This paper presents the design, implementation, and testing of an advanced conveyor belt surface monitoring system, specifically engineered for harsh and complex industrial environments. The system integrates multiple cutting-edge technologies, including programmable logic controllers (PLC), laser scanning, industrial-grade cameras, and deep learning algorithms, particularly YOLOv7, to achieve real-time, high-precision monitoring of conveyor belt conditions. Key innovations include optimized detection location based on failure modes, advanced PLC integration for seamless automation, and intelligent dust-proof features to maintain accuracy in challenging conditions. Through strategic placement of detection devices and multi-mode control strategies (local, remote, and automatic), the system offers unparalleled adaptability and responsiveness. The system leverages robust data management for trend analysis and predictive maintenance, enhancing operational efficiency. The hardware architecture comprises PLC-based control systems, high-resolution industrial cameras, and laser emitters, while the software features a two-tier structure combining human-machine interaction (HMI) with real-time data processing capabilities. Experimental results show that the system is highly effective in detecting common belt defects such as foreign objects, tears, and shallow scratches, ensuring optimal operational efficiency and minimizing downtime. The system’s scalability, robust data management, and adaptability to low-light and dusty conditions make it ideal for deployment in large-scale industrial operations, where continuous monitoring and early fault detection are critical to maintaining productivity and safety.

Cancer has become a major global health threat due to its high incidence and mortality. However, the development of anti-cancer drugs is limited by high costs, long cycles, and low success rates, slowing the progress of new treatments. As a method that simulates human cognitive functions, artificial intelligence (AI) has greatly improved the efficiency of drug development. Machine learning is a core part of AI and supports applications such as natural language processing and computer vision. This paper reviews recent advances in AI for optimizing anti-cancer drug discovery, development, and medication therapy management. First, we highlight the applications of AI in target identification, druggability assessment, drug screening, and repurposing. Second, we detail how AI optimizes drug combination therapy and clinical trial design. Finally, we describe the role of AI in treatment management, including nanoparticle delivery systems, personalized dosing, and adaptive therapy. AI greatly streamlines anti-cancer drug development and provides new directions for precision cancer therapy.