fix(bom-costing): default omitted model confidence conservatively

furniture-3d-system/src/server/services/bom-costing/bom-costing-ai-output-contract.ts

@@ -338,8 +338,21 @@ export const aiOptionalPositiveNumberSchema = z.preprocess(
   z.number().finite().positive().optional(),
 )
 
+const DEFAULT_AI_CONFIDENCE = 0.5
+
+function parseAiConfidence(value: unknown) {
+  // Confidence is advisory metadata. Some otherwise complete model responses
+  // omit it, so retain the usable BOM with an explicitly conservative value
+  // instead of discarding and re-running the entire batch.
+  if (value === null || value === undefined) return DEFAULT_AI_CONFIDENCE
+  if (typeof value === 'string' && !value.trim()) {
+    return DEFAULT_AI_CONFIDENCE
+  }
+  return parseAiFiniteNumber(value)
+}
+
 export const aiConfidenceSchema = z.preprocess(
-  parseAiFiniteNumber,
+  parseAiConfidence,
   z.number().finite().min(0).max(1),
 )
 

furniture-3d-system/src/server/services/bom-costing/bom-costing-multimodal-analyzer.test.ts

@@ -677,6 +677,34 @@ describe('BOM 多模态分析器', () => {
     })
   })
 
+  it('模型省略置信度时使用保守默认值，不重跑完整 BOM 批次', async () => {
+    const structureGroup = analyzedStructureGroup()
+    const modelLine = JSON.parse(
+      JSON.stringify(structureGroup.bomLines[0]),
+    ) as Record<string, unknown>
+    delete modelLine.confidence
+    const modelEvidence = modelLine.evidence as Array<Record<string, unknown>>
+    delete modelEvidence[0]?.confidence
+    mockFactExtractionThenAnalysis(snapshot, {
+      product: { category: '书桌' },
+      structureGroups: [{
+        ...structureGroup,
+        bomLines: [modelLine, ...structureGroup.bomLines.slice(1)],
+      }],
+      skus: [analyzedSku('sku-white'), analyzedSku('sku-black')],
+    })
+
+    const result = await new BomCostingMultimodalAnalyzer().analyze({
+      sourceUrl: snapshot.detailUrl,
+      snapshot,
+    })
+
+    expect(result.skus[0]?.bomLines[0]).toMatchObject({
+      confidence: 0.5,
+      evidence: [{ confidence: 0.5 }],
+    })
+  })
+
   it('模型返回其他不完整 SKU 信息时不暴露内部校验细节', async () => {
     mockFactExtractionThenAnalysis(snapshot, {
         product: { category: '书桌' },