The smart logistics industry utilizes advanced software and hardware technologies to enhance efficient transmission. By integrating smart components, it identifies vulnerabilities within the logistics sector, making it more susceptible to physical attacks aimed at theft and control. The main goal is to propose an effective logistics monitoring system that automates theft prevention. Specifically, the suggested model analyzes logistics transmission patterns through secure surveillance enabled by IoT-based blockchain technology. Additionally, a bi-directional convolutional neural network is employed to evaluate real-time theft vulnerabilities, aiding optimal decision-making. The proposed method has been shown to provide accurate real-time analysis of risky behaviors. Experimental simulations indicate that the proposed solution significantly improves logistics monitoring. The system’s performance is assessed using various statistical metrics, including latency rate (7.44 s), a data processing cost (O((n−1)logn)), and model training and testing results (precision (94.60%), recall (95.67%), and F-Measure (96.64%)), statistical performance (error reduction (48%)) and reliability (94.48%).

中文翻译：

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受区块链启发的物流盗窃控制智能框架


智能物流行业利用先进的软件和硬件技术来提高传输效率。通过集成智能组件，它可以识别物流行业的漏洞，使其更容易受到旨在盗窃和控制的物理攻击。主要目标是提出一个有效的物流监控系统，以实现防盗自动化。具体来说，建议的模型通过基于 IoT 的区块链技术实现的安全监控来分析物流传输模式。此外，采用双向卷积神经网络来评估实时盗窃漏洞，有助于最佳决策。所提出的方法已被证明可以提供对风险行为的准确实时分析。实验仿真表明，所提出的解决方案显著改善了物流监控。使用各种统计指标评估系统的性能，包括延迟率 （7.44 s）、数据处理成本 （O（（n−1）logn）） 和模型训练和测试结果 （精度 （94.60%）、召回率 （95.67%） 和 F 度量 （96.64%））、统计性能 （减少错误 （48%） 和可靠性 （94.48%）。