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.

翻译：失踪儿童调查的前72小时是成功寻回的关键窗口。然而，执法机构常面临数据碎片化、非结构化以及缺乏动态地理空间预测工具的困境。我们的系统Guardian为失踪儿童调查及早期搜寻规划提供了一个端到端的决策支持系统。该系统将异构、非结构化的案件文档转换为模式对齐的时空表征，通过地理编码与交通情境增强案件信息，并提供覆盖0-72小时的概率化搜寻产品。本文概述了Guardian系统，并详细描述了其三层预测组件。第一层为马尔可夫链——一种稀疏且可解释的模型，其状态转移融合了道路可达性成本、隐蔽偏好及通道偏向，并采用昼夜分立的参数化设置。马尔可夫链输出的预测分布随后由第二层的强化学习模块转化为具有操作实用性的搜寻方案。最后，第三层的大型语言模型在方案发布前对第二层的搜寻计划进行事后验证。通过一项合成但贴近现实的案例研究，我们报告了24/48/72小时时间跨度的量化输出，并分析了系统的敏感性、失效模式与权衡关系。结果表明，所提出的三层架构预测系统能够为区域优化与人工审核生成可解释的先验信息。