一种视频与物流业务数据融合的效率–安全双目标协同优化系统设计模式

A Design Pattern for Efficiency-Safety Dual-Objective Collaborative Optimization System Based on Fusion of Video and Logistics Business Data

摘要: 针对智慧物流中视频监控数据与业务数据融合存在的“数据孤岛”、多模态语义鸿沟及“效率–安全”双目标优化冲突问题，本文提出一种视频与物流业务数据融合的效率–安全双目标协同优化系统设计模式(ESCOS-DM)。该模式构建“感知层–边缘计算层–云端计算层–应用服务层–区块链审计层”的分层架构，通过数据交互总线实现跨层协同；设计跨模态适配层解决视频与业务数据的时空对齐及特征归一化问题，采用多模态注意力机制与知识蒸馏技术实现异构数据深度融合；引入改进的NSGA-III算法构建双目标优化引擎，动态平衡效率(任务延时最小化)与安全(风险识别率最大化)目标，并通过区块链审计层保障数据可信性。模拟测试结果表明，该系统在冷链运输场景中有效提升了物流运营效率与安全管理水平，验证了ESCOS-DM在解决“效率–安全”冲突及提升物流系统综合性能方面的有效性，为智慧物流多模态数据融合与双目标协同优化提供了新的技术设计模式。

Abstract: Aiming at the problems of “data silos”, multimodal semantic gaps, and conflicts in “efficiency-safety” dual-objective optimization in the integration of video surveillance data and business data in smart logistics, this paper proposes an efficiency-safety dual-objective collaborative optimization system design mode (ESCOS-DM) for the fusion of video and logistics business data. This mode constructs a hierarchical architecture consisting of “perception layer, edge computing layer, cloud computing layer, application service layer, and blockchain audit layer”, and realizes cross-layer collaboration through a data interaction bus. It designs a cross-modal adaptation layer to solve the problems of temporal-spatial alignment and feature normalization between video and business data, and adopts multimodal attention mechanism and knowledge distillation technology to achieve in-depth fusion of heterogeneous data. An improved NSGA-III algorithm is introduced to build a dual-objective optimization engine, which dynamically balances the efficiency objective (minimizing task delay) and the safety objective (maximizing risk identification rate), while ensuring data credibility through the blockchain audit layer. Simulation test results show that the system effectively improves logistics operation efficiency and safety management level in cold chain transportation scenarios, verifying the effectiveness of ESCOS-DM in solving “efficiency-safety” conflicts and enhancing the comprehensive performance of logistics systems. This provides a new technical design pattern for multimodal data fusion and dual-objective collaborative optimization in smart logistics.