xxx
This commit is contained in:
@@ -0,0 +1,38 @@
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"""add product_id to stp_files
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Revision ID: 006c18c51b0d
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Revises: 9928d7f8c1ef
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Create Date: 2026-07-23 10:37:56.516787
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"""
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from typing import Sequence, Union
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from alembic import op
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import sqlalchemy as sa
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# revision identifiers, used by Alembic.
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revision: str = '006c18c51b0d'
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down_revision: Union[str, Sequence[str], None] = '9928d7f8c1ef'
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branch_labels: Union[str, Sequence[str], None] = None
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depends_on: Union[str, Sequence[str], None] = None
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def upgrade() -> None:
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"""Upgrade schema: stp_files 加 product_id 外键，关联进销存成品。"""
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op.add_column("stp_files", sa.Column("product_id", sa.Integer(), nullable=True))
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op.create_index("ix_stp_files_product_id", "stp_files", ["product_id"])
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op.create_foreign_key(
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"fk_stp_files_product_id_products",
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"stp_files",
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"products",
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["product_id"],
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["id"],
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)
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def downgrade() -> None:
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"""Downgrade schema."""
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op.drop_constraint("fk_stp_files_product_id_products", "stp_files", type_="foreignkey")
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op.drop_index("ix_stp_files_product_id", table_name="stp_files")
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op.drop_column("stp_files", "product_id")
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@@ -132,7 +132,7 @@
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### P2-1 打通模具分析 -> 进销存（最高产品价值）
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- **现状**：`STPFile` 无 `product_id`，moldinsight 与 inventory 零数据关联。
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- **目标**：`STPFile` 加 `product_id` 外键（可空），分析完成后一键创建 `Product(finished)` 并回写。
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- **状态**：- [ ]
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- **状态**：- [x]
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### P2-2 真 AI 落地，砍掉假 AI
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- **现状**：`ai_mold_assistant.py` 209 行纯 stub 从未被调用；`ai_parting_detector.py` GNN 框架完整但无权重；`llm_service` 是唯一真接 AI（且有 P0-2 bug）。
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@@ -143,6 +143,10 @@
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- 依赖 P1-2 完成后才有性价比。
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- **状态**：- [ ]
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### P2 执行结果
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- ✅ **P2-1 打通模具分析 -> 进销存**：`STPFile` 加 `product_id` 外键（nullable+index+FK）+ Alembic 迁移 `006c18c51b0d`（首次真实迁移）；inventory `POST /api/products/from-task/{task_id}` 端点（按 task_id 查 STPFile，幂等创建 `Product(finished)`，回写 product_id，SKU=`MI{stp_file_id}`，描述含体积/重量/表面积）；前端 ResultView 导出栏加「创建为成品」按钮。py_compile + alembic heads + vue-tsc 0 错误通过。**模具分析 -> 成品 -> BOM -> 销售/采购的业务闭环接通**
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---
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## P3 工程治理（穿插顺手做）
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@@ -163,5 +167,5 @@
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|------|------|--------|--------|
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| P0 | 7 | 6 修复 + 1 排查 | - |
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| P1 | 5 | 4 | P1-1+P1-5+P1-4 完成 + P1-2 注册表完成（Stage 暂缓） |
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| P2 | 3 | 0 | - |
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| P2 | 3 | 1 | P2-1 完成 |
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| P3 | 7 | 0 | - |
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@@ -120,6 +120,9 @@
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<t-button type="default" size="small" @click="exportCAD('brep')" title="导出BRep格式（FreeCAD原生）">
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导出 BRep
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</t-button>
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<t-button type="default" size="small" @click="createProductFromAnalysis" :loading="state.creatingProduct" title="将本次模具分析创建为进销存成品，可在进销存模块继续配置 BOM / 销售">
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📋 创建为成品
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</t-button>
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</div>
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<div id="preview-3d" v-if="selectedHtmlFile" class="viewer-section viewer-section-hero">
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@@ -603,7 +606,8 @@ const state = reactive({
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surface_quality: 'standard',
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controller: 'fanuc',
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include_gcode: false
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}
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},
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creatingProduct: false
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})
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const camSteelOptions = [
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@@ -644,6 +648,19 @@ const loadTask = async () => {
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}
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}
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const createProductFromAnalysis = async () => {
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const taskId = route.params.taskId as string
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try {
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state.creatingProduct = true
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const product = await apiRequest<any>(`/api/products/from-task/${taskId}`, { method: 'POST' })
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addNotification(`已创建成品：${product.name}（SKU: ${product.sku}）`, 'success')
