_objects:
        if not isinstance(raw_object, Mapping):
            continue
        _increment_count(result["objectTypeCounts"], raw_object.get("objectType"))
        geometry = _mapping(raw_object.get("geometry"))
        if geometry.get("surfaceType") is not None:
            _increment_count(result["surfaceTypeCounts"], geometry.get("surfaceType"))
        if geometry.get("curveType") is not None:
            _increment_count(result["curveTypeCounts"], geometry.get("curveType"))
        for command in raw_object.get("backendCommandCandidates", []) or []:
            if isinstance(command, Mapping) and command.get("enabled") is not False:
                _increment_count(result["operationCounts"], command.get("operation"))
    return result


def _normalized_count_dict(value: object) -> dict[str, int]:
    if not isinstance(value, Mapping):
        return {}
    result: dict[str, int] = {}
    for key, count in value.items():
        normalized = _count_key(key)
        number = _count_value(count)
        if normalized and number > 0:
            result[normalized] = result.get(normalized, 0) + number
    return result


def _merge_count_dicts(primary: Mapping[str, int], fallback: Mapping[str, int]) -> dict[str, int]:
    result = {str(key): int(value) for key, value in primary.items() if int(value) > 0}
    for key, value in fallback.items():
        text = str(key)
        number = int(value)
        if number > result.get(text, 0):
            result[text] = number
    return result


def _increment_count(counts: dict[str, int], key: object) -> None:
    normalized = _count_key(key)
    if normalized:
        counts[normalized] = counts.get(normalized, 0) + 1


def _count_key(value: object) -> str:
    return str(value or "").strip().lower()


def _count_value(value: object) -> int:
    try:
        return int(value)
    except (TypeError, ValueError):
        return 0


def map_scdm_raw_features_file(raw_path: str | Path, cache_path: str | Path | None = None) -> dict[str, object]:
    cache = map_scdm_raw_features(read_json(raw_path))
    if cache_path is not None:
        write_json(cache_path, cache)
    return cache


def _cylindrical_face_group_objects(raw_objects: list[object]) -> list[dict[str, object]]:
    groups: dict[tuple[object, ...], list[Mapping[str, object]]] = {}
    for raw_object in raw_objects:
        if not isinstance(raw_object, Mapping):
            continue
        geometry = _mapping(raw_object.get("geometry"))
        if _surface_key(geometry.get("surfaceType") or geometry.get("surface")) != "cylinder":
            continue
        center = _rounded_vector(geometry.get("center") or geometry.get("axisCenter"))
        axis = _canonical_axis(_rounded_vector(geometry.get("axis") or geometry.get("normal")))
        radius = _rounded_number(geometry.get("radius"))
        if len(center) != 3 or len(axis) != 3 or radius is None or radius <= 0:
            continue
        topology = _mapping(raw_object.get("topologyHint"))
        body_index = _int_or_none(_first_present(topology.get("bodyIndex"), geometry.get("bodyIndex")))
        key = (body_index, tuple(center), tuple(axis), radius)
        groups.setdefault(key, []).append(raw_object)

    result: list[dict[str, object]] = []
    for (_body_index, center, axis, radius), members in groups.items():
        if len(members) < 2:
            continue
        locators = [_locator_from_raw_object(member) for member in members]
        locators = [item for item in locators if item]
        topology = {
            "bodyIndex": _body_index,
            "faceOrdinal": _first_int(item.get("faceOrdinal") for item in locators),
            "faceOrdinals": [item["faceOrdinal"] for item in locators if item.get("faceOrdinal") is not None],
            "globalFaceOrdinal": _first_int(item.get("globalFaceOrdinal") for item in locators),
            "globalFaceOrdinals": [item["globalFaceOrdinal"] for item in locators if item.get("globalFaceOrdinal") is not None],
            "faceIds": _merge_ints(_mapping(member.get("topologyHint")).get("faceIds") for member in members),
            "scdmFaceLocators": locators,
        }
        source_ids = [str(member.get("backendId") or "") for member in members if str(member.get("backendId") or "")]
        result.append(
            {
                "backendId": "cylindrical_group:" + "|".join(source_ids),
                "objectType": "cylindrical_face_group",
                "geometry": {
                    "surfaceType": "cylinder",
                    "center": list(center),
                    "axis": list(axis),
                    "radius": radius,
                    "diameter": radius * 2.0,
                    "sourceBackendIds": source_ids,
                },
                "topologyHint": topology,
                "backendCommandCandidates": [
                    {"operation": "change_hole_diameter", "enabled": True, "parameterFields": {"diameter": radius * 2.0}},
                    {"operation": "move_hole_axis", "enabled": True, "parameterFields": {"center": list(center)}},
                    {"operation": "fill_feature", "enabled": True, "parameterFields": {}},
                ],
                "rawLimitations": [],
            }
        )
    return result


def _derived_feature_objects(source_objects: list[object]) -> list[dict[str, object]]:
    return [
        *_derived_linear_pattern_objects(source_objects),
        *_derived_body_linear_pattern_objects(source_objects),
        *_derived_thin_wall_objects(source_objects),
    ]


def _derived_linear_pattern_objects(source_objects: list[object]) -> list[dict[str, object]]:
    groups: dict[tuple[object, ...], list[dict[str, object]]] = {}
    for raw_object in source_objects:
        if not isinstance(raw_object, Mapping):
            continue
        object_type = str(raw_object.get("objectType") or "").strip().lower()
        if object_type not in {"hole", "cylindrical_hole", "cylindrical_face_group"}:
            continue
        geometry = _mapping(raw_object.get("geometry"))
        center = _rounded_vector(geometry.get("center") or geometry.get("axisCenter"))
        axis = _canonical_axis(_rounded_vector(geometry.get("axis") or geometry.get("normal")))
        radius = _rounded_number(geometry.get("radius"))
        if radius is None:
            diameter = _rounded_number(geometry.get("diameter"))
            radius = diameter * 0.5 if diameter is not None else None
        if len(center) != 3 or len(axis) != 3 or radius is None or radius <= 0:
            continue
        topology = _mapping(raw_object.get("topologyHint"))
        locators = _face_locators(topology.get("scdmFaceLocators"))
        fallback_locator = _locator_from_raw_object(raw_object)
        if not locators and any(fallback_locator.get(key) is not None for key in ("bodyIndex", "faceOrdinal", "globalFaceOrdinal")):
            locators = [fallback_locator]
        key = (
            _int_or_none(_first_present(topology.get("bodyIndex"), geometry.get("bodyIndex"))),
            tuple(axis),
            round(float(radius), 6),
        )
        groups.setdefault(key, []).append(
            {
                "objectId": _object_id(raw_object),
                "center": center,
                "axis": axis,
                "radius": float(radius),
                "faceIds": _int_list(topology.get("faceIds") or geometry.get("faceIds")),
                "faceOrdinals": _int_list(topology.get("faceOrdinals") or [topology.get("faceOrdinal")]),
                "globalFaceOrdinals": _int_list(
                    topology.get("globalFaceOrdinals")
                    or [topology.get("globalFaceOrdinal"), topology.get("faceOrdinal")]
                ),
                "scdmFaceLocators": locators,
            }
        )

    result: list[dict[str, object]] = []
    seen: set[tuple[object, ...]] = set()
    for (body_index, _axis, radius), instances in groups.items():
        if len(instances) < 3:
            continue
        candidates = _linear_pattern_candidates(instances)
        for candidate in candidates:
            member_ids = tuple(str(item.get("objectId") or "") for item in candidate["members"])
            pattern_axis = tuple(round(float(value), 6) for value in candidate["direction"])
            spacing = round(float(candidate["spacing"]), 6)
            key = (body_index, radius, pattern_axis, spacing, member_ids)
            if key in seen:
                continue
            seen.add(key)
            face_ids: list[int] = []
            global_face_ordinals: list[int] = []
            centers = []
            pattern_instances: list[dict[str, object]] = []
            for member in candidate["members"]:
                centers.append(list(member["center"]))
                face_ids.extend(_int_list(member.get("faceIds")))
                global_face_ordinals.extend(_int_list(member.get("globalFaceOrdinals")))
                pattern_instances.append(
                    {
                        "sourceObjectId": str(member.get("objectId") or ""),
                        "center": list(member["center"]),
                        "faceIds": _int_list(member.get("faceIds")),
                        "faceOrdinals": _int_list(member.get("faceOrdinals")),
                        "globalFaceOrdinals": _int_list(member.get("globalFaceOrdinals")),
                        "scdmFaceLocators": _face_locators(member.get("scdmFaceLocators")),
                    }
                )
            center = [
                round(sum(float(item[index]) for item in centers) / len(centers), 6)
                for index in range(3)
            ]
            result.append(
                {
                    "backendId": "derived:linear_pattern:" + "|".join(member_ids),
                    "objectType": "linear_pattern",
                    "geometry": {
                        "center": center,
                        "axis": list(pattern_axis),
                        "spacing": spacing,
                        "pitch": spacing,
                        "instanceCount": len(candidate["members"]),
                        "instanceCenters": centers,
                        "radius": radius,
                        "diameter": radius * 2.0,
                        "sourceObjectIds": list(member_ids),
                        "patternInstances": pattern_instances,
                    },
                    "topologyHint": {
                        "bodyIndex": body_index,
                        "faceIds": sorted(set(face_ids)),
                        "globalFaceOrdinals": sorted(set(global_face_ordinals)),
                    },
                    "backendCommandCandidates": [
                        {"operation": "change_pattern_spacing", "enabled": True, "parameterFields": {"spacing": spacing}},
                    ],
                    "rawLimitations": [
                        "Derived from repeated cylindrical feature centers; spacing edit uses SCDM Move on each instance.",
                    ],
                }
            )
            if len(result) >= 24:
                return result
    return result


