Research on application of mechanical design engineering database based on intelligent optimization algorithm

Aiming at the efficiency bottleneck and optimization rigidities of traditional databases when dealing with high-dimensional, strongly coupled and dynamically evolving data, an optimization framework of dynamic databases based on intelligent optimization algorithm is proposed. The framework integrates topological feature extraction and graph neural network (GNN) to realize intelligent coding of data features, adopts GA-DRL joint optimization strategy of genetic algorithm (GA) and deep reinforcement learning (DRL) to improve complex query efficiency, and realizes real-time data interaction between CAD/CAE system and database through incremental synchronization mechanism based on timestamp. The experimental results show that the query response time of the proposed OptiMDB system is significantly better than that of traditional systems such as MySQL and Secondary in typical scenarios such as aero-engine material screening and automobile lightweight design, and the design efficiency is improved by 78.5%, and the scheme compliance rate is improved to 97%, which verifies the effectiveness and practicability of this method in engineering practice.