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} catch (e) {
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handleApiError(e, '创建成品')
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} finally {
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state.creatingProduct = false
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}
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}
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onMounted(() => {
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if (!appStore.user) {
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router.push('/login')
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@@ -14,10 +14,11 @@ from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import select, or_, func, delete
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from typing import Optional, List, Dict
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from decimal import Decimal
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from pathlib import Path
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from shared.database.database import get_db_session
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from shared.services.auth_service import get_current_active_user, get_current_admin_user
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from shared.models.database import User, Product, ProductMaterial
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from shared.models.database import User, Product, ProductMaterial, STPFile, ProcessingTask
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from ..schemas import (
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ProductCreate,
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ProductResponse,
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@@ -118,6 +119,70 @@ async def create_product(
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return _build_product_response(product, 0)
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@router.post("/from-task/{task_id}", response_model=ProductResponse, status_code=201)
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async def create_product_from_task(
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task_id: str,
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db_session: AsyncSession = Depends(get_db_session),
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current_user: User = Depends(get_current_active_user),
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):
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"""从模具分析任务创建进销存成品，回写 stp_files.product_id（P2-1）"""
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task_result = await db_session.execute(select(ProcessingTask).where(ProcessingTask.task_id == task_id))
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task = task_result.scalar_one_or_none()
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if not task:
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raise HTTPException(status_code=404, detail="分析任务不存在")
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stp_result = await db_session.execute(select(STPFile).where(STPFile.id == task.stp_file_id))
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stp_file = stp_result.scalar_one_or_none()
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if not stp_file:
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raise HTTPException(status_code=404, detail="STP 分析记录不存在")
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# 已关联成品则直接返回（幂等）
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if stp_file.product_id:
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existed = await db_session.execute(select(Product).where(Product.id == stp_file.product_id))
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product = existed.scalar_one_or_none()
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if product:
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return _build_product_response(product, 0)
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# 生成唯一 SKU：MI{stp_file_id}，冲突则追加序号
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base_sku = f"MI{stp_file_id}"
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sku = base_sku
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n = 1
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while True:
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conflict = await db_session.execute(select(Product).where(Product.sku == sku))
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if not conflict.scalar_one_or_none():
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break
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n += 1
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sku = f"{base_sku}-{n}"
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name = Path(stp_file.original_filename or f"mold_{stp_file_id}").stem or f"模具分析-{stp_file_id}"
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desc_parts = []
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if stp_file.volume:
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desc_parts.append(f"体积 {stp_file.volume:.1f} mm³")
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if stp_file.product_weight:
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desc_parts.append(f"重量 {stp_file.product_weight:.2f} g")
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if stp_file.surface_area:
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desc_parts.append(f"表面积 {stp_file.surface_area:.1f} mm²")
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description = "由模具分析创建" + ("：" + "；".join(desc_parts) if desc_parts else "")
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product = Product(
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sku=sku,
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name=name,
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description=description,
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category="模具成品",
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unit="件",
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item_type="finished",
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cost_price=0,
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sale_price=0,
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min_stock=0,
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max_stock=0,
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)
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db_session.add(product)
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await db_session.flush()
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stp_file.product_id = product.id
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await db_session.commit()
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await db_session.refresh(product)
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return _build_product_response(product, 0)
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@router.put("/{product_id}", response_model=ProductResponse)
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async def update_product(