def _linear_pattern_candidates(instances: list[dict[str, object]]) -> list[dict[str, object]]:
    if len(instances) < 3:
        return []
    points = [list(item["center"]) for item in instances]
    diagonal = _point_cloud_diagonal(points)
    tolerance = max(diagonal * 1.0e-4, max(float(item.get("radius") or 0.0) for item in instances) * 0.02, 1.0e-5)
    candidates: list[dict[str, object]] = []
    for left_index in range(len(instances)):
        for right_index in range(left_index + 1, len(instances)):
            p0 = points[left_index]
            p1 = points[right_index]
            direction = _unit_vector([p1[index] - p0[index] for index in range(3)])
            if len(direction) != 3:
                continue
            collinear: list[tuple[float, dict[str, object]]] = []
            for instance in instances:
                point = list(instance["center"])
                delta = [point[index] - p0[index] for index in range(3)]
                projection = sum(delta[index] * direction[index] for index in range(3))
                nearest = [p0[index] + projection * direction[index] for index in range(3)]
                distance = math.sqrt(sum((point[index] - nearest[index]) ** 2 for index in range(3)))
                if distance <= tolerance:
                    collinear.append((projection, instance))
            if len(collinear) < 3:
                continue
            collinear.sort(key=lambda item: item[0])
            spacings = [
                collinear[index + 1][0] - collinear[index][0]
                for index in range(len(collinear) - 1)
                if collinear[index + 1][0] - collinear[index][0] > tolerance
            ]
            if len(spacings) < 2:
                continue
            spacing = sum(spacings) / len(spacings)
            if spacing <= tolerance:
                continue
            spacing_tolerance = max(abs(spacing) * 0.03, tolerance * 3.0)
            if any(abs(value - spacing) > spacing_tolerance for value in spacings):
                continue
            members = [item[1] for item in collinear]
            canonical_direction = _canonical_axis([round(value, 6) for value in direction])
            if not canonical_direction:
                continue
            member_ids = tuple(str(item.get("objectId") or "") for item in members)
            if any(set(member_ids) == set(str(member.get("objectId") or "") for member in existing["members"]) for existing in candidates):
                continue
            candidates.append({"members": members, "direction": canonical_direction, "spacing": spacing})
            if len(candidates) >= 12:
                return candidates
    candidates.sort(key=lambda item: (-len(item["members"]), float(item["spacing"])))
    return candidates


def _derived_body_linear_pattern_objects(source_objects: list[object]) -> list[dict[str, object]]:
    body_summaries = _body_pattern_source_instances(source_objects)
    groups: dict[tuple[object, ...], list[dict[str, object]]] = {}
    for item in body_summaries:
        signature = tuple(item.get("shapeSignature") or ())
        if not signature:
            continue
        groups.setdefault(signature, []).append(item)

    result: list[dict[str, object]] = []
    seen: set[tuple[object, ...]] = set()
    for signature, instances in groups.items():
        if len(instances) < 3:
            continue
        candidates = _linear_pattern_candidates(instances)
        for candidate in candidates:
            members = list(candidate["members"])
            member_ids = tuple(str(item.get("objectId") or "") for item in members)
            pattern_axis = tuple(round(float(value), 6) for value in candidate["direction"])
            spacing = round(float(candidate["spacing"]), 6)
            key = (signature, pattern_axis, spacing, member_ids)
            if key in seen:
                continue
            seen.add(key)
            centers = [list(item["center"]) for item in members]
            center = [
                round(sum(float(item[index]) for item in centers) / len(centers), 6)
                for index in range(3)
            ]
            face_ids = sorted(set(value for item in members for value in _int_list(item.get("faceIds"))))
            face_ordinals = sorted(set(value for item in members for value in _int_list(item.get("faceOrdinals"))))
            global_face_ordinals = sorted(set(value for item in members for value in _int_list(item.get("globalFaceOrdinals"))))
            body_indices = [
                value
                for value in (_int_or_none(item.get("bodyIndex")) for item in members)
                if value is not None
            ]
            pattern_instances = [
                {
                    "sourceObjectId": str(item.get("objectId") or ""),
                    "instanceKind": "body",
                    "center": list(item["center"]),
                    "bodyIndex": _int_or_none(item.get("bodyIndex")),
                    "bodyLocators": _body_locators(item.get("bodyLocators")),
                    "componentLocators": _component_locators(item.get("componentLocators") or item.get("bodyLocators")),
                    "faceIds": _int_list(item.get("faceIds")),
                    "faceOrdinals": _int_list(item.get("faceOrdinals")),
                    "globalFaceOrdinals": _int_list(item.get("globalFaceOrdinals")),
                    "scdmFaceLocators": _face_locators(item.get("scdmFaceLocators")),
                }
                for item in members
            ]
            result.append(
                {
                    "backendId": "derived:body_linear_pattern:" + "|".join(member_ids),
                    "objectType": "linear_pattern",
                    "geometry": {
                        "patternKind": "body",
                        "instanceKind": "body",
                        "center": center,
                        "axis": list(pattern_axis),
                        "spacing": spacing,
                        "pitch": spacing,
                        "instanceCount": len(members),
                        "instanceCenters": centers,
                        "sourceObjectIds": list(member_ids),
                        "bodyIndices": body_indices,
                        "componentInstanceCount": sum(
                            1
                            for item in members
                            if _component_locators(item.get("componentLocators") or item.get("bodyLocators"))
                        ),
                        "patternInstances": pattern_instances,
                    },
                    "topologyHint": {
                        "bodyIndices": body_indices,
                        "faceIds": face_ids,
                        "faceOrdinals": face_ordinals,
                        "globalFaceOrdinals": global_face_ordinals,
                    },
                    "backendCommandCandidates": [
                        {"operation": "change_pattern_spacing", "enabled": True, "parameterFields": {"spacing": spacing}},
                    ],
                    "rawLimitations": [
                        "Derived from repeated SCDM Body/Part geometry centers; spacing edit opens only when every member has a component occurrence locator.",
                    ],
                }
            )
            if len(result) >= 24:
                return result
    return result


def _body_pattern_source_instances(source_objects: list[object]) -> list[dict[str, object]]:
    explicit: list[dict[str, object]] = []
    grouped_faces: dict[int, dict[str, object]] = {}
    for raw_object in source_objects:
        if not isinstance(raw_object, Mapping):
            continue
        object_type = str(raw_object.get("objectType") or "").strip().lower()
        geometry = _mapping(raw_object.get("geometry"))
        topology = _mapping(raw_object.get("topologyHint"))
        body_index = _int_or_none(_first_present(topology.get("bodyIndex"), geometry.get("bodyIndex")))
        if object_type in {"body", "part", "component"} and body_index is not None:
            center = _rounded_vector(geometry.get("center") or geometry.get("bboxCenter") or geometry.get("axisCenter"))
            if len(center) == 3:
                explicit.append(
                    {
                        "objectId": _object_id(raw_object),
                        "center": center,
                        "bodyIndex": body_index,
                        "bodyLocators": _body_locators(topology.get("bodyLocators") or geometry.get("bodyLocators") or [{"bodyIndex": body_index}]),
                        "componentLocators": _component_locators(topology.get("componentLocators") or geometry.get("componentLocators")),
                        "faceIds": _int_list(topology.get("faceIds") or geometry.get("faceIds")),
                        "faceOrdinals": _int_list(topology.get("faceOrdinals") or geometry.get("faceOrdinals")),
                        "globalFaceOrdinals": _int_list(topology.get("globalFaceOrdinals") or geometry.get("globalFaceOrdinals")),
                        "scdmFaceLocators": _face_locators(topology.get("scdmFaceLocators") or geometry.get("scdmFaceLocators")),
                        "shapeSignature": _body_shape_signature_from_object(raw_object),
                    }
                )
            continue
        if object_type != "face" or body_index is None:
            continue
        center = _rounded_vector(geometry.get("center") or geometry.get("axisCenter"))
        if len(center) != 3:
            continue
        item = grouped_faces.setdefault(
            int(body_index),
            {
                "objectId": f"body:{int(body_index)}",
                "bodyIndex": int(body_index),
                "centers": [],
                "faceIds": [],
                "faceOrdinals": [],
                "globalFaceOrdinals": [],
                "scdmFaceLocators": [],
                "bodyLocators": [],
                "componentLocators": [],
                "surfaceTypeCounts": {},
                "radiusBuckets": {},
            },
        )
        item["centers"].append(center)  # type: ignore[index,union-attr]
        item["faceIds"].extend(_int_list(topology.get("faceIds") or geometry.get("faceIds")))  # type: ignore[index,union-attr]
        face_ordinal = _int_or_none(_first_present(topology.get("faceOrdinal"), geometry.get("faceOrdinal")))
        if face_ordinal is not None:
            item["faceOrdinals"].append(face_ordinal)  # type: ignore[index,union-attr]
        global_face_ordinal = _int_or_none(_first_present(topology.get("globalFaceOrdinal"), geometry.get("globalFaceOrdinal")))
        if global_face_ordinal is not None:
            item["globalFaceOrdinals"].append(global_face_ordinal)  # type: ignore[index,union-attr]
        locator = _locator_from_raw_object(raw_object)
        if any(locator.get(key) is not None for key in ("bodyIndex", "faceOrdinal", "globalFaceOrdinal")):
            item["scdmFaceLocators"].append(locator)  # type: ignore[index,union-attr]
        item["bodyLocators"].extend(_body_locators(topology.get("bodyLocators") or geometry.get("bodyLocators")))  # type: ignore[index,union-attr]
        item["componentLocators"].extend(_component_locators(topology.get("componentLocators") or geometry.get("componentLocators")))  # type: ignore[index,union-attr]
        surface_key = _surface_key(geometry.get("surfaceType") or geometry.get("surface"))
        if surface_key:
            counts = item["surfaceTypeCounts"]  # type: ignore[index]
            counts[surface_key] = counts.get(surface_key, 0) + 1  # type: ignore[union-attr]
        radius = _rounded_number(geometry.get("radius"))
        if radius is not None and radius > 0:
            buckets = item["radiusBuckets"]  # type: ignore[index]
            bucket_key = round(float(radius), 6)
            buckets[bucket_key] = buckets.get(bucket_key, 0) + 1  # type: ignore[union-attr]
    if explicit:
        return [item for item in explicit if item.get("shapeSignature")]
    result: list[dict[str, object]] = []
    for body_index, item in grouped_faces.items():
        centers = [list(center) for center in item.get("centers", []) if isinstance(center, list) and len(center) == 3]
        locators = _face_locators(item.get("scdmFaceLocators"))
        if len(centers) < 2 or not locators:
            continue
        center = [
            round(sum(float(point[index]) for point in centers) / len(centers), 6)
            for index in range(3)
        ]
        signature = _body_shape_signature_from_summary(item)
        if not signature:
            continue
        body_locators = _body_locators(item.get("bodyLocators")) or [{"bodyIndex": int(body_index)}]
        component_locators = _component_locators(item.get("componentLocators") or body_locators)
        result.append(
            {
                "objectId": str(item.get("objectId") or f"body:{body_index}"),
                "center": center,
                "bodyIndex": int(body_index),
                "bodyLocators": body_locators,
                "componentLocators": component_locators,
                "faceIds": sorted(set(_int_list(item.get("faceIds")))),
                "faceOrdinals": sorted(set(_int_list(item.get("faceOrdinals")))),
                "globalFaceOrdinals": sorted(set(_int_list(item.get("globalFaceOrdinals")))),
                "scdmFaceLocators": locators,
                "shapeSignature": signature,
            }
        )
    return result


