Industry 4.0 makes it possible to develop smart emergency rescue systems in natural disasters. One of the most critical challenges is forecasting the demands of resources for appropriate resource allocations based on data from multiple sources with different levels of reliability. This paper deals with the challenge of data fusion and processing in forecasting resource demands for emergency responses to patients with various disease types. After an earthquake, the data on injuries, damages, and medical demands are characterized as diversified, unorganized, distributed, dynamic, and chaotic. Therefore, how to collect, filter, fuse, and mine data is most critical to forecast and allocate resources, especially for some emergent sources such as drugs for injuries and illnesses in post-earthquakes. To determine general patterns of outbreak diseases and corresponding medical needs, multi-source data is fused and processed to determine a reliable and accurate spectrum of post-earthquake diseases. The entropy-based weighting technology is adopted to determine the reliability and accuracy of data; the fused data is further processed to estimate the numbers of injuries, classify disease types, and finally predict the demands of medical supplies over time. In emergency rescues, medical resources are allocated and dispatched based on estimated numbers, types, and locations of patients. The effectiveness of the proposed method is verified and validated in simulation.

工业 4.0 使在自然灾害中开发智能应急救援系统成为可能。最关键的挑战之一是根据来自具有不同可靠性级别的多个来源的数据预测资源需求，以进行适当的资源分配。本文讨论了数据融合和处理在预测各种疾病类型患者应急响应资源需求方面的挑战。地震发生后，伤害、损失和医疗需求数据呈现多样化、无组织、分布式、动态和混乱的特点。因此，如何收集、过滤、融合和挖掘数​​据对于预测和分配资源最为关键，特别是对于一些紧急来源，例如震后伤病的药物。为了确定爆发疾病的一般模式和相应的医疗需求，融合和处理多源数据以确定可靠和准确的地震后疾病谱。采用基于熵的加权技术来确定数据的可靠性和准确性；融合数据经过进一步处理以估计受伤人数、对疾病类型进行分类，并最终预测医疗用品随时间的需求。在紧急救援中，根据估计的患者数量、类型和位置来分配和调度医疗资源。仿真验证了所提方法的有效性。采用基于熵的加权技术来确定数据的可靠性和准确性；融合数据经过进一步处理以估计受伤人数、对疾病类型进行分类，并最终预测医疗用品随时间的需求。在紧急救援中，根据估计的患者数量、类型和位置来分配和调度医疗资源。通过仿真验证了所提方法的有效性。采用基于熵的加权技术来确定数据的可靠性和准确性；融合数据经过进一步处理以估计受伤人数、对疾病类型进行分类，并最终预测医疗用品随时间的需求。在紧急救援中，根据估计的患者数量、类型和位置来分配和调度医疗资源。通过仿真验证了所提方法的有效性。