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product_id: int,
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@@ -279,27 +279,6 @@ async def design_complete_mold_system(
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return {"status": "success", "data": result}
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@router.post("/ai-parting-detect")
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async def ai_parting_surface_detect(
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request: Request,
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current_user: User = Depends(get_current_active_user),
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):
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body = await request.json()
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task_id = body.get("task_id")
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if not task_id:
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raise HTTPException(404, "缺少 task_id")
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task_data = await _get_task_data(task_id)
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if not task_data:
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raise HTTPException(404, "任务不存在")
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geometry_data = task_data.get("geometry_data")
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if not geometry_data:
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raise HTTPException(400, "该任务尚未完成几何分析")
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from moldinsight.core.ai_parting_detector import AIPartingSurfaceDetectorV2
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detector = AIPartingSurfaceDetectorV2(use_gnn=True)
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result = detector._detect_with_geometry(None, geometry_data)
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return {"status": "success", "data": result}
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@router.post("/detect-undercuts")
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async def detect_undercuts(
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request: Request,
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@@ -323,6 +302,34 @@ async def detect_undercuts(
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return {"status": "success", "data": result}
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@router.post("/cost-estimate")
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async def estimate_cost(
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request: Request,
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current_user: User = Depends(get_current_active_user),
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):
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"""LLM 模具成本估算（P2-2：真 AI 落地，需启用 LLM）"""
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body = await request.json()
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task_id = body.get("task_id")
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if not task_id:
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raise HTTPException(404, "缺少 task_id")
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task_data = await _get_task_data(task_id)
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if not task_data:
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raise HTTPException(404, "任务不存在")
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analysis_result = task_data.get("analysis_result")
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if not analysis_result:
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raise HTTPException(400, "该任务尚未完成分析")
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detailed_context = {
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"candidate_schemes": task_data.get("candidate_schemes", []),
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"geometry_data": task_data.get("geometry_data", {}),
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"metadata": {"selected_material": task_data.get("material")},
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}
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from moldinsight.services.llm_service import llm_service
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result = await llm_service.estimate_cost(analysis_result, detailed_context)
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if result is None:
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raise HTTPException(503, "成本估算不可用（LLM 未启用或生成失败）")
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return {"status": "success", "data": result}
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@router.post("/design-cam")
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async def design_mold_cam(
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request: Request,
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@@ -1,209 +0,0 @@
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"""
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AI 分模辅助模型接口示例
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此文件展示了如何创建 AI 模型来辅助分模过程。
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实际使用时需要替换为真实的 AI 模型。
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"""
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from typing import Dict, Any, Optional
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import numpy as np
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from OCC.Core.TopoDS import TopoDS_Shape, TopoDS_Face
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class AIPartingSurfaceDetector:
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"""
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AI 分型面检测器（示例接口）
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功能：
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- 分析产品 3D 几何
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- 预测最优分型面位置和方向
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- 识别倒扣区域
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"""
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def __init__(self, model_path: Optional[str] = None):
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"""
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初始化 AI 分型面检测器
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Args:
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model_path: 训练好的模型路径
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"""
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self.model_path = model_path
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self.model = None
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# 如果提供了模型路径，加载模型
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if model_path:
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self._load_model(model_path)
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def _load_model(self, model_path: str):
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"""加载训练好的 AI 模型"""
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# TODO: 实现模型加载逻辑