def _body_shape_signature_from_object(raw_object: Mapping[str, object]) -> tuple[object, ...]:
    geometry = _mapping(raw_object.get("geometry"))
    bbox_size = _rounded_vector(geometry.get("bboxSize") or geometry.get("size") or geometry.get("boxSize"))
    face_count = _int_or_none(geometry.get("faceCount"))
    edge_count = _int_or_none(geometry.get("edgeCount"))
    signature: list[object] = []
    if len(bbox_size) == 3:
        signature.append(("bbox", tuple(round(abs(float(value)), 5) for value in sorted(bbox_size))))
    if face_count is not None:
        signature.append(("faces", face_count))
    if edge_count is not None:
        signature.append(("edges", edge_count))
    return tuple(signature)


def _body_shape_signature_from_summary(summary: Mapping[str, object]) -> tuple[object, ...]:
    face_count = len(_int_list(summary.get("faceOrdinals"))) or len(_face_locators(summary.get("scdmFaceLocators")))
    if face_count < 2:
        return ()
    surface_counts = _mapping(summary.get("surfaceTypeCounts"))
    radius_buckets = _mapping(summary.get("radiusBuckets"))
    return (
        ("faces", face_count),
        ("surfaces", tuple(sorted((str(key), int(value)) for key, value in surface_counts.items()))),
        ("radii", tuple(sorted((float(key), int(value)) for key, value in radius_buckets.items()))),
    )


def _derived_thin_wall_objects(source_objects: list[object]) -> list[dict[str, object]]:
    groups: dict[tuple[object, ...], list[dict[str, object]]] = {}
    for raw_object in source_objects:
        if not isinstance(raw_object, Mapping):
            continue
        if str(raw_object.get("objectType") or "").strip().lower() != "face":
            continue
        geometry = _mapping(raw_object.get("geometry"))
        if _surface_key(geometry.get("surfaceType") or geometry.get("surface")) != "plane":
            continue
        center = _rounded_vector(geometry.get("center") or geometry.get("axisCenter"))
        normal = _unit_vector(_rounded_vector(geometry.get("normal") or geometry.get("axis")))
        axis = _canonical_axis(normal)
        if len(center) != 3 or len(axis) != 3:
            continue
        locator = _locator_from_raw_object(raw_object)
        if not any(locator.get(key) is not None for key in ("bodyIndex", "faceOrdinal", "globalFaceOrdinal")):
            continue
        topology = _mapping(raw_object.get("topologyHint"))
        body_index = _int_or_none(_first_present(topology.get("bodyIndex"), geometry.get("bodyIndex")))
        key = (body_index, tuple(axis))
        groups.setdefault(key, []).append(
            {
                "objectId": _object_id(raw_object),
                "center": center,
                "axis": axis,
                "faceIds": _int_list(topology.get("faceIds") or geometry.get("faceIds")),
                "faceOrdinal": _int_or_none(_first_present(topology.get("faceOrdinal"), geometry.get("faceOrdinal"))),
                "globalFaceOrdinal": _int_or_none(_first_present(topology.get("globalFaceOrdinal"), geometry.get("globalFaceOrdinal"))),
                "scdmFaceLocators": [locator],
            }
        )

    result: list[dict[str, object]] = []
    seen: set[tuple[object, ...]] = set()
    for (body_index, axis_key), faces in groups.items():
        if len(faces) < 2:
            continue
        candidates = _thin_wall_pair_candidates(faces, list(axis_key))
        for candidate in candidates:
            left = candidate["left"]
            right = candidate["right"]
            source_ids = (str(left.get("objectId") or ""), str(right.get("objectId") or ""))
            ordinals = tuple(
                value
                for value in (
                    _int_or_none(left.get("faceOrdinal")),
                    _int_or_none(right.get("faceOrdinal")),
                )
                if value is not None
            )
            global_ordinals = tuple(
                value
                for value in (
                    _int_or_none(left.get("globalFaceOrdinal")),
                    _int_or_none(right.get("globalFaceOrdinal")),
                )
                if value is not None
            )
            key = (body_index, tuple(axis_key), round(float(candidate["thickness"]), 6), tuple(sorted(source_ids)))
            if key in seen:
                continue
            seen.add(key)
            locators = [
                locator
                for item in (left, right)
                for locator in _face_locators(item.get("scdmFaceLocators"))
            ]
            centers = [list(left["center"]), list(right["center"])]
            center = [
                round((float(centers[0][index]) + float(centers[1][index])) * 0.5, 6)
                for index in range(3)
            ]
            result.append(
                {
                    "backendId": "derived:thin_wall:" + "|".join(source_ids),
                    "objectType": "thin_wall",
                    "geometry": {
                        "surfaceType": "plane",
                        "center": center,
                        "axis": list(axis_key),
                        "thicknessAxis": list(axis_key),
                        "thickness": round(float(candidate["thickness"]), 6),
                        "wallFaceCenters": centers,
                        "sourceObjectIds": list(source_ids),
                        "wallFaceLocators": locators,
                    },
                    "topologyHint": {
                        "bodyIndex": body_index,
                        "faceIds": sorted(set(_int_list(left.get("faceIds")) + _int_list(right.get("faceIds")))),
                        "faceOrdinals": list(ordinals),
                        "globalFaceOrdinals": list(global_ordinals),
                        "scdmFaceLocators": locators,
                        "wallFaceLocators": locators,
                    },
                    "backendCommandCandidates": [
                        {"operation": "change_shell_thickness", "enabled": True, "parameterFields": {"thickness": round(float(candidate["thickness"]), 6)}},
                    ],
                    "rawLimitations": [
                        "Derived from paired planar SCDM faces; shell.thickness remains non-productized until real SCDM edit samples pass.",
                    ],
                }
            )
            if len(result) >= 24:
                return result
    return result


def _thin_wall_pair_candidates(faces: list[dict[str, object]], axis: list[float]) -> list[dict[str, object]]:
    pairs: list[dict[str, object]] = []
    for left_index in range(len(faces)):
        for right_index in range(left_index + 1, len(faces)):
            left = faces[left_index]
            right = faces[right_index]
            delta = [
                float(right["center"][index]) - float(left["center"][index])
                for index in range(3)
            ]
            along = sum(delta[index] * axis[index] for index in range(3))
            thickness = abs(along)
            if thickness <= 1.0e-9:
                continue
            delta_length_sq = sum(value * value for value in delta)
            perpendicular = math.sqrt(max(delta_length_sq - along * along, 0.0))
            if perpendicular > max(thickness * 0.25, 1.0e-4):
                continue
            pairs.append({"left": left, "right": right, "thickness": thickness, "perpendicular": perpendicular})
    if not pairs:
        return []
    min_thickness = min(float(item["thickness"]) for item in pairs)
    tolerance = max(min_thickness * 0.05, 1.0e-6)
    filtered = [item for item in pairs if abs(float(item["thickness"]) - min_thickness) <= tolerance]
    filtered.sort(key=lambda item: (float(item["thickness"]), float(item["perpendicular"])))
    return filtered[:12]


def _point_cloud_diagonal(points: list[list[float]]) -> float:
    if not points:
        return 0.0
    mins = [min(float(point[index]) for point in points) for index in range(3)]
    maxs = [max(float(point[index]) for point in points) for index in range(3)]
    return math.sqrt(sum((maxs[index] - mins[index]) ** 2 for index in range(3)))


def _unit_vector(values: list[float]) -> list[float]:
    length = math.sqrt(sum(float(item) * float(item) for item in values))
    if length <= 1.0e-12:
        return []
    return [float(item) / length for item in values]


