Research on heterogeneous data fusion and arithmetic optimization in multi-sensor systems

Multi-sensor systems integrate diverse sensor data to achieve comprehensive and accurate environmental perception. However, how to effectively fuse heterogeneous data and realize the efficiency of real-time processing is still a hot and difficult issue in current research. Therefore, focusing on data fusion and arithmetic optimization of multi-source heterogeneous sensors, an innovative solution was proposed. Firstly, a data fusion system based on master-slave architecture was designed to solve the problem of multi-source heterogeneous data processing. Secondly, a three-layer "cloud-edge-end" architecture was implemented, leveraging edge servers to offload computational pressure from cloud servers, optimizing task scheduling strategies, and enabling coordinated management of network and computing resources. Finally, the delay and energy consumption requirements of tasks were modeled, and the optimization problem of minimizing system cost was constructed under resource constraints, which was transformed into Markov decision process (MDP) and solved with deep deterministic policy gradient (DDPG) algorithm. Simulation experiments show that the proposed architecture and scheduling algorithm exhibit excellent performance in reducing both latency and energy consumption, and provide a new idea for efficient data fusion and arithmetic optimization in multi-sensor systems.