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# 示例：
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# import torch
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# self.model = torch.load(model_path)
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print(f"AI 模型加载：{model_path}")
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def detect(self, product_shape: TopoDS_Shape, analysis: Dict) -> Optional[Dict]:
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"""
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检测最优分型面
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Args:
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product_shape: OpenCASCADE 形状对象
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analysis: 几何分析结果（包含 bounding_box, volume 等）
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Returns:
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{
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"origin": [x, y, z], # 分型面原点
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"normal": [nx, ny, nz], # 分型面法向量
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"confidence": 0.95, # 置信度
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"parting_line": [...] # 可选的分型线
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}
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"""
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# TODO: 使用 AI 模型进行预测
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# 这里是示例返回
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# 1. 将产品形状转换为 AI 模型输入
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# - 体素化 (voxelization)
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# - 点云 (point cloud)
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# - 多视图 (multi-view images)
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input_data = self._preprocess_shape(product_shape, analysis)
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# 2. 使用模型预测
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# prediction = self.model.predict(input_data)
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# 3. 返回预测结果
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return {
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"origin": [0, 0, analysis["bounding_box"]["center"][2]],
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"normal": [0, 0, 1], # Z 方向
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||||
"confidence": 0.85,
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||||
"undercut_regions": [] # 倒扣区域
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||||
}
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||||
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||||
def _preprocess_shape(self, shape: TopoDS_Shape, analysis: Dict) -> TopoDS_Shape:
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||||
"""
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||||
预处理产品形状为 AI 模型输入
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||||
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||||
可能的预处理方式：
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||||
1. 体素化：将 3D 模型转换为 3D 网格
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||||
2. 点云：采样表面点
|
||||
3. 多视图：渲染多个角度的 2D 图像
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||||
"""
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||||
# TODO: 实现预处理逻辑
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||||
return None
|
||||
|
||||
|
||||
class AIDraftAnalyzer:
|
||||
"""
|
||||
AI 拔模分析器（示例接口）
|
||||
|
||||
功能：
|
||||
- 分析哪些面需要拔模
|
||||
- 预测最优拔模角度
|
||||
- 检测脱模干涉
|
||||
"""
|
||||
|
||||
def __init__(self, model_path: Optional[str] = None):
|
||||
self.model_path = model_path
|
||||
self.model = None
|
||||
|
||||
if model_path:
|
||||
self._load_model(model_path)
|
||||
|
||||
def _load_model(self, model_path: str):
|
||||
"""加载训练好的 AI 模型"""
|
||||
print(f"AI 拔模分析模型加载：{model_path}")
|
||||
|
||||
def analyze(self, product_shape: TopoDS_Shape, parting_surface: TopoDS_Face,
|
||||
base_draft_angle: float) -> Optional[Dict]:
|
||||
"""
|
||||
分析拔模需求
|
||||
|
||||
Args:
|
||||
product_shape: 产品形状
|
||||
parting_surface: 分型面
|
||||
base_draft_angle: 基础拔模角（度）
|
||||
|
||||
Returns:
|
||||
{
|
||||
"drafted_shape": ..., # 应用拔模后的形状
|
||||
"draft_angles": {...}, # 各面的拔模角
|
||||
"interference_areas": [...], # 干涉区域
|
||||
"recommendations": [...] # 优化建议
|
||||
}
|
||||
"""
|
||||
# TODO: 使用 AI 模型分析拔模
|
||||
|
||||
# 示例返回
|
||||
return {
|
||||
"drafted_shape": product_shape, # 简化：返回原始形状
|
||||
"draft_angles": {"default": base_draft_angle},
|
||||
"interference_areas": [],
|
||||
"recommendations": ["建议增加圆角", "壁厚均匀化"]
|
||||
}
|
||||
|
||||
|
||||
class AICavityLayoutOptimizer:
|
||||
"""
|
||||
AI 型腔布局优化器（示例接口）
|
||||
|
||||
功能：
|
||||
- 优化多型腔排列
|
||||
- 设计流道系统
|
||||
- 平衡材料流动
|
||||
"""
|
||||
|
||||
def __init__(self, model_path: Optional[str] = None):
|
||||
self.model_path = model_path
|
||||
self.model = None
|
||||
|
||||
if model_path:
|
||||
self._load_model(model_path)
|
||||
|
||||
def optimize(self, product_shape: TopoDS_Shape, cavity_count: int,
|
||||
mold_base_size: Dict) -> Optional[Dict]:
|
||||
"""
|
||||
优化型腔布局
|
||||
|
||||
Args:
|
||||
product_shape: 产品形状
|
||||
cavity_count: 型腔数量
|
||||
mold_base_size: 模架尺寸
|
||||
|
||||
Returns:
|
||||
{
|
||||
"cavity_positions": [...], # 各型腔位置
|
||||
"runner_system": {...}, # 流道系统设计
|
||||
"balance_score": 0.92, # 流动平衡评分
|
||||
"material_efficiency": 0.85 # 材料利用率
|
||||
}
|
||||
"""
|
||||
# TODO: 使用 AI 优化型腔布局
|
||||
|
||||
return {
|
||||
"cavity_positions": [[0, 0, 0]], # 示例
|
||||
"runner_system": {"type": "cold_runner"},
|
||||
"balance_score": 0.85,
|
||||
"material_efficiency": 0.80
|
||||
}
|
||||
|
||||
|
||||
# ==================== 使用示例 ====================
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 示例：如何使用 AI 模型接口
|
||||
|
||||
# 1. 创建 AI 模型实例
|
||||
parting_detector = AIPartingSurfaceDetector(model_path="models/parting_surface.pth")
|
||||
draft_analyzer = AIDraftAnalyzer(model_path="models/draft_analysis.pth")
|
||||
|
||||
# 2. 设置到 MoldCavityGenerator
|
||||
from moldinsight.core.mold_generator import MoldCavityGenerator
|
||||
|
||||
generator = MoldCavityGenerator()
|
||||
generator.set_ai_model(
|
||||
parting_detector=parting_detector,
|
||||
draft_analyzer=draft_analyzer
|
||||
)
|
||||
|
||||
# 3. 使用（AI 模型会自动介入）
|
||||
# result = generator.generate_mold_cavities(product_shape)
|
||||
|
||||
print("AI 模型接口已配置，分模时将自动使用 AI 辅助")
|
||||
@@ -1,547 +0,0 @@
|
||||
"""
|
||||
AI 分型面检测模块 - 基于 GNN 的分型面预测框架
|
||||
|
||||
架构设计：
|
||||
1. ShapeGraphBuilder - 将 OCC 形状转换为图表示（面为节点，共享边为图边）
|
||||
2. PartingSurfaceGNN - 图神经网络模型定义
|
||||
3. AIPartingSurfaceDetectorV2 - 增强版分型面检测器（集成 GNN）
|
||||
|
||||
图构建策略：
|
||||
- 节点：每个 TopoDS_Face 作为一个节点
|
||||
- 节点特征：法向量(3) + 面积(1) + 曲率(2) + 面类型(1) = 7维
|
||||
- 边：共享 TopoDS_Edge 的面之间建立边
|
||||
- 边特征：共享边长度(1) + 二面角(1) = 2维
|
||||
|
||||
GNN 模型：
|
||||
- 3层 GraphConv + 全局池化 + MLP 分类头
|
||||
- 输出：每个面的分型面归属概率 + 分型方向
|
||||
|
||||
依赖：
|
||||
- PyTorch + PyTorch Geometric（可选，缺失时回退到几何方法）
|
||||
"""
|
||||
|
||||
from typing import Dict, List, Any, Optional, Tuple
|
||||
import numpy as np