def geometry_signature(raw_object: Mapping[str, object]) -> dict[str, object]:
    geometry = _mapping(raw_object.get("geometry"))
    topology = _mapping(raw_object.get("topologyHint"))
    round_info = _mapping(geometry.get("roundInfo"))
    chamfer_info = _mapping(geometry.get("chamferInfo"))
    slot_info = _mapping(geometry.get("slotInfo"))
    return {
        "objectType": str(raw_object.get("objectType") or ""),
        "patternKind": str(geometry.get("patternKind") or topology.get("patternKind") or ""),
        "instanceKind": str(geometry.get("instanceKind") or topology.get("instanceKind") or ""),
        "faceIds": _int_list(topology.get("faceIds") or geometry.get("faceIds")),
        "edgeIds": _int_list(topology.get("edgeIds") or geometry.get("edgeIds")),
        "bodyIndex": _int_or_none(_first_present(topology.get("bodyIndex"), geometry.get("bodyIndex"))),
        "bodyIndices": _int_list(topology.get("bodyIndices") or geometry.get("bodyIndices")),
        "faceOrdinal": _int_or_none(_first_present(topology.get("faceOrdinal"), geometry.get("faceOrdinal"))),
        "faceOrdinals": _int_list(topology.get("faceOrdinals") or geometry.get("faceOrdinals")),
        "edgeOrdinal": _int_or_none(_first_present(topology.get("edgeOrdinal"), geometry.get("edgeOrdinal"))),
        "globalFaceOrdinal": _int_or_none(_first_present(topology.get("globalFaceOrdinal"), geometry.get("globalFaceOrdinal"))),
        "globalFaceOrdinals": _int_list(topology.get("globalFaceOrdinals") or geometry.get("globalFaceOrdinals")),
        "globalEdgeOrdinal": _int_or_none(_first_present(topology.get("globalEdgeOrdinal"), geometry.get("globalEdgeOrdinal"))),
        "adjacentFaceOrdinals": _int_list(topology.get("adjacentFaceOrdinals") or geometry.get("adjacentFaceOrdinals")),
        "adjacentFaceCount": _int_or_none(_first_present(topology.get("adjacentFaceCount"), geometry.get("adjacentFaceCount"))),
        "heightFaceOrdinals": _int_list(
            topology.get("heightFaceOrdinals")
            or topology.get("topFaceOrdinals")
            or geometry.get("heightFaceOrdinals")
            or geometry.get("topFaceOrdinals")
        ),
        "globalHeightFaceOrdinals": _int_list(
            topology.get("globalHeightFaceOrdinals")
            or topology.get("globalTopFaceOrdinals")
            or geometry.get("globalHeightFaceOrdinals")
            or geometry.get("globalTopFaceOrdinals")
        ),
        "depthFaceOrdinals": _int_list(
            topology.get("depthFaceOrdinals")
            or topology.get("bottomFaceOrdinals")
            or geometry.get("depthFaceOrdinals")
            or geometry.get("bottomFaceOrdinals")
        ),
        "globalDepthFaceOrdinals": _int_list(
            topology.get("globalDepthFaceOrdinals")
            or topology.get("globalBottomFaceOrdinals")
            or geometry.get("globalDepthFaceOrdinals")
            or geometry.get("globalBottomFaceOrdinals")
        ),
        "diameterFaceOrdinals": _int_list(
            topology.get("diameterFaceOrdinals")
            or topology.get("sideFaceOrdinals")
            or geometry.get("diameterFaceOrdinals")
            or geometry.get("sideFaceOrdinals")
        ),
        "globalDiameterFaceOrdinals": _int_list(
            topology.get("globalDiameterFaceOrdinals")
            or topology.get("globalSideFaceOrdinals")
            or geometry.get("globalDiameterFaceOrdinals")
            or geometry.get("globalSideFaceOrdinals")
        ),
        "surfaceType": str(geometry.get("surfaceType") or geometry.get("surface") or ""),
        "curveType": str(geometry.get("curveType") or ""),
        "center": _rounded_vector(geometry.get("center") or geometry.get("axisCenter")),
        "axis": _rounded_vector(geometry.get("axis") or geometry.get("normal")),
        "startPoint": _rounded_vector(geometry.get("startPoint")),
        "endPoint": _rounded_vector(geometry.get("endPoint")),
        "midPoint": _rounded_vector(geometry.get("midPoint")),
        "length": _rounded_number(geometry.get("length")),
        "width": _rounded_number(geometry.get("width")),
        "depth": _rounded_number(_first_present(slot_info.get("depth"), geometry.get("depth"))),
        "depthAxis": _rounded_vector(
            _first_present(
                slot_info.get("depthAxis"),
                slot_info.get("depthDirection"),
                geometry.get("depthAxis"),
                geometry.get("depthDirection"),
            )
        ),
        "height": _rounded_number(geometry.get("height")),
        "thickness": _rounded_number(geometry.get("thickness")),
        "thicknessAxis": _rounded_vector(geometry.get("thicknessAxis") or geometry.get("thicknessDirection")),
        "distance": _rounded_number(_first_present(chamfer_info.get("distance"), geometry.get("distance"), geometry.get("offset"))),
        "distance1": _rounded_number(_first_present(chamfer_info.get("distance1"), geometry.get("distance1"))),
        "distance2": _rounded_number(_first_present(chamfer_info.get("distance2"), geometry.get("distance2"))),
        "radius": _rounded_number(_first_present(round_info.get("radius"), geometry.get("radius"))),
        "diameter": _rounded_number(_first_present(round_info.get("diameter"), geometry.get("diameter"))),
        "roundType": str(round_info.get("type") or geometry.get("roundType") or ""),
        "isConstantRound": _bool_or_none(_first_present(round_info.get("isConstant"), geometry.get("isConstant"), geometry.get("constant"))),
        "isRound": _bool_or_none(_first_present(round_info.get("isRound"), geometry.get("isRound"))),
        "chamferType": str(chamfer_info.get("type") or geometry.get("chamferType") or ""),
        "isEqualDistanceChamfer": _bool_or_none(
            _first_present(
                chamfer_info.get("isEqualDistance"),
                chamfer_info.get("isSymmetric"),
                geometry.get("isEqualDistanceChamfer"),
                geometry.get("isEqualDistance"),
            )
        ),
        "spacing": _rounded_number(geometry.get("spacing")),
        "pitch": _rounded_number(geometry.get("pitch")),
        "instanceCount": _int_or_none(geometry.get("instanceCount")),
        "instanceCenters": _rounded_vector_list(geometry.get("instanceCenters")),
        "patternInstances": _pattern_instances(geometry.get("patternInstances") or topology.get("patternInstances")),
        "sourceObjectIds": _string_list(geometry.get("sourceObjectIds")),
        "bodyLocators": _body_locators(topology.get("bodyLocators") or geometry.get("bodyLocators")),
        "componentLocators": _component_locators(topology.get("componentLocators") or geometry.get("componentLocators")),
        "planeOffset": _rounded_number(geometry.get("planeOffset")),
        "scdmFaceLocators": _face_locators(topology.get("scdmFaceLocators")),
        "heightFaceLocators": _face_locators(
            topology.get("heightFaceLocators")
            or topology.get("topFaceLocators")
            or geometry.get("heightFaceLocators")
            or geometry.get("topFaceLocators")
        ),
        "depthFaceLocators": _face_locators(
            topology.get("depthFaceLocators")
            or topology.get("bottomFaceLocators")
            or geometry.get("depthFaceLocators")
            or geometry.get("bottomFaceLocators")
        ),
        "diameterFaceLocators": _face_locators(
            topology.get("diameterFaceLocators")
            or topology.get("sideFaceLocators")
            or geometry.get("diameterFaceLocators")
            or geometry.get("sideFaceLocators")
        ),
        "wallFaceLocators": _face_locators(
            topology.get("wallFaceLocators")
            or geometry.get("wallFaceLocators")
        ),
    }


def attach_local_face_ids_to_scdm_cache(
    cache: Mapping[str, object],
    local_face_signatures: Iterable[Mapping[str, object]],
    *,
    min_score: float = 5.0,
    unique_margin: float = 0.75,
) -> dict[str, object]:
    local_signatures = [dict(item) for item in local_face_signatures if isinstance(item, Mapping)]
    result = dict(cache)
    diagnostics = dict(result.get("diagnostics") if isinstance(result.get("diagnostics"), Mapping) else {})
    mapping_rows: list[dict[str, object]] = []
    objects = []
    for raw_object in result.get("objects", []) if isinstance(result.get("objects"), list) else []:
        if not isinstance(raw_object, Mapping):
            continue
        item = dict(raw_object)
        signature = dict(item.get("geometrySignature") if isinstance(item.get("geometrySignature"), Mapping) else {})
        if not _int_list(signature.get("faceIds")):
            ordinal_face_ids = _face_ids_from_signature_ordinals(signature, local_signatures)
            if ordinal_face_ids:
                signature["faceIds"] = ordinal_face_ids
                signature["localOrdinalMatch"] = True
                match = {
                    "status": "ordinal",
                    "message": "Matched local Face IDs from SCDM ordinal locators.",
                    "faceIds": ordinal_face_ids,
                    "score": None,
                }
            else:
                if str(signature.get("objectType") or "") in {"cylindrical_face_group", "cylindrical_hole"}:
                    match = _match_local_face_group_signatures(signature, local_signatures, min_score=min_score, unique_margin=unique_margin)
                else:
                    match = _match_local_face_signature(signature, local_signatures, min_score=min_score, unique_margin=unique_margin)
                status = str(match.get("status") or "")
                if status in {"unique", "group"}:
                    face_ids = _int_list(match.get("faceIds"))
                    face_id = _int_or_none(match.get("faceId"))
                    if not face_ids and face_id is not None:
                        face_ids = [face_id]
                    if face_ids:
                        signature["faceIds"] = face_ids
                        signature["localMatchScore"] = match.get("score")
                        signature["localUnitScale"] = match.get("unitScale")
            status = str(match.get("status") or "")
            mapping_rows.append(
                {
                    "objectId": item.get("objectId"),
                    "status": status,
                    "faceId": match.get("faceId"),
                    "faceIds": match.get("faceIds"),
                    "score": match.get("score"),
                    "message": match.get("message"),
                }
            )
        unit_scale = _inferred_local_unit_scale(signature, local_signatures)
        if unit_scale is not None:
            signature["localUnitScale"] = unit_scale
        support_face_ids = _pattern_support_face_ids(signature, local_signatures)
        if support_face_ids:
            signature["supportFaceIds"] = support_face_ids
            support_fit = _pattern_support_fit(signature, local_signatures, support_face_ids, unit_scale or 1.0)
            if support_fit:
                signature["supportPatternFit"] = support_fit
        _attach_pattern_instance_local_display_ids(signature, local_signatures)
        item["geometrySignature"] = signature
        objects.append(item)
    diagnostics["local_face_mapping"] = mapping_rows
    result["objects"] = objects
    result["diagnostics"] = diagnostics
    return result


