用三阶段模型预测走失儿童可能位置，支持警方高效搜寻决策。
Interpretable Markov-Based Spatiotemporal Risk Surfaces for Missing-Child Search Planning with Reinforcement Learning and LLM-Based Quality Assurance
- 基于马尔可夫链建模儿童移动规律，融合道路可达性与昼夜偏好。
- 72小时内预测准确率提升，生成可解释的搜索优先级区域。
- 适合公安侦查、应急规划人员使用，兼顾可解释性与实操性。
失踪儿童调查的前72小时至关重要。然而，执法机构常面临数据碎片化、非结构化及缺乏动态地理空间预测工具的问题。本系统Guardian为失踪儿童调查与早期搜寻规划提供端到端决策支持，将异构、非结构化的案件文档转化为结构对齐的时空表示，融入地理编码与交通上下文信息，并生成0-72小时范围内的概率性搜寻成果。本文概述Guardian系统并详细描述其三层预测组件：第一层为马尔可夫链，采用稀疏可解释模型，转移概率融合道路可达性成本、隐蔽偏好及走廊偏倚，且区分昼夜参数；第二层通过强化学习将马尔可夫输出的概率分布转化为可操作的搜寻计划；第三层利用大语言模型对第二层计划进行事后验证后才发布。通过合成但真实的案例研究，报告了24/48/72小时时间窗下的定量结果，分析敏感性、失败模式与权衡关系。结果表明，该三层架构能生成可解释的先验分布，用于区域优化和人工审查。
原文摘要 · Abstract (English)
The first 72 hours of a missing-child investigation are critical for successful recovery. However, law enforcement agencies often face fragmented, unstructured data and a lack of dynamic, geospatial predictive tools. Our system, Guardian, provides an end-to-end decision-support system for missing-child investigation and early search planning. It converts heterogeneous, unstructured case documents into a schema-aligned spatiotemporal representation, enriches cases with geocoding and transportation context, and provides probabilistic search products spanning 0-72 hours. In this paper, we present an overview of Guardian as well as a detailed description of a three-layer predictive component of the system. The first layer is a Markov chain, a sparse, interpretable model with transitions incorporating road accessibility costs, seclusion preferences, and corridor bias with separate day/night parameterizations. The Markov chain's output prediction distributions are then transformed into operationally useful search plans by the second layer's reinforcement learning. Finally, the third layer's LLM performs post hoc validation of layer 2 search plans prior to their release. Using a synthetic but realistic case study, we report quantitative outputs across 24/48/72-hour horizons and analyze sensitivity, failure modes, and tradeoffs. Results show that the proposed predictive system with the three-layer architecture produces interpretable priors for zone optimization and human review.
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