Accurate prediction of product lifecycle stages is crucial for enhancing inventory turnover and strategic planning in the tobacco industry. This paper proposes an intelligent prediction model that integrates Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU), further optimized by an Improved Grey Wolf Optimizer (IGWO). The model fuses multi-source enterprise data—including sales trends, IoT logistics information, environmental conditions, and inventory records—to dynamically forecast lifecycle stages and remaining durations. The dataset comprises 180,000 labeled samples collected from real-world tobacco enterprise operations, encompassing multi-source variables such as sales volume, inventory changes, logistics routes, and environmental feedback. Experimental evaluations based on this dataset demonstrate that the proposed IGWO-CNN-GRU model achieves a Mean Squared Error (MSE) of 2.13, a Mean Absolute Error (MAE) of 1.17, and an R² of 0.932, significantly outperforming baseline models. In practical deployment simulations, the prediction deviation is limited to ±5 days, improving allocation efficiency and reducing inventory risks. The approach provides a robust and adaptable solution for full-lifecycle management in tobacco supply chains, offering practical value for intelligent production and market deployment strategies.
This research proposes an efficient parallel graph partitioning algorithm for the big data environment, aiming to solve the bottlenecks of traditional clustering techniques in terms of processing speed and scalability. The algorithm adopts a multi-level graph partitioning framework, decomposing the network information processing task into multiple levels, gradually simplifying the graph structure and backtracking refinement, thereby significantly reducing the computational complexity while ensuring the partitioning quality. The algorithm focuses on balancing the node cohesion within partitions and the edge cutting cost of inter-partition communication. By constructing a global objective function, it minimizes the number of edges across partitions and the workload differences among various sub-graphs, thereby achieving a more balanced partitioning result. The research results show that this algorithm achieves a resource utilization rate of 0.95. In the Hadoop cluster environment, 95% of the computing resources are effectively used for actual task processing, which is significantly higher than that of the competing algorithms. The energy efficiency ratio reaches 0.98, indicating that the number of tasks completed per unit of energy consumption is close to the optimal level, which is superior to the 0.78 to 0.67 range of existing methods, reflecting the advantages of this algorithm in green computing. The load imbalance rate is only 0.00395, and the point weight imbalance rate is 0.00141, which are much lower values than those of the comparison algorithm. This indicates that the algorithm achieves a high degree of balance in task allocation and node weight distribution, effectively avoiding resource waste and performance bottlenecks.
To solve the problems that the weights and thresholds of discrete Hopfield neural networks are easy to fall into local optima and have insufficient anti-noise ability in digit recognition, a digit recognition method based on discrete Hopfield neural network is proposed, which is optimized by fish swarm algorithm and called AFSA-HOP integration method. The parameters of the discrete Hopfield neural network are optimized by using AFSA's powerful global search ability, and the recognition accuracy of the Hopfield neural network is taken as the fitness function. This allows the Hopfield neural network to maintain a high associative success rate even under high noise-to-signal ratios. Computer simulation experiments show that while the recognition performance of the traditional Hopfield neural network significantly deteriorates when the noise intensity is 0.2, the AFSA-HOP method maintains a high recognition accuracy even at noise intensities of 0.4 and 0.5, demonstrating superior digital recognition performance. This method provides a robust new approach for digital recognition and could be further extended in future applications by integrating other optimization algorithms.
This paper presents a novel real-time fire detection framework tailored for IoT devices by integrating the fine-tuned YOLOv10 model with the Accelerator module. Trained on the FireSmokeDataset (Roboflow) and an additional dataset we collected via Roboflow, the system covers fire, smoke, and distracting objects. Optimized for resource-constrained edge devices, the framework demonstrates exceptional performance, achieving high mean average precision (mAP) for fire and smoke detection, with metrics exceeding 84% and a maximum mAP50 of over 91%. We target deployments in residential homes, industrial facilities, and forest monitoring stations. A key contribution of the proposed framework is the construction of a diverse dataset encompassing fire, smoke, and distracting objects - an element often overlooked in existing fire detection datasets. Additionally, fine-tuning the YOLOv10 model components in conjunction with hardware acceleration ensures both prediction accuracy and improved inference response performance. Comprehensive evaluations confirm the system's robustness, scalability, and practicality under various operating conditions. Through experimental analysis, the YOLOv10-S (small) model stands out for its balance between efficiency and resource usage, making it a suitable choice for low-cost real-time applications with resource constraints. By utilizing the Coral Accelerator, the proposed framework reduces inference time by 58% compared to CPU-based implementations, achieving a latency of just 1.7 seconds per frame. The system's lightweight design ensures reliable deployment in remote areas with limited computational resources and unstable network connectivity, maintaining high accuracy while minimizing false alarms.
The September 2025 (Vol. 33, No. 3) issue of CIT. Journal of Computing and Information Technology brings four papers from the areas of graph data processing, computer vision, and business intelligence.