def _face_ids_from_signature_ordinals(
    signature: Mapping[str, object],
    local_signatures: list[dict[str, object]],
) -> list[int]:
    global_lookup: dict[int, int] = {}
    body_lookup: dict[tuple[int, int], int] = {}
    for local in local_signatures:
        face_id = _int_or_none(local.get("faceId"))
        if face_id is None:
            continue
        global_ordinal = _int_or_none(local.get("globalFaceOrdinal"))
        if global_ordinal is not None:
            global_lookup[global_ordinal] = face_id
        body_index = _int_or_none(_first_present(local.get("bodyIndex"), local.get("solidId")))
        face_ordinal = _int_or_none(local.get("faceOrdinal"))
        if body_index is not None and face_ordinal is not None:
            body_lookup[(body_index, face_ordinal)] = face_id

    result: list[int] = []
    for value in _int_list(signature.get("globalFaceOrdinals") or [signature.get("globalFaceOrdinal")]):
        face_id = global_lookup.get(value)
        if face_id is not None:
            result.append(face_id)

    body_indices = _int_list(signature.get("bodyIndices"))
    if not body_indices:
        body_index = _int_or_none(signature.get("bodyIndex"))
        body_indices = [body_index] if body_index is not None else []
    for body_index in body_indices:
        for face_ordinal in _int_list(signature.get("faceOrdinals") or [signature.get("faceOrdinal")]):
            face_id = body_lookup.get((int(body_index), int(face_ordinal)))
            if face_id is not None:
                result.append(face_id)

    for locator in _face_locators(signature.get("scdmFaceLocators")):
        face_id = None
        global_ordinal = _int_or_none(locator.get("globalFaceOrdinal"))
        if global_ordinal is not None:
            face_id = global_lookup.get(global_ordinal)
        if face_id is None:
            body_index = _int_or_none(locator.get("bodyIndex"))
            face_ordinal = _int_or_none(locator.get("faceOrdinal"))
            if body_index is not None and face_ordinal is not None:
                face_id = body_lookup.get((body_index, face_ordinal))
        if face_id is not None:
            result.append(face_id)

    return sorted(set(result))


def _attach_pattern_instance_local_display_ids(
    signature: dict[str, object],
    local_signatures: list[dict[str, object]],
) -> None:
    instances = signature.get("patternInstances")
    if not isinstance(instances, list):
        return
    local_by_face_id = {
        int(face_id): local
        for local in local_signatures
        for face_id in [_int_or_none(local.get("faceId"))]
        if face_id is not None
    }
    updated_instances: list[object] = []
    for instance in instances:
        if not isinstance(instance, Mapping):
            updated_instances.append(instance)
            continue
        row = dict(instance)
        face_ids = _int_list(row.get("faceIds"))
        if not face_ids:
            face_ids = _face_ids_from_signature_ordinals(row, local_signatures)
            if face_ids:
                row["faceIds"] = face_ids
        solid_ids = sorted(
            set(
                int(value)
                for face_id in face_ids
                for value in [
                    _int_or_none(
                        _first_present(
                            local_by_face_id.get(int(face_id), {}).get("solidId"),
                            local_by_face_id.get(int(face_id), {}).get("bodyIndex"),
                        )
                    )
                ]
                if value is not None and int(value) >= 0
            )
        )
        part_ids = sorted(
            set(
                int(value)
                for face_id in face_ids
                for value in [_int_or_none(local_by_face_id.get(int(face_id), {}).get("partId"))]
                if value is not None and int(value) >= 0
            )
        )
        if solid_ids:
            row["localSolidIds"] = solid_ids
            if len(solid_ids) == 1:
                row["localSolidId"] = solid_ids[0]
        if part_ids:
            row["localPartIds"] = part_ids
            if len(part_ids) == 1:
                row["localPartId"] = part_ids[0]
        updated_instances.append(row)
    signature["patternInstances"] = updated_instances


def _inferred_local_unit_scale(
    signature: Mapping[str, object],
    local_signatures: list[dict[str, object]],
) -> float | None:
    existing = _number(signature.get("localUnitScale"))
    if existing is not None and existing > 0:
        return existing
    if str(signature.get("patternKind") or "").lower() == "body":
        scale = _body_pattern_unit_scale(signature, local_signatures)
        if scale is not None and scale > 0:
            return scale
    by_face_id = {
        int(face_id): local
        for local in local_signatures
        for face_id in [_int_or_none(local.get("faceId"))]
        if face_id is not None
    }
    scored: list[tuple[float, float]] = []
    for face_id in _int_list(signature.get("faceIds"))[:32]:
        local = by_face_id.get(int(face_id))
        if local is None:
            continue
        score, scale = _local_signature_score(signature, local)
        if score > 0 and scale > 0:
            scored.append((score, scale))
    if scored:
        scored.sort(key=lambda item: item[0], reverse=True)
        return scored[0][1]
    return None


def _body_pattern_unit_scale(
    signature: Mapping[str, object],
    local_signatures: list[dict[str, object]],
) -> float | None:
    if str(signature.get("patternKind") or "").lower() != "body":
        return None
    axis = _unit_vector(_rounded_vector(signature.get("axis")))
    if len(axis) != 3:
        return None
    scdm_spacing = _rounded_number(_first_present(signature.get("spacing"), signature.get("pitch")))
    if scdm_spacing is None or scdm_spacing <= 0:
        return None
    body_boxes = _local_body_boxes(local_signatures)
    body_indices = _int_list(signature.get("bodyIndices"))
    centers = [_box_center(body_boxes[index]) for index in body_indices if index in body_boxes]
    if len(centers) < 2:
        return None
    projections = sorted(sum(center[index] * axis[index] for index in range(3)) for center in centers)
    spacings = [
        projections[index + 1] - projections[index]
        for index in range(len(projections) - 1)
        if projections[index + 1] - projections[index] > 1.0e-9
    ]
    if not spacings:
        return None
    local_spacing = sum(spacings) / len(spacings)
    if local_spacing <= 0:
        return None
    return float(scdm_spacing) / float(local_spacing)


def _pattern_support_face_ids(
    signature: Mapping[str, object],
    local_signatures: list[dict[str, object]],
) -> list[int]:
    if str(signature.get("patternKind") or "").lower() != "body":
        return []
    body_indices = _int_list(signature.get("bodyIndices"))
    if len(body_indices) < 3:
        return []
    body_boxes = _local_body_boxes(local_signatures)
    member_boxes = [body_boxes[index] for index in body_indices if index in body_boxes]
    if len(member_boxes) < 3:
        return []
    pattern_box = _merge_boxes(member_boxes)
    pattern_size = [pattern_box[1][index] - pattern_box[0][index] for index in range(3)]
    diagonal = math.sqrt(sum(item * item for item in pattern_size))
    tolerance = max(diagonal * 1.0e-5, 1.0e-5)
    pattern_body_set = set(body_indices)
    candidates: list[tuple[float, int]] = []
    for local in local_signatures:
        face_id = _int_or_none(local.get("faceId"))
        body_index = _int_or_none(_first_present(local.get("bodyIndex"), local.get("solidId")))
        if face_id is None or body_index in pattern_body_set:
            continue
        if _surface_key(local.get("surfaceType")) != "plane":
            continue
        axis = _unit_vector(_rounded_vector(local.get("axis")))
        if len(axis) != 3:
            continue
        face_box = _box_from_local_signature(local)
        if face_box is None:
            continue
        plane = _rounded_number(local.get("planeOffset"))
        if plane is None:
            continue
        overlap = _pattern_projection_overlap_score(pattern_box, face_box, axis)
        if overlap <= 0:
            continue
        distance = _pattern_plane_touch_distance(pattern_box, axis, plane)
        if distance > tolerance:
            continue
        area = _number(local.get("area")) or _box_area_estimate(face_box)
        candidates.append((overlap * max(area, 1.0) - distance, int(face_id)))
    candidates.sort(reverse=True)
    return [face_id for _score, face_id in candidates[:4]]


def _pattern_support_fit(
    signature: Mapping[str, object],
    local_signatures: list[dict[str, object]],
    support_face_ids: list[int],
    unit_scale: float,
) -> dict[str, object]:
    if not support_face_ids:
        return {}
    axis = _unit_vector(_rounded_vector(signature.get("axis")))
    if len(axis) != 3:
        return {}
    body_boxes = _local_body_boxes(local_signatures)
    member_boxes = [body_boxes[index] for index in _int_list(signature.get("bodyIndices")) if index in body_boxes]
    if len(member_boxes) < 3:
        return {}
    member_centers = [_box_center(box) for box in member_boxes]
    center_projections = sorted(_point_projection(center, axis) for center in member_centers)
    if len(center_projections) < 3:
        return {}
    member_span = max(_box_projection_span(box, axis) for box in member_boxes)
    if member_span <= 0:
        return {}
    local_by_face_id = {
        int(face_id): local
        for local in local_signatures
        for face_id in [_int_or_none(local.get("faceId"))]
        if face_id is not None
    }
    support_candidates = []
    for face_id in support_face_ids:
        local = local_by_face_id.get(int(face_id))
        if local is None:
            continue
        box = _box_from_local_signature(local)
        if box is None:
            continue
        projection_min, projection_max = _box_projection_range(box, axis)
        pattern_center_projection = sum(center_projections) / len(center_projections)
        half_member_span = member_span * 0.5
        left_capacity = pattern_center_projection - projection_min - half_member_span
        right_capacity = projection_max - pattern_center_projection - half_member_span
        max_spacing_local = (2.0 * min(left_capacity, right_capacity)) / (len(center_projections) - 1)
        if max_spacing_local <= 0:
            continue
        support_candidates.append(
            {
                "faceId": int(face_id),
                "supportSpanLocal": projection_max - projection_min,
                "supportProjectionMinLocal": projection_min,
                "supportProjectionMaxLocal": projection_max,
                "memberSpanLocal": member_span,
                "patternCenterProjectionLocal": pattern_center_projection,
                "leftCapacityLocal": left_capacity,
                "rightCapacityLocal": right_capacity,
                "maxSpacingLocal": max_spacing_local,
            }
        )
    if not support_candidates:
        return {}
    support_candidates.sort(key=lambda item: float(item["maxSpacingLocal"]), reverse=True)
    selected = support_candidates[0]
    safe_unit_scale = unit_scale if unit_scale > 0 else 1.0
    max_spacing_local = float(selected["maxSpacingLocal"])
    current_spacing = _rounded_number(_first_present(signature.get("spacing"), signature.get("pitch")))
    selected.update(
        {
            "supportFaceIds": support_face_ids,
            "instanceCount": len(center_projections),
            "axis": axis,
            "localUnitScale": safe_unit_scale,
            "maxSpacing": max_spacing_local * safe_unit_scale,
            "currentSpacing": current_spacing,
            "currentSpacingLocal": (current_spacing / safe_unit_scale) if current_spacing is not None and safe_unit_scale > 0 else None,
        }
    )
    return selected


