arXiv：cs.AI· Akriti Bhat, Wayne Zhu, Ishan Gupta·· 16 小时前
CIR：用历史衰减与消费者评论信号实现食品安全检查的连续风险调度
Continuous Risk-Based Scheduling for Food Safety Inspection: Integrating Historical Decay with Event-Driven Consumer Review Signals
AI 导读
针对固定周期批量排检难以发现"刚通过检查却随后出现严重违规"的盲区，研究者提出事件驱动调度系统 CIR，将衰减加权历史评分与语言模型分类的实时消费者评论信号结合，并用时间分区规则排除早于最近一次检查的评论。
正文
Abstract:Batch risk-based inspection scheduling struggles to detect a critical failure class: food facilities with recent clean inspections that subsequently develop severe violations. Traditional systems recompute risk on fixed planning cycles using static historical data, leaving blind spots between inspection cycles. To address this, we introduce the Continuous Inspection Risk (CIR) system, an event-driven scheduling pipeline that re-ranks the inspection queue dynamically as new data arrives. CIR combines a decay-weighted historical score with a stream of real-time consumer reviews triaged by a language-model classifier. A key structural contribution is our temporal partition rule, which strictly excludes reviews predating the most recent inspection to prevent superseded signals from skewing current risk. Evaluating CIR on 9,505 routine inspections in Santa Clara County, California (2024-2026), we show that history-based scoring alone reduces mean time-to-detection of major violations by 69 days (20.9%) under a 30% inspection budget, but fails to anticipate severe vermin-related closures (0/8 detected). Incorporating post-inspection review signals drastically improves performance, predicting 7 of 8 vermin closures with an average lead time of 117 days-firing on the exact day of review publication in four instances. Introducing a review corroboration rule improves precision from 0.70 to 0.78 without loss of recall. Finally, we analyze critical implementation challenges, documenting a data-hygiene pitfall (stale review leakage across inspection boundaries) and an evaluation limitation involving unobserved facility self-remediation. This work provides the first case-level evaluation of event-driven inspection triggering using open data and provides a reproducible benchmark for review-augmented public health systems
| Subjects: | Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2610.15191 [cs.AI] |
| (or arXiv:2610.15191v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.15191 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ishan Gupta [view email]
[v1]
Sat, 10 Oct 2026 21:36:10 UTC (15 KB)
来源：arXiv：cs.AI · arxiv.org