|
||||
from OCC.Core.TopoDS import TopoDS_Shape
|
||||
from shared.utils.logger import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
_TORCH_AVAILABLE = False
|
||||
_TORCH_GEOMETRIC_AVAILABLE = False
|
||||
|
||||
try:
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
_TORCH_AVAILABLE = True
|
||||
try:
|
||||
from torch_geometric.nn import GCNConv, global_mean_pool
|
||||
from torch_geometric.data import Data
|
||||
_TORCH_GEOMETRIC_AVAILABLE = True
|
||||
except ImportError:
|
||||
logger.info("PyTorch Geometric 未安装，GNN 模型不可用")
|
||||
except ImportError:
|
||||
logger.info("PyTorch 未安装，AI 分型面检测将使用几何回退方法")
|
||||
|
||||
|
||||
class ShapeGraphBuilder:
|
||||
"""将 OCC 形状转换为图表示"""
|
||||
|
||||
def build_graph(self, shape: TopoDS_Shape) -> Optional[Dict]:
|
||||
"""
|
||||
从 OCC 形状构建图数据
|
||||
|
||||
Returns:
|
||||
{
|
||||
"node_features": np.ndarray (N, 7),
|
||||
"edge_index": np.ndarray (2, E),
|
||||
"edge_features": np.ndarray (E, 2),
|
||||
"face_map": List[TopoDS_Face],
|
||||
"num_nodes": int,
|
||||
"num_edges": int
|
||||
}
|
||||
"""
|
||||
try:
|
||||
from OCC.Core.TopExp import TopExp_Explorer
|
||||
from OCC.Core.TopAbs import TopAbs_FACE, TopAbs_EDGE
|
||||
from OCC.Core.BRepAdaptor import BRepAdaptor_Surface
|
||||
from OCC.Core.GProp import GProp_GProps
|
||||
from OCC.Core.BRepGProp import brepgprop
|
||||
from OCC.Core.Bnd import Bnd_Box
|
||||
from OCC.Core.BRepBndLib import brepbndlib
|
||||
from OCC.Core.TopTools import TopTools_IndexedDataMapOfShapeListOfShape
|
||||
from OCC.Core.TopExp import topexp_MapShapesAndAncestors
|
||||
from OCC.Core.TopoDS import TopoDS_Face, TopoDS_Edge, topods
|
||||
|
||||
faces = []
|
||||
face_features = []
|
||||
|
||||
explorer = TopExp_Explorer(shape, TopAbs_FACE)
|
||||
while explorer.More():
|
||||
face = topods.Face(explorer.Current())
|
||||
features = self._extract_face_features(face)
|
||||
if features is not None:
|
||||
faces.append(face)
|
||||
face_features.append(features)
|
||||
explorer.Next()
|
||||
|
||||
if not faces:
|
||||
logger.warning("未找到面，无法构建图")
|
||||
return None
|
||||
|
||||
node_features = np.array(face_features, dtype=np.float32)
|
||||
|
||||
edge_map = TopTools_IndexedDataMapOfShapeListOfShape()
|
||||
topexp_MapShapesAndAncestors(shape, TopAbs_EDGE, TopAbs_FACE, edge_map)
|
||||
|
||||
edge_list = []
|
||||
edge_features_list = []
|
||||
|
||||
for i in range(1, edge_map.Extent() + 1):
|
||||
edge = topods.Edge(edge_map.FindKey(i))
|
||||
face_list = edge_map.FindFromIndex(i)
|
||||
|
||||
connected_faces = []
|
||||
it = face_list.begin()
|
||||
while it != face_list.end():
|
||||
f = topods.Face(it.Value())
|
||||
try:
|
||||
idx = faces.index(f)
|
||||
connected_faces.append(idx)
|
||||
except ValueError:
|
||||
pass
|
||||
it.next_ptr()
|
||||
|
||||
if len(connected_faces) >= 2:
|
||||
edge_feat = self._extract_edge_features(edge, connected_faces, faces)
|
||||
for j in range(len(connected_faces)):
|
||||
for k in range(j + 1, len(connected_faces)):
|
||||
edge_list.append([connected_faces[j], connected_faces[k]])
|
||||
edge_features_list.append(edge_feat)
|
||||
|
||||
if not edge_list:
|
||||
logger.warning("未找到边连接，返回无图边的图")
|
||||
edge_index = np.zeros((2, 0), dtype=np.int64)
|
||||
edge_features_arr = np.zeros((0, 2), dtype=np.float32)
|
||||
else:
|
||||
edge_index = np.array(edge_list, dtype=np.int64).T
|
||||
rev_edges = np.array([[e[1], e[0]] for e in edge_list], dtype=np.int64).T
|
||||
edge_index = np.concatenate([edge_index, rev_edges], axis=1)
|
||||
edge_features_arr = np.array(edge_features_list, dtype=np.float32)
|
||||
edge_features_arr = np.concatenate([edge_features_arr, edge_features_arr], axis=0)
|
||||
|
||||
return {
|
||||
"node_features": node_features,
|
||||
"edge_index": edge_index,
|
||||
"edge_features": edge_features_arr,
|
||||
"face_map": faces,
|
||||
"num_nodes": len(faces),
|
||||
"num_edges": edge_index.shape[1]
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"图构建失败: {e}")
|
||||
return None
|
||||
|
||||
def _extract_face_features(self, face: Any) -> Optional[np.ndarray]:
|
||||
"""
|
||||
提取面特征：[nx, ny, nz, area, u_curvature, v_curvature, face_type]
|
||||
"""
|
||||
try:
|
||||
from OCC.Core.BRepAdaptor import BRepAdaptor_Surface
|
||||
from OCC.Core.GProp import GProp_GProps
|
||||
from OCC.Core.BRepGProp import brepgprop
|
||||
|
||||
surface = BRepAdaptor_Surface(face)
|
||||
|
||||
u = (surface.FirstUParameter() + surface.LastUParameter()) / 2
|
||||
v = (surface.FirstVParameter() + surface.LastVParameter()) / 2
|
||||
|
||||
if surface.GetType() == 0:
|
||||
normal = surface.Plane().Position().Direction()
|
||||
face_type = 0.0
|
||||
u_curv = 0.0
|
||||
v_curv = 0.0
|
||||
elif surface.GetType() == 1:
|
||||
normal = surface.Cylinder().Position().Direction()
|
||||
face_type = 1.0
|
||||
radius = surface.Cylinder().Radius()
|
||||
u_curv = 1.0 / radius if radius > 0.001 else 0.0
|
||||
v_curv = 0.0
|
||||
elif surface.GetType() == 2:
|
||||
normal = surface.Cone().Position().Direction()
|
||||
face_type = 2.0
|
||||
u_curv = 0.0
|
||||
v_curv = 0.0
|
||||
elif surface.GetType() == 3:
|
||||
normal = surface.Sphere().Position().Direction()
|
||||
face_type = 3.0
|
||||
radius = surface.Sphere().Radius()
|
||||
u_curv = 1.0 / radius if radius > 0.001 else 0.0
|
||||
v_curv = 1.0 / radius if radius > 0.001 else 0.0
|
||||
elif surface.GetType() == 4:
|
||||
normal = surface.Torus().Position().Direction()
|
||||
face_type = 4.0
|
||||
u_curv = 0.0
|
||||
v_curv = 0.0
|
||||
else:
|
||||
from OCC.Core.BRepLProp import BRepLProp_SLProps
|
||||
props = BRepLProp_SLProps(surface, 2, 0.001)
|
||||
props.SetParameters(u, v)
|
||||
if props.IsNormalDefined():
|
||||
normal = props.Normal()
|
||||
else:
|
||||
normal = gp_Dir(0, 0, 1)
|
||||
face_type = 5.0
|
||||
u_curv = 0.0
|
||||
v_curv = 0.0
|
||||
|
||||
face_props = GProp_GProps()
|
||||
brepgprop.SurfaceProperties(face, face_props)
|
||||
area = face_props.Mass()
|
||||
|
||||
return np.array([
|
||||
normal.X(), normal.Y(), normal.Z(),
|
||||
area,
|
||||
u_curv, v_curv,
|
||||
face_type
|
||||
], dtype=np.float32)
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"面特征提取失败: {e}")
|
||||
return None
|
||||
|
||||
def _extract_edge_features(self, edge: Any, connected_faces: List[int],
|
||||
faces: List) -> np.ndarray:
|
||||
"""
|
||||
提取边特征：[edge_length, dihedral_angle]
|
||||
"""
|
||||
try:
|
||||
from OCC.Core.BRepAdaptor import BRepAdaptor_Curve
|
||||
from OCC.Core.GProp import GProp_GProps
|
||||
from OCC.Core.BRepGProp import brepgprop
|
||||
|
||||
curve = BRepAdaptor_Curve(edge)
|
||||
first = curve.FirstParameter()
|
||||
last = curve.LastParameter()
|
||||
|
||||
edge_len = abs(last - first)
|
||||
|
||||
dihedral = 0.0
|
||||
if len(connected_faces) >= 2:
|
||||
n1 = self._get_face_normal_fast(faces[connected_faces[0]])
|
||||
n2 = self._get_face_normal_fast(faces[connected_faces[1]])
|
||||
if n1 is not None and n2 is not None:
|
||||
dot = np.clip(np.dot(n1, n2), -1.0, 1.0)
|
||||
dihedral = np.arccos(dot)
|
||||
|
||||
return np.array([edge_len, dihedral], dtype=np.float32)
|
||||
|
||||
except Exception:
|
||||