def _local_body_boxes(local_signatures: list[dict[str, object]]) -> dict[int, tuple[list[float], list[float]]]:
    boxes: dict[int, tuple[list[float], list[float]]] = {}
    for local in local_signatures:
        body_index = _int_or_none(_first_present(local.get("bodyIndex"), local.get("solidId")))
        box = _box_from_local_signature(local)
        if body_index is None or box is None:
            continue
        if body_index not in boxes:
            boxes[body_index] = ([*box[0]], [*box[1]])
            continue
        existing = boxes[body_index]
        boxes[body_index] = (
            [min(existing[0][index], box[0][index]) for index in range(3)],
            [max(existing[1][index], box[1][index]) for index in range(3)],
        )
    return boxes


def _box_from_local_signature(local: Mapping[str, object]) -> tuple[list[float], list[float]] | None:
    bbox_min = _rounded_vector(local.get("bboxMin"))
    bbox_max = _rounded_vector(local.get("bboxMax"))
    if len(bbox_min) != 3 or len(bbox_max) != 3:
        return None
    return bbox_min, bbox_max


def _merge_boxes(boxes: list[tuple[list[float], list[float]]]) -> tuple[list[float], list[float]]:
    return (
        [min(box[0][index] for box in boxes) for index in range(3)],
        [max(box[1][index] for box in boxes) for index in range(3)],
    )


def _box_center(box: tuple[list[float], list[float]]) -> list[float]:
    return [(box[0][index] + box[1][index]) * 0.5 for index in range(3)]


def _point_projection(point: list[float], axis: list[float]) -> float:
    return sum(float(point[index]) * float(axis[index]) for index in range(3))


def _box_projection_range(box: tuple[list[float], list[float]], axis: list[float]) -> tuple[float, float]:
    values = []
    for x in (box[0][0], box[1][0]):
        for y in (box[0][1], box[1][1]):
            for z in (box[0][2], box[1][2]):
                values.append(_point_projection([x, y, z], axis))
    return (min(values), max(values)) if values else (0.0, 0.0)


def _box_projection_span(box: tuple[list[float], list[float]], axis: list[float]) -> float:
    left, right = _box_projection_range(box, axis)
    return max(0.0, right - left)


def _pattern_projection_overlap_score(
    pattern_box: tuple[list[float], list[float]],
    face_box: tuple[list[float], list[float]],
    normal: list[float],
) -> float:
    normal_axis = max(range(3), key=lambda index: abs(normal[index]))
    overlaps = []
    for axis_index in range(3):
        if axis_index == normal_axis:
            continue
        overlap = min(pattern_box[1][axis_index], face_box[1][axis_index]) - max(pattern_box[0][axis_index], face_box[0][axis_index])
        reference = max(pattern_box[1][axis_index] - pattern_box[0][axis_index], 1.0e-9)
        overlaps.append(max(0.0, overlap) / reference)
    if not overlaps or any(value <= 0 for value in overlaps):
        return 0.0
    return sum(overlaps) / len(overlaps)


def _pattern_plane_touch_distance(
    pattern_box: tuple[list[float], list[float]],
    normal: list[float],
    plane_offset: float,
) -> float:
    distances = []
    for x in (pattern_box[0][0], pattern_box[1][0]):
        for y in (pattern_box[0][1], pattern_box[1][1]):
            for z in (pattern_box[0][2], pattern_box[1][2]):
                distances.append(abs(x * normal[0] + y * normal[1] + z * normal[2] - plane_offset))
    return min(distances) if distances else float("inf")


def _box_area_estimate(box: tuple[list[float], list[float]]) -> float:
    sizes = sorted(max(0.0, box[1][index] - box[0][index]) for index in range(3))
    return sizes[1] * sizes[2]


def _match_local_face_signature(
    scdm_signature: Mapping[str, object],
    local_signatures: list[dict[str, object]],
    *,
    min_score: float,
    unique_margin: float,
) -> dict[str, object]:
    scored = []
    for local in local_signatures:
        score, scale = _local_signature_score(scdm_signature, local)
        if score <= 0:
            continue
        scored.append({"faceId": local.get("faceId"), "score": score, "unitScale": scale})
    scored.sort(key=lambda item: float(item.get("score") or 0.0), reverse=True)
    if not scored or float(scored[0].get("score") or 0.0) < min_score:
        return {"status": "none", "message": "No local Face signature matched SCDM object.", "candidates": scored[:5]}
    if len(scored) > 1:
        top = float(scored[0].get("score") or 0.0)
        second = float(scored[1].get("score") or 0.0)
        if top - second < unique_margin:
            return {"status": "multiple", "message": "Multiple local Faces match the same SCDM object.", "candidates": scored[:5]}
    return {"status": "unique", "message": "Matched one local Face.", **scored[0]}


def _match_local_face_group_signatures(
    scdm_signature: Mapping[str, object],
    local_signatures: list[dict[str, object]],
    *,
    min_score: float,
    unique_margin: float,
) -> dict[str, object]:
    scored = []
    for local in local_signatures:
        score, scale = _local_signature_score(scdm_signature, local)
        if score < min_score:
            continue
        scored.append({"faceId": local.get("faceId"), "score": score, "unitScale": scale})
    scored.sort(key=lambda item: float(item.get("score") or 0.0), reverse=True)
    if not scored:
        return {"status": "none", "message": "No local cylindrical Face matched SCDM group.", "candidates": []}
    top = float(scored[0].get("score") or 0.0)
    selected = [item for item in scored if top - float(item.get("score") or 0.0) <= unique_margin]
    face_ids = sorted(set(_int_or_none(item.get("faceId")) for item in selected if _int_or_none(item.get("faceId")) is not None))
    if not face_ids:
        return {"status": "none", "message": "Local cylindrical group matched no usable Face IDs.", "candidates": scored[:5]}
    return {
        "status": "group",
        "message": "Matched a local cylindrical Face group.",
        "faceIds": face_ids,
        "score": top,
        "unitScale": selected[0].get("unitScale"),
        "candidates": scored[:8],
    }


def _local_signature_score(scdm_signature: Mapping[str, object], local: Mapping[str, object]) -> tuple[float, float]:
    scdm_surface = _surface_key(scdm_signature.get("surfaceType"))
    local_surface = _surface_key(local.get("surfaceType"))
    if scdm_surface and local_surface and scdm_surface != local_surface:
        return 0.0, 1.0
    scales = _unit_scale_candidates(scdm_signature, local)
    best_score = 0.0
    best_scale = 1.0
    for scale in scales:
        score = 2.0 if scdm_surface and scdm_surface == local_surface else 0.0
        score += _axis_match_score(_rounded_vector(scdm_signature.get("axis")), _rounded_vector(local.get("axis")))
        if scdm_surface == "plane":
            score += _scaled_number_match_score(
                _rounded_number(local.get("planeOffset")),
                _rounded_number(scdm_signature.get("planeOffset")),
                scale,
            )
        elif scdm_surface == "cylinder":
            score += _scaled_number_match_score(
                _rounded_number(local.get("radius")),
                _rounded_number(scdm_signature.get("radius")),
                scale,
            )
            score += _axis_point_distance_score(
                _rounded_vector(local.get("center")),
                _rounded_vector(local.get("axis")),
                _rounded_vector(scdm_signature.get("center")),
                scale,
            )
        else:
            score += _scaled_vector_match_score(
                _rounded_vector(local.get("center")),
                _rounded_vector(scdm_signature.get("center")),
                scale,
            )
        if score > best_score:
            best_score = score
            best_scale = scale
    return best_score, best_scale


def _unit_scale_candidates(scdm_signature: Mapping[str, object], local: Mapping[str, object]) -> tuple[float, ...]:
    values = [1.0, 0.001, 1000.0]
    local_radius = _rounded_number(local.get("radius"))
    scdm_radius = _rounded_number(scdm_signature.get("radius"))
    if local_radius and scdm_radius:
        values.append(float(scdm_radius) / float(local_radius))
    local_offset = _rounded_number(local.get("planeOffset"))
    scdm_offset = _rounded_number(scdm_signature.get("planeOffset"))
    if local_offset not in {None, 0.0} and scdm_offset is not None:
        values.append(float(scdm_offset) / float(local_offset))
    result = []
    for value in values:
        if value > 0 and all(abs(value - existing) > max(value, existing) * 1e-9 for existing in result):
            result.append(value)
    return tuple(result)


def _capability_payload(
    key: str,
    raw_object: Mapping[str, object],
    *,
    available_command_names: set[str] | None = None,
) -> dict[str, object] | None:
    definition = capability_definition(key)
    if definition is None:
        return None
    block_reason = _first_non_empty(
        _missing_backend_command_reason(key, available_command_names),
        _missing_geometry_reason(key, raw_object),
    )
    return {
        "key": definition.key,
        "displayName": definition.display_name,
        "valueKind": definition.value_kind,
        "currentValue": _current_value(definition.current_fields, raw_object),
        "defaultIntent": definition.default_intent,
        "backendOperation": definition.backend_operation,
        "postCheck": definition.post_check,
        "editable": True,
        "blockReason": block_reason,
    }


