As reliable distributed systems, Blockchains have been widely applied in diverse domains, such as the Internet of Things (IoT). Recent studies have explored Deep Reinforcement Learning (DRL) to enhance blockchain performance. However, existing DRL-based blockchain performance optimization methods rely on implicit and idealized assumptions about node behaviors and transactional workloads, limiting their effectiveness and efficiency on the blockchain with dynamic work-loads and heterogeneous nodes. To alleviate this, we propose CHAINOPT, a novel blockchain performance optimization framework devised for optimal parameter configuration to handle dynamic workloads and heterogeneous nodes. Specifically, we first propose an interaction-aware state representation learning module to model both global system-level and local heterogeneous node feature interactions to generate better state representations. Then, a contrastive learning-enhanced workload identification module is designed to extract discriminative workload-specific state representations to improve workload identification accuracy. Finally, we design a workload-similarity guided policy reuse module to produce effective reuse weights to transfer knowledge from history policies based on workload relevance, thereby improving optimization speed and stability. Extensive experiments show the effectiveness of CHAINOPT in improving blockchain performance, achieving 185.87% higher scalability and 1492.16% stronger security with only a marginal 6.26% latency increase. Moreover, it outperforms baselines in static scenarios while maintaining considerable superiority under varying workloads.

This work aims to analyze the specific application of sensor environment perception based on the Kalman filter algorithm in intelligent vehicles. Hence, this work proposes a design for a multi-sensor environment perception and adaptive cruise control (ACC) system based on the Kalman filter algorithm. The system utilizes multiple sensors to collect data and employs the Kalman filter algorithm to process the data, enabling obstacle detection and tracking. This provides a new solution for environmental perception in intelligent vehicles. Meanwhile, combined with ACC technology, the vehicle speed is adjusted to achieve a safe and efficient autonomous driving experience. The experimental results indicate that the system using the Kalman filter algorithm performs in various scenarios, including different weather conditions, road conditions, and obstacle detection. This work achieves high detection accuracy and tracking precision, with the highest values reaching 97.5% and 96.3%, respectively. In the tests, the ACC system can maintain an appropriate following distance and control the vehicle speed well, whether it is a car, a large truck, or a motorcycle. This work has crucial reference value and promotion significance for developing intelligent vehicle technology.