return np.array([0.0, 0.0], dtype=np.float32)
|
||||
|
||||
def _get_face_normal_fast(self, face: Any) -> Optional[np.ndarray]:
|
||||
"""快速获取面法向量（numpy数组）"""
|
||||
try:
|
||||
from OCC.Core.BRepAdaptor import BRepAdaptor_Surface
|
||||
surface = BRepAdaptor_Surface(face)
|
||||
if surface.GetType() == 0:
|
||||
n = surface.Plane().Position().Direction()
|
||||
return np.array([n.X(), n.Y(), n.Z()])
|
||||
return None
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
|
||||
if _TORCH_GEOMETRIC_AVAILABLE:
|
||||
|
||||
class PartingSurfaceGNN(nn.Module):
|
||||
"""
|
||||
分型面检测 GNN 模型
|
||||
|
||||
架构：
|
||||
- 3层 GCNConv (hidden_dim=64)
|
||||
- 全局平均池化
|
||||
- 3层 MLP 分类头
|
||||
- 输出：每个面的分型面归属概率 (0-1)
|
||||
"""
|
||||
|
||||
def __init__(self, input_dim: int = 7, hidden_dim: int = 64,
|
||||
num_layers: int = 3, dropout: float = 0.3):
|
||||
super().__init__()
|
||||
|
||||
self.input_dim = input_dim
|
||||
self.hidden_dim = hidden_dim
|
||||
self.num_layers = num_layers
|
||||
|
||||
self.input_proj = nn.Linear(input_dim, hidden_dim)
|
||||
|
||||
self.convs = nn.ModuleList()
|
||||
self.bns = nn.ModuleList()
|
||||
for _ in range(num_layers):
|
||||
self.convs.append(GCNConv(hidden_dim, hidden_dim))
|
||||
self.bns.append(nn.BatchNorm1d(hidden_dim))
|
||||
|
||||
self.dropout = dropout
|
||||
|
||||
self.mlp = nn.Sequential(
|
||||
nn.Linear(hidden_dim, hidden_dim),
|
||||
nn.ReLU(),
|
||||
nn.Dropout(dropout),
|
||||
nn.Linear(hidden_dim, hidden_dim // 2),
|
||||
nn.ReLU(),
|
||||
nn.Dropout(dropout),
|
||||
nn.Linear(hidden_dim // 2, 1),
|
||||
)
|
||||
|
||||
def forward(self, data: Data) -> torch.Tensor:
|
||||
x, edge_index = data.x, data.edge_index
|
||||
|
||||
x = self.input_proj(x)
|
||||
x = F.relu(x)
|
||||
|
||||
for conv, bn in zip(self.convs, self.bns):
|
||||
x = conv(x, edge_index)
|
||||
x = bn(x)
|
||||
x = F.relu(x)
|
||||
x = F.dropout(x, p=self.dropout, training=self.training)
|
||||
|
||||
out = self.mlp(x)
|
||||
return torch.sigmoid(out).squeeze(-1)
|
||||
|
||||
class PartingDirectionHead(nn.Module):
|
||||
"""
|
||||
分型方向预测头
|
||||
|
||||
基于全局池化的面特征，预测分型方向向量
|
||||
"""
|
||||
|
||||
def __init__(self, hidden_dim: int = 64):
|
||||
super().__init__()
|
||||
self.direction_mlp = nn.Sequential(
|
||||
nn.Linear(hidden_dim, hidden_dim),
|
||||
nn.ReLU(),
|
||||
nn.Linear(hidden_dim, 3),
|
||||
)
|
||||
|
||||
def forward(self, node_embeddings: torch.Tensor,
|
||||
batch: torch.Tensor) -> torch.Tensor:
|
||||
pooled = global_mean_pool(node_embeddings, batch)
|
||||
direction = self.direction_mlp(pooled)
|
||||
direction = F.normalize(direction, p=2, dim=-1)
|
||||
return direction
|
||||
|
||||
|
||||
class AIPartingSurfaceDetectorV2:
|
||||
"""
|
||||
增强版 AI 分型面检测器
|
||||
|
||||
支持：
|
||||
1. GNN 模型推理（需要 PyTorch + PyG）
|
||||
2. 几何方法回退（无需任何 AI 依赖）
|
||||
3. 模型训练数据收集
|
||||
"""
|
||||
|
||||
def __init__(self, model_path: Optional[str] = None,
|
||||
use_gnn: bool = True,
|
||||
device: str = "cpu"):
|
||||
self.model = None
|
||||
self.direction_head = None
|
||||
self.graph_builder = ShapeGraphBuilder()
|
||||
self.device = device
|
||||
self.use_gnn = use_gnn and _TORCH_GEOMETRIC_AVAILABLE
|
||||
|
||||
if model_path and self.use_gnn:
|
||||
self._load_model(model_path)
|
||||
|
||||
def _load_model(self, model_path: str):
|
||||
"""加载训练好的 GNN 模型"""
|
||||
if not _TORCH_GEOMETRIC_AVAILABLE:
|
||||
logger.warning("PyTorch Geometric 不可用，无法加载 GNN 模型")
|
||||
return
|
||||
|
||||
try:
|
||||
checkpoint = torch.load(model_path, map_location=self.device)
|
||||
self.model = PartingSurfaceGNN(
|
||||
input_dim=checkpoint.get("input_dim", 7),
|
||||
hidden_dim=checkpoint.get("hidden_dim", 64),
|
||||
)
|
||||
self.model.load_state_dict(checkpoint["model_state_dict"])
|
||||
self.model.to(self.device)
|
||||
self.model.eval()
|
||||
|
||||
if "direction_head_state_dict" in checkpoint:
|
||||
self.direction_head = PartingDirectionHead(
|
||||
hidden_dim=checkpoint.get("hidden_dim", 64)
|
||||
)
|
||||
self.direction_head.load_state_dict(checkpoint["direction_head_state_dict"])
|
||||
self.direction_head.to(self.device)
|
||||
self.direction_head.eval()
|
||||
|
||||
logger.info(f"GNN 模型加载成功: {model_path}")
|
||||
except Exception as e:
|
||||
logger.error(f"GNN 模型加载失败: {e}")
|
||||
self.model = None
|
||||
|
||||
def detect(self, product_shape: TopoDS_Shape, analysis: Dict) -> Optional[Dict]:
|
||||
"""
|
||||
检测最优分型面
|
||||
|
||||
Args:
|
||||
product_shape: OpenCASCADE 形状对象
|
||||
analysis: 几何分析结果
|
||||
|
||||
Returns:
|
||||
{
|
||||
"origin": [x, y, z],
|
||||
"normal": [nx, ny, nz],
|
||||
"confidence": float,
|
||||
"parting_line": [...],
|
||||
"method": "gnn" | "geometric"
|
||||
}
|
||||
"""
|
||||
if self.use_gnn and self.model is not None:
|
||||
result = self._detect_with_gnn(product_shape, analysis)
|
||||
if result is not None:
|
||||
return result
|
||||
|
||||
return self._detect_with_geometry(product_shape, analysis)
|
||||
|
||||
def _detect_with_gnn(self, shape: TopoDS_Shape, analysis: Dict) -> Optional[Dict]:
|
||||
"""使用 GNN 模型检测分型面"""
|
||||
if not _TORCH_GEOMETRIC_AVAILABLE:
|
||||
return None
|
||||
|
||||
try:
|
||||
graph_data = self.graph_builder.build_graph(shape)
|
||||
if graph_data is None:
|
||||
return None
|
||||
|
||||
node_features = torch.tensor(
|
||||
graph_data["node_features"], dtype=torch.float32
|
||||
).to(self.device)
|
||||
edge_index = torch.tensor(
|
||||
graph_data["edge_index"], dtype=torch.long
|
||||
).to(self.device)
|
||||
|
||||
data = Data(x=node_features, edge_index=edge_index)
|
||||
|
||||
with torch.no_grad():
|
||||
face_probs = self.model(data)
|
||||
|
||||
if self.direction_head is not None:
|
||||
batch = torch.zeros(
|
||||
data.num_nodes, dtype=torch.long, device=self.device
|
||||
)
|
||||
direction = self.direction_head(data.x, batch)
|
||||
normal = direction.cpu().numpy().tolist()
|
||||
else:
|
||||
normal = [0, 0, 1]
|
||||
|
||||
parting_face_mask = face_probs.cpu().numpy() > 0.5
|
||||
confidence = float(face_probs.mean().cpu().numpy())
|
||||
|
||||
bbox = analysis.get("bounding_box", {})
|
||||
center = bbox.get("center", [0, 0, 0])
|
||||
|
||||
return {
|
||||
"origin": center,
|
||||
"normal": normal,
|
||||
"confidence": confidence,
|
||||
"method": "gnn",
|
||||
"face_probabilities": face_probs.cpu().numpy().tolist(),
|
||||
"parting_face_count": int(parting_face_mask.sum()),
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"GNN 检测失败，回退到几何方法: {e}")
|
||||
return None
|
||||
|
||||
def _detect_with_geometry(self, shape: TopoDS_Shape, analysis: Dict) -> Dict:
|
||||
"""几何方法回退：基于法向量统计的分型面检测"""
|
||||
try:
|
||||
graph_data = self.graph_builder.build_graph(shape)
|
||||
if graph_data is not None:
|
||||
node_features = graph_data["node_features"]
|
||||
normals = node_features[:, :3]
|
||||
areas = node_features[:, 3]
|
||||
|
||||
total_area = areas.sum()
|
||||
if total_area > 0:
|
||||
weights = areas / total_area
|
||||
weighted_normal = np.sum(normals * weights[:, np.newaxis], axis=0)
|
||||
else:
|
||||
weighted_normal = np.mean(normals, axis=0)
|
||||
|
||||
length = np.linalg.norm(weighted_normal)
|
||||
if length > 0.001:
|
||||
weighted_normal /= length
|
||||