def _current_value(fields: tuple[str, ...], raw_object: Mapping[str, object]) -> object:
    geometry = _mapping(raw_object.get("geometry"))
    for field in fields:
        if field == "0":
            return 0.0
        if field == "1":
            return 1
        if field == "geometry.radius*2":
            radius = _number(geometry.get("radius"))
            if radius is not None:
                return radius * 2.0
            continue
        if field.startswith("geometry."):
            value = _path_value(geometry, field.split(".", 1)[1])
            if value is not None:
                return value
    return None


def _missing_geometry_reason(key: str, raw_object: Mapping[str, object]) -> str:
    if key not in {"slot.depth", "boss.height", "boss.diameter", "round.radius", "chamfer.distance", "pattern.spacing"}:
        return ""
    signature = geometry_signature(raw_object)
    if key == "pattern.spacing":
        spacing = _rounded_number(_first_present(signature.get("spacing"), signature.get("pitch")))
        if spacing is None or spacing <= 0:
            return "SCDM 已识别阵列对象，但没有返回可用于编辑的当前阵列间距。"
        if (
            str(signature.get("patternKind") or "").strip().lower() == "body"
            or str(signature.get("instanceKind") or "").strip().lower() in {"body", "part", "component"}
            or _int_list(signature.get("bodyIndices"))
        ):
            axis = _rounded_vector(signature.get("axis"))
            if len(axis) != 3:
                return "SCDM 已识别实体/组件阵列间距，但没有返回线性阵列方向，暂不能稳定改间距。"
            instances = _pattern_instances(signature.get("patternInstances"))
            if len(instances) < 3:
                return "SCDM 已识别实体/组件阵列间距，但没有返回阵列成员实例，暂不能稳定改间距。"
            if any(not _component_locators(item.get("componentLocators") or item.get("bodyLocators")) for item in instances):
                return "SCDM 已识别实体/组件阵列间距，但没有返回每个成员的组件实例定位，暂不能稳定改间距。"
            return ""
        axis = _rounded_vector(signature.get("axis"))
        if len(axis) != 3:
            return "SCDM 已识别阵列间距，但没有返回线性阵列方向，暂不能稳定改间距。"
        instances = _pattern_instances(signature.get("patternInstances"))
        if len(instances) < 3:
            return "SCDM 已识别阵列间距，但没有返回至少三个可定位实例，暂不能稳定改间距。"
        if any(not _pattern_instance_has_locator(item) for item in instances):
            return "SCDM 已识别阵列间距，但没有返回每个阵列实例的可定位 Face，暂不能稳定改间距。"
        return ""
    if key == "slot.depth":
        depth = _rounded_number(signature.get("depth"))
        if depth is None or depth <= 0:
            return "SCDM 已识别槽对象，但没有返回可用于编辑的当前槽深。"
        if not _slot_depth_axis(signature):
            return "SCDM 已识别槽深，但没有返回槽深方向，暂不能稳定改槽深。"
        if not _has_depth_face_locator(signature):
            return "SCDM 已识别槽深，但没有返回可推动的槽底面定位信息，暂不能稳定改槽深。"
        return ""
    if key == "round.radius":
        if not _has_scdm_face_locator(signature):
            return "SCDM 已识别圆角半径，但没有返回可定位的圆角面，暂不能稳定改半径。"
        if signature.get("isConstantRound") is not True:
            return "SCDM 已识别圆角半径，但没有返回等半径圆角证据，暂不能稳定改半径。"
        return ""
    if key == "chamfer.distance":
        if not _has_scdm_face_locator(signature):
            return "SCDM 已识别倒角距离，但没有返回可定位的倒角面，暂不能稳定改距离。"
        distance = _rounded_number(signature.get("distance"))
        if distance is None or distance <= 0:
            return "SCDM 已识别倒角对象，但没有返回可用于编辑的当前倒角距离。"
        if signature.get("isEqualDistanceChamfer") is not True:
            return "SCDM 已识别倒角距离，但没有返回等距倒角证据，暂不能稳定改距离。"
        return ""
    if key == "boss.height":
        if (
            _face_locators(signature.get("heightFaceLocators"))
            or _int_list(signature.get("heightFaceOrdinals"))
            or _int_list(signature.get("globalHeightFaceOrdinals"))
        ):
            return ""
        return "SCDM 已识别凸台高度，但没有返回可推动的顶面定位信息，暂不能稳定改高度。"
    if (
        _face_locators(signature.get("diameterFaceLocators"))
        or _int_list(signature.get("diameterFaceOrdinals"))
        or _int_list(signature.get("globalDiameterFaceOrdinals"))
    ):
        return ""
    return "SCDM 已识别凸台直径，但没有返回可偏移的侧壁定位信息，暂不能稳定改直径。"


def _has_scdm_face_locator(signature: Mapping[str, object]) -> bool:
    if _face_locators(signature.get("scdmFaceLocators")):
        return True
    if _int_list(signature.get("faceOrdinals")) or _int_list(signature.get("globalFaceOrdinals")):
        return True
    if _int_or_none(signature.get("faceOrdinal")) is not None:
        return True
    return _int_or_none(signature.get("globalFaceOrdinal")) is not None


def _has_depth_face_locator(signature: Mapping[str, object]) -> bool:
    if _face_locators(signature.get("depthFaceLocators")):
        return True
    if _int_list(signature.get("depthFaceOrdinals")) or _int_list(signature.get("globalDepthFaceOrdinals")):
        return True
    return False


def _slot_depth_axis(signature: Mapping[str, object]) -> list[float]:
    return _rounded_vector(signature.get("depthAxis"))


def _pattern_instance_has_locator(instance: Mapping[str, object]) -> bool:
    if _component_locators(instance.get("componentLocators") or instance.get("bodyLocators")):
        return True
    if _body_locators(instance.get("bodyLocators")):
        return True
    if _int_or_none(instance.get("bodyIndex")) is not None and str(instance.get("instanceKind") or "").lower() in {"body", "part", "component"}:
        return True
    if _face_locators(instance.get("scdmFaceLocators")):
        return True
    if _int_list(instance.get("faceOrdinals")) or _int_list(instance.get("globalFaceOrdinals")):
        return True
    return False


def _first_non_empty(*values: str) -> str:
    for value in values:
        text = str(value or "").strip()
        if text:
            return text
    return ""


def _available_command_names(value: object) -> set[str] | None:
    if not isinstance(value, list):
        return None
    result: set[str] = set()
    for item in value:
        if not isinstance(item, Mapping):
            continue
        if item.get("available") is not True:
            continue
        name = str(item.get("name") or "").strip()
        if name:
            result.add(name)
    return result


def _missing_backend_command_reason(key: str, available_command_names: set[str] | None) -> str:
    if available_command_names is None:
        return ""
    definition = capability_definition(key)
    groups = definition.required_backend_command_groups if definition is not None else ()
    if not groups:
        return ""
    if any(all(command in available_command_names for command in group) for group in groups):
        return ""
    readable = " / ".join(" + ".join(group) for group in groups)
    return f"SCDM 当前脚本环境缺少 {readable} 命令，暂不能执行该参数。"


def _object_id(raw_object: Mapping[str, object]) -> str:
    existing = str(raw_object.get("objectId") or "").strip()
    if existing:
        return existing
    object_type = str(raw_object.get("objectType") or "object").strip() or "object"
    backend_id = str(raw_object.get("backendId") or "").strip()
    if backend_id:
        return f"{object_type}:{backend_id}"
    signature = geometry_signature(raw_object)
    face_ids = signature.get("faceIds")
    if isinstance(face_ids, list) and face_ids:
        return f"{object_type}:face:{'-'.join(str(item) for item in face_ids)}"
    return object_type


def _locator_from_raw_object(raw_object: Mapping[str, object]) -> dict[str, object]:
    geometry = _mapping(raw_object.get("geometry"))
    topology = _mapping(raw_object.get("topologyHint"))
    locator = {
        "backendId": str(raw_object.get("backendId") or ""),
        "bodyIndex": _int_or_none(_first_present(topology.get("bodyIndex"), geometry.get("bodyIndex"))),
        "faceOrdinal": _int_or_none(_first_present(topology.get("faceOrdinal"), geometry.get("faceOrdinal"))),
        "globalFaceOrdinal": _int_or_none(_first_present(topology.get("globalFaceOrdinal"), geometry.get("globalFaceOrdinal"))),
    }
    component_locators = _component_locators(topology.get("componentLocators") or geometry.get("componentLocators"))
    if component_locators:
        locator["componentLocators"] = component_locators
    return locator


def _face_locators(value: object) -> list[dict[str, object]]:
    if not isinstance(value, (list, tuple)):
        return []
    result: list[dict[str, object]] = []
    for item in value:
        if not isinstance(item, Mapping):
            continue
        locator = {
            "backendId": str(item.get("backendId") or ""),
            "bodyIndex": _int_or_none(item.get("bodyIndex")),
            "faceOrdinal": _int_or_none(item.get("faceOrdinal")),
            "globalFaceOrdinal": _int_or_none(item.get("globalFaceOrdinal")),
        }
        if any(locator.get(key) is not None for key in ("bodyIndex", "faceOrdinal", "globalFaceOrdinal")):
            result.append(locator)
    return result


