239 lines
8.2 KiB
Python
239 lines
8.2 KiB
Python
from __future__ import annotations
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import math
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from typing import Mapping
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USER_OPERATION_PRIORITY: dict[str, int] = {
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"push_pull_plane": 10,
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"resize_cylinder": 20,
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"resize_depth": 24,
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"suppress_cylinder": 28,
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"resize_slot_width": 30,
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"resize_slot_depth": 31,
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"resize_slot_arc_length": 34,
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"resize_slot_angular_span": 35,
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"resize_boss": 40,
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"resize_boss_height": 41,
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"move_boss_axis": 44,
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"inspect_existing_fillet": 50,
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"inspect_existing_chamfer": 51,
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"fillet_edge": 52,
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"chamfer_edge": 53,
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"resize_shell_thickness": 60,
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"resize_edge_length": 70,
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"resize_ellipse_edge_major_radius": 72,
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"resize_ellipse_edge_minor_radius": 73,
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}
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USER_PRIORITY_BUCKETS: tuple[tuple[int, str, str], ...] = (
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(10, "Face 面编辑", "最常用：平面拉伸/切除、面尺寸、中心和偏移。"),
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(20, "孔", "常用：孔径、孔深、孔轴心和封堵。"),
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(30, "槽/半孔", "常用：槽宽、槽深、弧长、弧角和槽轴心。"),
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(40, "凸台/外圆", "常用：凸台直径、高度和轴心。"),
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(50, "圆角/倒角", "常用但风险更高：已有圆角半径、新增圆角或倒角。"),
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(60, "壳体厚度", "专项：相对面可识别时修改局部厚度。"),
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(70, "Edge 边编辑", "受限：边长、端点、圆边半径等一级关系编辑。"),
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(80, "解析曲面", "较少直接修改：圆锥、球面、环面等解析曲面参数。"),
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(90, "只读/诊断", "暂未稳定归类为可修改特征。"),
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)
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EXTERNAL_RELATION_SCORE_WEIGHTS: dict[str, int] = {
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"coaxial": 8,
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"tangent": 5,
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"coplanar": 4,
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"parallel": 3,
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"perpendicular": 3,
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"parallel_axis": 3,
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}
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EXTERNAL_RELATION_SCORE_LIMIT = 18
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EXTERNAL_FEATURE_HINT_SCORE_LIMIT = 12
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def _text(value: object) -> str:
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return str(value or "").strip()
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def _float_or_none(value: object) -> float | None:
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try:
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return float(value)
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except (TypeError, ValueError):
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return None
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def _int_or_zero(value: object) -> int:
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try:
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return int(value)
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except (TypeError, ValueError):
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return 0
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def _text_values(value: object) -> tuple[str, ...]:
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if value is None or value == "":
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return ()
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if isinstance(value, str):
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return (value.strip(),) if value.strip() else ()
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if isinstance(value, (list, tuple, set)):
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return tuple(str(item).strip() for item in value if str(item).strip())
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return ()
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def _relation_types_from_summary(value: object) -> tuple[str, ...]:
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text = _text(value)
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if not text:
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return ()
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result: list[str] = []
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for chunk in text.replace(";", ",").split(","):
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relation_type = chunk.split(":", 1)[0].strip()
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if relation_type:
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result.append(relation_type)
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return tuple(result)
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def external_relation_score_bonus(info: Mapping[str, object]) -> int:
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relation_types = _text_values(info.get("external_recognition_relation_types")) or _text_values(
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info.get("asitus_geometric_relation_types")
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)
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if not relation_types:
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relation_types = _relation_types_from_summary(
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info.get("external_recognition_relation_summary")
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or info.get("asitus_geometric_relation_summary")
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)
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relation_count = _int_or_zero(
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info.get("external_recognition_relation_count")
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or info.get("asitus_geometric_relation_count")
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)
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score = 0
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for relation_type in relation_types:
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score += EXTERNAL_RELATION_SCORE_WEIGHTS.get(relation_type, 1)
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if relation_count and not relation_types:
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score = min(relation_count * 2, EXTERNAL_RELATION_SCORE_LIMIT)
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score = max(0, min(score, EXTERNAL_RELATION_SCORE_LIMIT))
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hint_score = min(_int_or_zero(info.get("analysis_situs_feature_hint_score")), EXTERNAL_FEATURE_HINT_SCORE_LIMIT)
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return max(0, min(score + hint_score, EXTERNAL_RELATION_SCORE_LIMIT + EXTERNAL_FEATURE_HINT_SCORE_LIMIT))
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def _confidence_sort_rank(value: object) -> int:
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return {
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"high": 0,
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"medium": 1,
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"low": 2,
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"pending": 3,
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"unchecked": 3,
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"none": 4,
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}.get(_text(value), 5)
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def _is_effectively_full_cylinder(info: Mapping[str, object]) -> bool:
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if bool(info.get("is_full_cylinder")):
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return True
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for key in ("same_domain_angular_span", "angular_span"):
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angular_span = _float_or_none(info.get(key))
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if angular_span is not None and angular_span >= math.tau * 0.92:
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return True
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return False
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def feature_recognition_priority(info: Mapping[str, object]) -> int:
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"""Rank feature candidates by likely user editing frequency.