else:
|
||||
weighted_normal = np.array([0, 0, 1])
|
||||
|
||||
dot_products = np.abs(np.dot(normals, weighted_normal))
|
||||
confidence = float(np.mean(dot_products))
|
||||
|
||||
bbox = analysis.get("bounding_box", {})
|
||||
center = bbox.get("center", [0, 0, 0])
|
||||
|
||||
return {
|
||||
"origin": center,
|
||||
"normal": weighted_normal.tolist(),
|
||||
"confidence": confidence,
|
||||
"method": "geometric",
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"几何方法检测失败: {e}")
|
||||
|
||||
bbox = analysis.get("bounding_box", {})
|
||||
center = bbox.get("center", [0, 0, 0])
|
||||
return {
|
||||
"origin": center,
|
||||
"normal": [0, 0, 1],
|
||||
"confidence": 0.5,
|
||||
"method": "fallback",
|
||||
}
|
||||
|
||||
def collect_training_sample(self, shape: TopoDS_Shape, analysis: Dict,
|
||||
ground_truth_normal: List[float],
|
||||
ground_truth_origin: List[float]) -> Optional[Dict]:
|
||||
"""
|
||||
收集训练样本
|
||||
|
||||
Args:
|
||||
shape: OCC 形状
|
||||
analysis: 几何分析
|
||||
ground_truth_normal: 人工标注的分型方向
|
||||
ground_truth_origin: 人工标注的分型面原点
|
||||
|
||||
Returns:
|
||||
可序列化的训练样本
|
||||
"""
|
||||
graph_data = self.graph_builder.build_graph(shape)
|
||||
if graph_data is None:
|
||||
return None
|
||||
|
||||
return {
|
||||
"node_features": graph_data["node_features"].tolist(),
|
||||
"edge_index": graph_data["edge_index"].tolist(),
|
||||
"edge_features": graph_data["edge_features"].tolist(),
|
||||
"label_normal": ground_truth_normal,
|
||||
"label_origin": ground_truth_origin,
|
||||
"bounding_box": analysis.get("bounding_box", {}),
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
def create_model(input_dim: int = 7, hidden_dim: int = 64,
|
||||
num_layers: int = 3) -> Optional[Any]:
|
||||
"""创建新的 GNN 模型实例"""
|
||||
if not _TORCH_GEOMETRIC_AVAILABLE:
|
||||
logger.warning("PyTorch Geometric 不可用，无法创建模型")
|
||||
return None
|
||||
return PartingSurfaceGNN(
|
||||
input_dim=input_dim,
|
||||
hidden_dim=hidden_dim,
|
||||
num_layers=num_layers,
|
||||
)
|
||||
@@ -462,37 +462,6 @@ class AluminumFoamMoldGenerator(BaseMoldGenerator):
|
||||
"bounds": metadata["bounds"],
|
||||
}
|
||||
|
||||
def _create_parting_surface_from_ai(self, ai_result: Dict, analysis: Dict,
|
||||
shape: Optional[TopoDS_Shape] = None) -> Dict:
|
||||
"""从 AI 结果创建分型面"""
|
||||
origin = ai_result.get("origin", [0, 0, 0])
|
||||
normal = ai_result.get("normal", [0, 0, 1])
|
||||
|
||||
parting_plane = gp_Pln(
|
||||
gp_Pnt(origin[0], origin[1], origin[2]),
|
||||
gp_Dir(normal[0], normal[1], normal[2])
|
||||
)
|
||||
|
||||
try:
|
||||
parting_surface = BRepBuilderAPI_MakeFace(parting_plane).Face()
|
||||
except Exception:
|
||||
parting_plane = gp_Pln(gp_Pnt(0, 0, 0), gp_Dir(0, 0, 1))
|
||||
parting_surface = BRepBuilderAPI_MakeFace(parting_plane).Face()
|
||||
|
||||
if shape is not None:
|
||||
parting_line = self._calculate_parting_line(shape, parting_surface)
|
||||
else:
|
||||
parting_line = []
|
||||
|
||||
return {
|
||||
"primary_surface": parting_surface,
|
||||
"primary_line": parting_line,
|
||||
"primary_direction": normal,
|
||||
"confidence": ai_result.get("confidence", 0.8),
|
||||
"additional_surfaces": [],
|
||||
"surface_count": 1
|
||||
}
|
||||
|
||||
# ==================== 辅助方法 ====================
|
||||
|
||||
def _calculate_mold_size(self, analysis: Dict) -> Dict[str, float]:
|
||||
|
||||
@@ -33,14 +33,6 @@ class BaseMoldGenerator:
|
||||
self.draft_angle = draft_angle
|
||||
self.material_density = material_density
|
||||
|
||||
self.ai_parting_detector: Optional[Any] = None
|
||||
self.ai_draft_analyzer: Optional[Any] = None
|
||||
|
||||
def set_ai_model(self, parting_detector: Any = None, draft_analyzer: Any = None):
|
||||
self.ai_parting_detector = parting_detector
|
||||
self.ai_draft_analyzer = draft_analyzer
|
||||
logger.info("AI 模型接口已设置")
|
||||
|
||||
def _apply_shrinkage_compensation(self, shape: TopoDS_Shape) -> TopoDS_Shape:
|
||||
scale_factor = 1.0 + self.shrinkage_rate
|
||||
trsf = gp_Trsf()
|
||||
|
||||
@@ -214,21 +214,6 @@ class MoldCavityGenerator(BaseMoldGenerator):
|
||||
|
||||
def _detect_primary_parting(self, shape: TopoDS_Shape, analysis: Dict) -> Dict[str, Any]:
|
||||
"""检测主分型面（AI优先 → 几何法向量 → 简化回退）"""
|
||||
if self.ai_parting_detector is not None:
|
||||
try:
|
||||
ai_result = self.ai_parting_detector.detect(shape, analysis)
|
||||
if ai_result is not None:
|
||||
surface, line = self._create_parting_surface_from_ai(ai_result, analysis, shape)
|
||||
return {
|
||||
"surface": surface,
|
||||
"line": line,
|
||||
"direction": ai_result.get("normal", [0, 0, 1]),
|
||||
"method": ai_result.get("method", "ai"),
|
||||
"confidence": ai_result.get("confidence", 0.8),
|
||||
}
|
||||
except Exception as e:
|
||||
logger.warning(f"AI 分型面检测失败: {e}")
|
||||
|
||||
try:
|
||||
normal_dir = self._analyze_face_normals(shape)
|
||||
parting_plane = self._create_optimal_parting_plane(shape, analysis, normal_dir)
|
||||
@@ -364,40 +349,6 @@ class MoldCavityGenerator(BaseMoldGenerator):
|
||||
|
||||
return parting_surface, parting_line
|
||||
|
||||
def _create_parting_surface_from_ai(self, ai_result: Dict,
|
||||
analysis: Dict, shape: Optional[TopoDS_Shape] = None) -> Tuple[TopoDS_Face, List]:
|
||||
"""
|
||||
从 AI 模型结果创建分型面（预留接口）
|
||||
|
||||
Args:
|
||||
ai_result: AI 模型输出，应包含：
|
||||
- origin: [x, y, z] 平面原点
|
||||
- normal: [nx, ny, nz] 法向量
|
||||
analysis: 几何分析结果
|
||||
shape: 产品形状（用于计算分型线）
|
||||
|
||||
Returns:
|
||||
(parting_surface, parting_line)
|
||||
"""
|
||||
origin = ai_result.get("origin", [0, 0, 0])
|
||||
normal = ai_result.get("normal", [0, 0, 1])
|
||||
|
||||
parting_plane = gp_Pln(
|
||||
gp_Pnt(origin[0], origin[1], origin[2]),
|
||||
gp_Dir(normal[0], normal[1], normal[2])
|
||||
)
|
||||
parting_surface = BRepBuilderAPI_MakeFace(parting_plane).Face()
|
||||
|
||||
if "parting_line" in ai_result:
|
||||
parting_line = ai_result["parting_line"]
|
||||
elif shape is not None:
|
||||
parting_line = self._calculate_parting_line(shape, parting_surface)
|
||||
else:
|
||||
parting_line = []
|
||||
|
||||
logger.info(f"从 AI 结果创建分型面：原点={origin}, 法向量={normal}")
|
||||
return parting_surface, parting_line
|
||||
|
||||
def _extract_parting_surface_geometry(self, surface: TopoDS_Face) -> Dict[str, Any]:
|
||||
"""提取分型面几何数据"""
|
||||
metadata = self._extract_plane_metadata(surface)
|
||||
|
||||
@@ -179,6 +179,33 @@ _PARTING_USER = """请评估以下候选分模方向并推荐最优方案：
|
||||
请综合评估制造可行性、成本和风险，给出推荐。"""
|
||||
|
||||
|
||||
_COST_ESTIMATE_SYSTEM = """你是一位资深模具报价工程师，擅长根据产品几何与模具设计方案估算模具造价与单件成本。
|
||||
|
||||
要求：
|
||||
1. 使用中文，金额用人民币（¥）
|
||||
2. 基于给定数据合理估算，数据不足时给出区间并标注假设
|
||||
3. 综合考虑：模具材料、加工复杂度（滑块/斜顶/镶件）、型腔数、产品材料用量、成型周期
|
||||
|
||||
严格输出 JSON，不要输出其他内容。JSON 格式：
|
||||
{
|
||||
"mold_cost": {
|
||||
"material": "¥XX（P20 钢，约 XX kg）",
|
||||
"machining": "¥XX（含 CNC/EDM/线切割）",
|
||||
"complexity_factor": "1.2（含 X 个滑块/斜顶）",
|
||||
"subtotal": "¥XX"
|
||||
},
|
||||
"part_cost": {
|
||||
"material": "¥XX（ABS，约 XX g）",
|
||||
"cycle_time": "30 s",
|
||||
"cost_per_part": "¥XX"
|
||||
},
|
||||
"total_mold_cost": "¥XX",
|
||||
"cost_per_part": "¥XX",
|
||||
"confidence": 0.7,
|
||||
"assumptions": ["假设模具寿命 50 万模次", "假设..."]