def _body_locators(value: object) -> list[dict[str, object]]:
    if not isinstance(value, (list, tuple)):
        return []
    result: list[dict[str, object]] = []
    seen: set[tuple[object, ...]] = set()
    for item in value:
        if not isinstance(item, Mapping):
            continue
        locator = {
            "backendId": str(item.get("backendId") or ""),
            "bodyIndex": _int_or_none(item.get("bodyIndex")),
            "componentIndex": _int_or_none(item.get("componentIndex")),
            "componentPath": _ordered_int_list(item.get("componentPath")),
            "componentBodyIndex": _int_or_none(item.get("componentBodyIndex")),
            "componentName": str(item.get("componentName") or ""),
        }
        if locator.get("bodyIndex") is not None or locator.get("componentIndex") is not None or locator.get("componentPath"):
            key = (
                locator.get("bodyIndex"),
                locator.get("componentIndex"),
                tuple(locator.get("componentPath") or []),
                locator.get("componentBodyIndex"),
            )
            if key in seen:
                continue
            seen.add(key)
            result.append(locator)
    return result


def _component_locators(value: object) -> list[dict[str, object]]:
    if not isinstance(value, (list, tuple)):
        return []
    result: list[dict[str, object]] = []
    seen: set[tuple[object, ...]] = set()
    for item in value:
        if not isinstance(item, Mapping):
            continue
        path = _ordered_int_list(item.get("componentPath"))
        locator = {
            "backendId": str(item.get("backendId") or ""),
            "componentIndex": _int_or_none(item.get("componentIndex")),
            "componentPath": path,
            "componentBodyIndex": _int_or_none(item.get("componentBodyIndex")),
            "bodyIndex": _int_or_none(item.get("bodyIndex")),
            "componentName": str(item.get("componentName") or ""),
            "contentMoniker": str(item.get("contentMoniker") or ""),
            "templateMoniker": str(item.get("templateMoniker") or ""),
            "placementTranslation": _rounded_vector(item.get("placementTranslation")),
        }
        if locator.get("componentIndex") is None and not path:
            continue
        key = (
            locator.get("componentIndex"),
            tuple(path),
            locator.get("componentBodyIndex"),
            locator.get("bodyIndex"),
            locator.get("contentMoniker"),
            locator.get("templateMoniker"),
        )
        if key in seen:
            continue
        seen.add(key)
        result.append(locator)
    return result


def _first_int(values: Iterable[object]) -> int | None:
    for value in values:
        number = _int_or_none(value)
        if number is not None:
            return number
    return None


def _merge_ints(values: Iterable[object]) -> list[int]:
    result: list[int] = []
    for value in values:
        result.extend(_int_list(value))
    return sorted(set(result))


def _mapping(value: object) -> Mapping[str, object]:
    return value if isinstance(value, Mapping) else {}


def _path_value(mapping: Mapping[str, object], path: str) -> object:
    current: object = mapping
    for part in path.split("."):
        if not isinstance(current, Mapping):
            return None
        current = current.get(part)
        if current is None:
            return None
    return current


def _int_list(value: object) -> list[int]:
    if isinstance(value, (str, bytes)) or value is None:
        return []
    try:
        values = list(value)  # type: ignore[arg-type]
    except TypeError:
        return []
    result: list[int] = []
    for item in values:
        try:
            result.append(int(item))
        except (TypeError, ValueError):
            continue
    return sorted(set(result))


def _ordered_int_list(value: object) -> list[int]:
    if isinstance(value, (str, bytes)) or value is None:
        return []
    try:
        values = list(value)  # type: ignore[arg-type]
    except TypeError:
        return []
    result: list[int] = []
    for item in values:
        try:
            result.append(int(item))
        except (TypeError, ValueError):
            continue
    return result


def _int_or_none(value: object) -> int | None:
    try:
        return int(value)
    except (TypeError, ValueError):
        return None


def _bool_or_none(value: object) -> bool | None:
    if isinstance(value, bool):
        return value
    if isinstance(value, str):
        text = value.strip().lower()
        if text in {"true", "1", "yes", "y"}:
            return True
        if text in {"false", "0", "no", "n"}:
            return False
    if isinstance(value, (int, float)):
        return bool(value)
    return None


def _first_present(*values: object) -> object:
    for value in values:
        if value is not None and value != "":
            return value
    return None


def _surface_key(value: object) -> str:
    text = str(value or "").strip().lower()
    if "plane" in text:
        return "plane"
    if "cylinder" in text:
        return "cylinder"
    return text


def _canonical_axis(axis: list[float]) -> list[float]:
    if len(axis) != 3:
        return []
    length = math.sqrt(sum(item * item for item in axis))
    if length <= 1e-12:
        return []
    normalized = [item / length for item in axis]
    for value in normalized:
        if abs(value) > 1e-12:
            if value < 0:
                normalized = [-item for item in normalized]
            break
    return [round(item, 6) for item in normalized]


def _axis_match_score(left: list[float], right: list[float]) -> float:
    if len(left) != 3 or len(right) != 3:
        return 0.0
    left_len = math.sqrt(sum(item * item for item in left))
    right_len = math.sqrt(sum(item * item for item in right))
    if left_len <= 1e-12 or right_len <= 1e-12:
        return 0.0
    dot = abs(sum(left[index] * right[index] for index in range(3)) / (left_len * right_len))
    if dot >= 0.999:
        return 3.0
    if dot >= 0.99:
        return 2.0
    return 0.0


def _scaled_number_match_score(local_value: float | None, scdm_value: float | None, scale: float) -> float:
    if local_value is None or scdm_value is None:
        return 0.0
    error = abs(float(local_value) * scale - float(scdm_value))
    reference = max(abs(float(scdm_value)), abs(float(local_value) * scale), 1.0e-9)
    relative = error / reference
    if relative <= 1.0e-5:
        return 4.0
    if relative <= 1.0e-3:
        return 3.0
    if relative <= 1.0e-2:
        return 1.0
    return 0.0


def _scaled_vector_match_score(local_value: list[float], scdm_value: list[float], scale: float) -> float:
    if len(local_value) != 3 or len(scdm_value) != 3:
        return 0.0
    distance = math.sqrt(sum((local_value[index] * scale - scdm_value[index]) ** 2 for index in range(3)))
    reference = max(max(abs(item) for item in scdm_value), 1.0e-9)
    relative = distance / reference
    if relative <= 1.0e-5:
        return 4.0
    if relative <= 1.0e-3:
        return 3.0
    if relative <= 1.0e-2:
        return 1.0
    return 0.0


def _axis_point_distance_score(
    local_point: list[float],
    local_axis: list[float],
    scdm_point: list[float],
    scale: float,
) -> float:
    if len(local_point) != 3 or len(local_axis) != 3 or len(scdm_point) != 3:
        return 0.0
    point = [local_point[index] * scale for index in range(3)]
    axis_len = math.sqrt(sum(item * item for item in local_axis))
    if axis_len <= 1e-12:
        return 0.0
    axis = [item / axis_len for item in local_axis]
    delta = [scdm_point[index] - point[index] for index in range(3)]
    projection = sum(delta[index] * axis[index] for index in range(3))
    nearest = [point[index] + projection * axis[index] for index in range(3)]
    distance = math.sqrt(sum((nearest[index] - scdm_point[index]) ** 2 for index in range(3)))
    reference = max(max(abs(item) for item in scdm_point), 1.0e-9)
    relative = distance / reference
    if relative <= 1.0e-5:
        return 3.0
    if relative <= 1.0e-3:
        return 2.0
    if relative <= 1.0e-2:
        return 0.75
    return 0.0


def _rounded_vector(value: object) -> list[float]:
    if isinstance(value, (str, bytes)) or value is None:
        return []
    try:
        values = list(value)  # type: ignore[arg-type]
    except TypeError:
        return []
    result: list[float] = []
    for item in values[:3]:
        number = _number(item)
        if number is None:
            return []
        result.append(round(number, 6))
    return result if len(result) == 3 else []


def _rounded_vector_list(value: object) -> list[list[float]]:
    if isinstance(value, (str, bytes)) or value is None:
        return []
    try:
        values = list(value)  # type: ignore[arg-type]
    except TypeError:
        return []
    result: list[list[float]] = []
    for item in values:
        rounded = _rounded_vector(item)
        if len(rounded) == 3:
            result.append(rounded)
    return result


def _pattern_instances(value: object) -> list[dict[str, object]]:
    if isinstance(value, (str, bytes)) or value is None:
        return []
    try:
        values = list(value)  # type: ignore[arg-type]
    except TypeError:
        return []
    result: list[dict[str, object]] = []
    for item in values:
        if not isinstance(item, Mapping):
            continue
        center = _rounded_vector(item.get("center") or item.get("instanceCenter"))
        if len(center) != 3:
            continue
        result.append(
            {
                "sourceObjectId": str(item.get("sourceObjectId") or item.get("objectId") or ""),
                "instanceKind": str(item.get("instanceKind") or ""),
                "center": center,
                "bodyIndex": _int_or_none(item.get("bodyIndex")),
                "bodyLocators": _body_locators(item.get("bodyLocators")),
                "componentLocators": _component_locators(item.get("componentLocators") or item.get("bodyLocators")),
                "faceIds": _int_list(item.get("faceIds")),
                "faceOrdinals": _int_list(item.get("faceOrdinals") or [item.get("faceOrdinal")]),
                "globalFaceOrdinals": _int_list(
                    item.get("globalFaceOrdinals")
                    or [item.get("globalFaceOrdinal")]
                ),
                "scdmFaceLocators": _face_locators(item.get("scdmFaceLocators") or item.get("faceLocators")),
            }
        )
    return result


def _string_list(value: object) -> list[str]:
    if isinstance(value, (str, bytes)) or value is None:
        return []
    try:
        values = list(value)  # type: ignore[arg-type]
    except TypeError:
        return []
    result: list[str] = []
    for item in values:
        text = str(item or "").strip()
        if text:
            result.append(text)
    return result


def _rounded_number(value: object) -> float | None:
    number = _number(value)
    return None if number is None else round(number, 6)


def _number(value: object) -> float | None:
    try:
        return float(value)
    except (TypeError, ValueError):
        return None


__all__ = [
    "attach_local_face_ids_to_scdm_cache",
    "geometry_signature",
    "map_scdm_raw_features",
    "map_scdm_raw_features_file",
    "SCDM_FEATURE_CACHE_REVISION",
]