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 per-ception.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 architec-ture 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.Fi-nally,the delay and energy consumption requirements of tasks were modeled,and the optimization problem of minimiz-ing 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 ar-chitecture 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.
multi source heterogeneous datadata fusionsensorarithmetic optimization
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