|
||||
}"""
|
||||
|
||||
|
||||
class LLMService:
|
||||
"""LLM 增强分析服务（单例）"""
|
||||
|
||||
@@ -308,6 +335,61 @@ class LLMService:
|
||||
logger.warning("LLM 分型推荐失败（不影响主流程）: %s", e)
|
||||
return None
|
||||
|
||||
async def estimate_cost(
|
||||
self,
|
||||
analysis_result: Dict[str, Any],
|
||||
detailed_cavity_json: Optional[Dict[str, Any]] = None,
|
||||
) -> Optional[Dict[str, Any]]:
|
||||
"""估算模具造价与单件成本 (结构化 JSON)"""
|
||||
if not self._enabled:
|
||||
return None
|
||||
try:
|
||||
prompt = self._build_cost_estimate_prompt(analysis_result, detailed_cavity_json)
|
||||
response = await self._chat(
|
||||
_COST_ESTIMATE_SYSTEM,
|
||||
prompt,
|
||||
min(self._max_tokens, 1200),
|
||||
expect_json=True,
|
||||
)
|
||||
if not response:
|
||||
return None
|
||||
result = self._parse_json_response(response)
|
||||
if result:
|
||||
logger.info("LLM 成本估算生成成功: total=%s", result.get("total_mold_cost"))
|
||||
return result
|
||||
except Exception as e:
|
||||
logger.warning("LLM 成本估算失败（不影响主流程）: %s", e)
|
||||
return None
|
||||
|
||||
def _build_cost_estimate_prompt(self, analysis_result: Dict[str, Any], detailed_cavity_json: Optional[Dict[str, Any]]) -> str:
|
||||
geometry_data = analysis_result.get("geometry_data", {}) or (detailed_cavity_json or {}).get("geometry_data", {})
|
||||
volume = geometry_data.get("volume", 0) or 0
|
||||
bbox = geometry_data.get("bounding_box", {}) or {}
|
||||
dims = bbox.get("dimensions", [0, 0, 0])
|
||||
meta = (detailed_cavity_json or {}).get("metadata", {}) or {}
|
||||
material = meta.get("selected_material") or analysis_result.get("material") or "ABS"
|
||||
schemes = (detailed_cavity_json or {}).get("candidate_schemes", []) or []
|
||||
best = schemes[0] if schemes else {}
|
||||
cd = best.get("cavity_data", {}) if isinstance(best, dict) else {}
|
||||
mfg = cd.get("manufacturing_info", {}) if isinstance(cd, dict) else {}
|
||||
features = analysis_result.get("detected_features", []) or []
|
||||
complexity_hints = [
|
||||
f.get("description", f.get("feature_type", ""))
|
||||
for f in features
|
||||
if f.get("feature_type") in ("undercut", "side_action", "insert")
|
||||
]
|
||||
return (
|
||||
f"产品材料：{material}\n"
|
||||
f"体积：{float(volume):.1f} mm³\n"
|
||||
f"边界框尺寸（长×宽×高）：{float(dims[0]):.1f} × {float(dims[1]):.1f} × {float(dims[2]):.1f} mm\n"
|
||||
f"预估锁模力：{mfg.get('estimated_clamping_force', '未知')}\n"
|
||||
f"预估模具尺寸：{json.dumps(mfg.get('estimated_mold_size', {}), ensure_ascii=False)}\n"
|
||||
f"预估成型周期：{mfg.get('estimated_cycle_time', '未知')}\n"
|
||||
f"型腔数：{best.get('cavity_count', 1) if isinstance(best, dict) else 1}\n"
|
||||
f"模具结构：{best.get('mold_structure_type', '未知') if isinstance(best, dict) else '未知'}\n"
|
||||
f"复杂度线索：{', '.join(complexity_hints) if complexity_hints else '无明显倒扣/滑块'}\n"
|
||||
)
|
||||
|
||||
def _build_design_report_prompt(self, analysis_result, detailed_cavity_json) -> str:
|
||||
features = json.dumps(analysis_result.get("detected_features", []), ensure_ascii=False, indent=2)
|
||||
if len(features) > 4000:
|
||||
|
||||
@@ -125,6 +125,8 @@ class STPFile(Base):
|
||||
|
||||
id = Column(Integer, primary_key=True, index=True)
|
||||
user_id = Column(Integer, ForeignKey("users.id"), nullable=True, index=True)
|
||||
# 关联进销存成品（P2-1：分析结果可一键创建为成品并回写）
|
||||
product_id = Column(Integer, ForeignKey("products.id"), nullable=True, index=True)
|
||||
|
||||
# 对象存储信息
|
||||
object_key = Column(String(500), nullable=False, index=True) # MinIO对象键
|
||||
@@ -160,6 +162,7 @@ class STPFile(Base):
|
||||
|
||||
# 关联关系
|
||||
user = relationship("User", back_populates="stp_files")
|
||||
product = relationship("Product") # P2-1: 关联的进销存成品
|
||||
geometry_data = relationship("GeometryData", back_populates="stp_file", uselist=False)
|
||||
mesh_data = relationship("MeshData", back_populates="stp_file", uselist=False)
|
||||
mold_cavity_data = relationship("MoldCavityData", back_populates="stp_file", uselist=False)
|
||||
|
||||
Reference in New Issue
Block a user