Building a Semantic Model for Recycling Contamination Triage

说明与用途

ResidueDesk‑AI 使用语义模型评估回收设施的装载，既考虑数值指标，又考虑备注中的禁用词，并生成班次优先简报。

问题

Recycling facilities need to decide how to handle inbound loads when a numeric residue percent is low but a note contains a prohibited item, such as a battery pack, which should override the numeric assessment.

结果

On a sample of ten tickets, the brief reported 2 accepts, 4 resorts, 3 diverts, and 1 reject, with an average residue of 8.36%. The system correctly quarantined a load with a battery note despite a low residue percent.

工作流

• Validate ticket with Pydantic
• Project ticket to semantic layer (Load, Commodity, Supplier, measures)
• Scout for prohibited words in note
• Grade load against commodity metrics (ResiduePercent, MoisturePercent caps)
• Route load to appropriate line (accept, resort, divert, quarantine)
• Generate shift brief sorted by severity
• Expose API via FastAPI and CLI via Rich

证据

I built ResidueDesk-AI, a personal experiment, so a laptop at a fictional scale house would quarantine that load instead of waving it onto a tip floor because the residue percent was low.

来源

The PoC is a semantic model with a local coordinator on top. The model names three entities, Load, Commodity, and Supplier, and three measures on Load: NetWeightLb, ResiduePercent, and MoisturePercent.

来源

On the ten sample tickets the brief lands at 2 accept, 4 resort, 3 divert, 1 reject, average residue 8.36 percent.

来源