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The rank is intentionally product-facing, not a geometry confidence score:
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lower numbers should appear earlier in editable-feature lists.
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"""
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operation_key = _text(info.get("operation_key"))
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if operation_key in USER_OPERATION_PRIORITY:
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return USER_OPERATION_PRIORITY[operation_key]
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surface = _text(info.get("surface"))
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feature_guess = _text(info.get("feature_guess"))
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feature_type = _text(info.get("feature_type"))
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feature_actions = _text(info.get("feature_edit_actions"))
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ready_actions = _text(info.get("recognition_ready_actions"))
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combined_text = "；".join(item for item in (feature_type, feature_actions, ready_actions) if item)
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if _text(info.get("existing_chamfer_status")) == "candidate" or feature_guess == "chamfer candidate":
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return 50
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if surface == "plane":
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return 10
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if surface == "cylinder":
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if feature_guess == "hole/groove candidate":
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angular_span = _float_or_none(info.get("angular_span"))
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if (
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not _is_effectively_full_cylinder(info)
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and angular_span is not None
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and angular_span < math.tau * 0.92
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) or _text(info.get("slot_kind")) == "partial-cylindrical-groove" or "槽/半孔候选" in feature_type:
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return 30
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return 20
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if feature_guess == "boss/outer-round candidate":
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return 40
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if feature_guess == "round/fillet candidate":
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return 50
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if "孔" in combined_text:
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return 20
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if "槽" in combined_text or "半孔" in combined_text:
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return 30
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if "凸台" in combined_text or "外圆" in combined_text:
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return 40
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if "圆角" in combined_text or "倒圆" in combined_text:
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return 50
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return 90
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if surface == "edge" or _text(info.get("curve")):
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if "圆角" in combined_text:
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return 52
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if "倒角" in combined_text:
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return 53
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return 70
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if surface in {"cone", "sphere", "torus"}:
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return 80
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if "壳体" in combined_text or _text(info.get("shell_region_status")) == "candidate":
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return 60
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return 90
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def feature_recognition_priority_bucket(priority: int) -> tuple[int, str, str]:
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selected = USER_PRIORITY_BUCKETS[-1]
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for bucket in USER_PRIORITY_BUCKETS:
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if priority >= bucket[0]:
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selected = bucket
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else:
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break
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return selected
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def feature_recognition_priority_label(info: Mapping[str, object]) -> str:
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priority = feature_recognition_priority(info)
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_start, label, _reason = feature_recognition_priority_bucket(priority)
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return f"{priority:02d} · {label}"
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def feature_recognition_priority_reason(info: Mapping[str, object]) -> str:
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priority = feature_recognition_priority(info)
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_start, _label, reason = feature_recognition_priority_bucket(priority)
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return reason
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def feature_recognition_sort_key(info: Mapping[str, object]) -> tuple[int, int, int, int, int, int]:
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status_order = {"ready": 0, "candidate": 0, "caution": 1, "blocked": 2}
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risk_order = {"low": 0, "medium": 1, "high": 2, "blocked": 3}
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target_id = info.get("target_id", info.get("face_id", info.get("edge_id", -1)))
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try:
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numeric_target = int(target_id)
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except (TypeError, ValueError):
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numeric_target = -1
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return (
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feature_recognition_priority(info),
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status_order.get(_text(info.get("status")), 9),
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risk_order.get(_text(info.get("risk")), 9),
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_confidence_sort_rank(info.get("confidence") or info.get("recognition_confidence")),
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-external_relation_score_bonus(info),
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numeric_target,
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)
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